| Title: | An R Package for Comprehensive Analysis of Metabolomics Data |
|---|---|
| Description: | This package contains the R functions and libraries underlying the popular MetaboAnalyst web server, including 500 functions for data processing, normalization, statistical analysis, metabolite set enrichment analysis, metabolic pathway analysis, and biomarker analysis. The package is synchronized with the web server. After installing and loading the package, users will be able to reproduce the same results from their local computers using the corresponding R command history downloaded from MetaboAnalyst, to achieve maximum flexibility and reproducibility. |
| Authors: | Jianguo Xia [aut, cre], Jasmine Chong [aut], Zhiqiang Pang [aut] |
| Maintainer: | Zhiqiang Pang <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 4.0.0 |
| Built: | 2025-04-01 04:27:04 UTC |
| Source: | https://github.com/xia-lab/MetaboAnalystR |
Calculate ROC performance with CV
.do.CVTest.LRmodel(data.in, fmla.in, kfold = 10, run.stepwise = FALSE).do.CVTest.LRmodel(data.in, fmla.in, kfold = 10, run.stepwise = FALSE)
data.in |
Input matrix of data |
fmla.in |
Input for generalized linear model |
kfold |
Numeric |
run.stepwise |
Logistic Regression |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Function downloads the required file and reads it only if not already in working directory. Need to specify the file URL and the destfile.
.get.my.lib(filenm, sub.dir = NULL).get.my.lib(filenm, sub.dir = NULL)
filenm |
Input the name of the file to download |
sub.dir |
sub.dir |
Function to read in a data table. First, it will try to use fread, however, it has issues with some windows 10 files. In such case, use the slower read.table method.
.readDataTable(fileName, save.copy = TRUE).readDataTable(fileName, save.copy = TRUE)
fileName |
Input filename |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
The error message will be printed in all cases. Used in higher functions.
AddErrMsg(msg)AddErrMsg(msg)
msg |
Error message to print |
Lipid analysis pipeliner
analyze.lipids(inFile, iso = "y")analyze.lipids(inFile, iso = "y")
inFile |
Input the file to read in |
iso |
Default is set to "y" |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
ANOVA analysis
ANOVA.Anal(mSetObj=NA, nonpar=FALSE, thresh=0.05, all_results=FALSE)ANOVA.Anal(mSetObj=NA, nonpar=FALSE, thresh=0.05, all_results=FALSE)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
nonpar |
Logical, use a non-parametric test (T) or not (F) |
thresh |
Numeric, from 0 to 1, indicate the p-value threshold |
all_results |
Logical, if TRUE, it will output the ANOVA results for all compounds |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform Two-way ANOVA
ANOVA2.Anal(mSetObj=NA, thresh=0.05, p.cor="fdr", type="time0")ANOVA2.Anal(mSetObj=NA, thresh=0.05, p.cor="fdr", type="time0")
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
thresh |
Input the p-value threshold |
p.cor |
Select method for p-value correction, bonferroni, holm or fdr |
type |
Select b to perform between-subjects ANOVA, and w for within-subjects ANOVA |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform anova and only return p values and MSres (for Fisher's LSD)
aof(x, cls)aof(x, cls)
x |
Input the data to perform ANOVA |
cls |
Input class labels |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform Two-way ANOVA Perform repeated measure one-way anova
aov.1wayrep(x)aov.1wayrep(x)
x |
Input the data |
time.fac |
Input the time factor |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform Two-way ANOVA Perform two-way anova
aov.2way(x)aov.2way(x)
x |
Input data to perform 2-way ANOVA |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform Two-way ANOVA Perform within-subjects anova
aov.mixed(x)aov.mixed(x)
x |
Input the data |
time.fac |
Input the time factor |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform ASCA
ASCAfun.res(X, Fac)ASCAfun.res(X, Fac)
X |
Input list of compounds |
Fac |
Numeric McGill University, Canada License: GNU GPL (>= 2) |
Jeff Xia [email protected]
Perform ASCA
ASCAfun1(X, Design, Fac)ASCAfun1(X, Design, Fac)
X |
Numeric, number of compounds |
Design |
Number of levels in the factor |
Fac |
Numeric, the factor |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform ASCA
ASCAfun2(X, Desa, Desb, Fac)ASCAfun2(X, Desa, Desb, Fac)
X |
Numeric, number of compounds |
Desa |
Number of levels in the factor TIME |
Desb |
Number of levels in the other factor |
Fac |
Numeric, the factor |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Assuming independent random distribution of FA, the most probable frequency will be the product of the each component. Note: the data is concentration, we need to get frequncies - percentage w.r.t the total nmol. the result is the saved as separate files for each lipid class data for each FA class, first col is sample name
calculateConcISO(dat, cls.name, cls.num, min.file, prob.file)calculateConcISO(dat, cls.name, cls.num, min.file, prob.file)
dat |
Input the data |
cls.name |
Input the class names |
cls.num |
Input the number of classes |
min.file |
Input the min file |
prob.file |
Input the prob file |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform calculation of feature importance (AUC, p value, fold change)
CalculateFeatureRanking(mSetObj=NA, clust.num=5)CalculateFeatureRanking(mSetObj=NA, clust.num=5)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
clust.num |
Numeric, input the number of clusters for cluster-analysis |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Various enrichment analysis algorithms
CalculateGlobalTestScore(mSetObj = NA)CalculateGlobalTestScore(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Over-representation analysis using hypergeometric tests The probability is calculated from obtaining equal or higher number of hits using 1-phyper. Since phyper is a cumulative probability, to get P(X>=hit.num) => P(X>(hit.num-1))
CalculateHyperScore(mSetObj = NA)CalculateHyperScore(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
This function calculates the all important features based on a specfic cutoff.
CalculateImpVarCutoff(mSetObj, spe.thresh, lev.thresh)CalculateImpVarCutoff(mSetObj, spe.thresh, lev.thresh)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
spe.thresh |
alpha threshold, less is better, default less than 5 percentile based chi-square note: spe and leverage are vectors, not a single value, but a list to store the result note: the last model is Model.res, no spe Calculate leverage cutoff based on permutation Calculate the reference distribution of leverages note: leverage.perm is a list with each member in a 3 column matrix |
lev.thresh |
leverage threshold, the higher better, default more than 95 percentile of permuted leverage |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Calculate the over representation analysis score
CalculateOraScore(mSetObj=NA, nodeImp, method)CalculateOraScore(mSetObj=NA, nodeImp, method)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
nodeImp |
Indicate the pathway topology analysis, "rbc" for relative-betweeness centrality, and "dgr" for out-degree centrality. |
method |
is "fisher" or "hyperg" |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Mat are log normalized, diff will be ratio. Used in higher functions.
CalculatePairwiseDiff(mat)CalculatePairwiseDiff(mat)
mat |
Input matrix of data to calculate pair-wise differences. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Calculate quantitative enrichment score
CalculateQeaScore(mSetObj=NA, nodeImp, method)CalculateQeaScore(mSetObj=NA, nodeImp, method)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
nodeImp |
Indicate the pathway topology analysis, "rbc" for relative-betweeness centrality, and "dgr" for out-degree centrality. |
method |
Indicate the pathway enrichment analysis, global test is "gt" and global ancova is "ga". |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
reference concentrations stored in the library
CalculateSSP(mSetObj = NA)CalculateSSP(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
CentroidCheck function used to check centroid or not
CentroidCheck(filename)CentroidCheck(filename)
filename |
filename to check |
Zhiqiang Pang
This function determines if all annotated data are ready for meta-analysis
CheckMetaDataConsistency(mSetObj = NA, combat = TRUE)CheckMetaDataConsistency(mSetObj = NA, combat = TRUE)
mSetObj |
Input name of the created mSet Object |
combat |
Adjust for batch effects, logical variable: TRUE = adjust for batch effects using an empirical Bayes framework (R package sva), FALSE = no batch effect adjustment. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Cleans data and removes -Inf, Inf, NA, negative and 0s.
CleanData(bdata, removeNA = T, removeNeg = T, removeConst = T)CleanData(bdata, removeNA = T, removeNeg = T, removeConst = T)
bdata |
Input data to clean |
removeNA |
Logical, T to remove NAs, F to not. |
removeNeg |
Logical, T to remove negative numbers, F to not. |
removeConst |
Logical, T to remove samples/features with 0s, F to not. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Function used in higher functinos to clean data matrix
CleanDataMatrix(ndata)CleanDataMatrix(ndata)
ndata |
Input the data to be cleaned |
Replace -Inf, Inf to 99999 and -99999
CleanNumber(bdata)CleanNumber(bdata)
bdata |
Input matrix to clean numbers |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Remove from, within, leading and trailing spaces
ClearStrings(query)ClearStrings(query)
query |
Input the query to clear |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Compute the average ROC curve
ComputeAverageCurve(perf, avg.method)ComputeAverageCurve(perf, avg.method)
perf |
Input the average |
avg.method |
Input the name of the method to compute the average curve |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
The upper limit for each combination is considered to be the minimal of the fatty acid concentration (nmol fatty acid/gram of sample) X is the lopomics data obtained above the result is the saved as separate files for each lipid class
computeConc(X, iso = "y")computeConc(X, iso = "y")
X |
Input the data |
iso |
Default is set to "y" |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Computes the 95 percent interval only for the y-axis. Utility function, called upon by higher functions
ComputeHighLow(perf)ComputeHighLow(perf)
perf |
Input the performance |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
ContainMissing is used to check if any missing data exists in the uploaded file.
ContainMissing(mSetObj=NA)ContainMissing(mSetObj=NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Convert2AnalObject This function is used to convert mSet object from raw spectra processing for the following analysis.
Convert2AnalObject(mSet, data.type, anal.type, paired = FALSE)Convert2AnalObject(mSet, data.type, anal.type, paired = FALSE)
mSet |
mSet from raw spectral processing pipeline, OptiLCMS |
data.type |
data type, should be 'spec' |
anal.type |
analysis type, should be 'raw' |
paired |
data is paired or not, use FALSE by default |
Following t-test analysis or effect size calculation, this functions converts the results from the mSetObj to the proper format for mummichog analysis.
Convert2Mummichog( mSetObj = NA, rt = FALSE, rds.file = FALSE, rt.type = "seconds", test = "tt", mode = NA )Convert2Mummichog( mSetObj = NA, rt = FALSE, rds.file = FALSE, rt.type = "seconds", test = "tt", mode = NA )
mSetObj |
Input the name of the created mSetObj. |
rt |
Logical, whether or not to include retention time information. |
rds.file |
Logical, if true, the "annotated_peaklist.rds" must be in the current working directory to get corresponding retention time information for the features. If not, the retention time information will be taken from the feature names. Feature names must be formatted so that the mz and retention time for a single peak is separated by two underscores. For instance, m/z of 410.2148 and retention time of 42.46914 seconds must be formatted as 410.2148__42.46914. |
rt.type |
Character, input whether retention time is in seconds (default as RT using MetaboAnalystR is seconds) or minutes (as from MZmine). |
test |
Character, input what statistical values to include in the mummichog input. For p-values and t-scores only from t-test, use "tt". For log2FC from the fold-change analsis, use "fc". For effect-sizes, use "es". For, p-values, fold-changes and effect sizes, use "all". For multiple groups, use 'aov'. |
mode |
ion mode, positive or negative |
Jasmine Chong, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Following t-test analysis or effect size calculation, this functions converts the results from the mSetObj to the proper format for mummichog analysis.
Convert2MummichogMetaPath( mSetObj = NA, rt = FALSE, rds.file = FALSE, rt.type = "seconds", test = "tt", mode = NA )Convert2MummichogMetaPath( mSetObj = NA, rt = FALSE, rds.file = FALSE, rt.type = "seconds", test = "tt", mode = NA )
mSetObj |
Input the name of the created mSetObj. |
rt |
Logical, whether or not to include retention time information. |
rds.file |
Logical, if true, the "annotated_peaklist.rds" must be in the current working directory to get corresponding retention time information for the features. If not, the retention time information will be taken from the feature names. Feature names must be formatted so that the mz and retention time for a single peak is separated by two underscores. For instance, m/z of 410.2148 and retention time of 42.46914 seconds must be formatted as 410.2148__42.46914. |
rt.type |
Character, input whether retention time is in seconds (default as RT using MetaboAnalystR is seconds) or minutes (as from MZmine). |
test |
Character, input what statistical values to include in the mummichog input. For p-values and t-scores only from t-test, use "tt". For log2FC from the fold-change analsis, use "fc". For effect-sizes, use "es". For, p-values, fold-changes and effect sizes, use "all". |
mode |
ion mode, positive or negative |
Jasmine Chong, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
CovariateScatter.Anal
CovariateScatter.Anal( mSetObj, imgName = "NA", format = "png", analysis.var, ref = NULL, block = "NA", thresh = 0.05, pval.type = "fdr", contrast.cls = "anova" )CovariateScatter.Anal( mSetObj, imgName = "NA", format = "png", analysis.var, ref = NULL, block = "NA", thresh = 0.05, pval.type = "fdr", contrast.cls = "anova" )
mSetObj |
mSetObj object |
imgName |
image name |
format |
image format |
analysis.var |
variable of analysis |
ref |
reference group |
block |
block name |
thresh |
threshold |
pval.type |
pvalue type (raw or fdr) |
contrast.cls |
contrast group |
Creates a message stating that no analyses were performed on your data.
CreateAnalNullMsg()CreateAnalNullMsg()
Report generation using Sweave Create ANOVA document
CreateANOVAdoc(mSetObj = NA)CreateANOVAdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave ANOVA
CreateAOV2doc(mSetObj = NA)CreateAOV2doc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Random Forest ASCA
CreateASCAdoc(mSetObj = NA)CreateASCAdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Power analysis report, data input documentation.
CreateBiomarkerInputDoc(mSetObj = NA)CreateBiomarkerInputDoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Biomarker analysis report introduction
CreateBiomarkerIntr()CreateBiomarkerIntr()
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Power analysis report overview
CreateBiomarkerOverview()CreateBiomarkerOverview()
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Biomarker analysis, ratio option
CreateBiomarkerRatioOverview(mSetObj = NA)CreateBiomarkerRatioOverview(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Puts together the analysis report
CreateBiomarkerRnwReport(mSetObj, usrName)CreateBiomarkerRnwReport(mSetObj, usrName)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
usrName |
Input the name of the user |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Correlation and Partial Correlation Analysis
CreateCorAnalysis(mSetObj = NA)CreateCorAnalysis(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jessica Ewald [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave For Correlation Heatmap
CreateCorHeatmap(mSetObj = NA)CreateCorHeatmap(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jessica Ewald [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Create correlation document
CreateCorrDoc(mSetObj = NA)CreateCorrDoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Covariate Adjustment
CreateCovAdj(mSetObj = NA)CreateCovAdj(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jessica Ewald [email protected] McGill University, Canada License: GNU GPL (>= 2)
Separate data set with k-fold CV, used in higher function
createCVset(groupN, kfold, rseed)createCVset(groupN, kfold, rseed)
groupN |
Input the size of the group |
kfold |
Input the number of cross-validations |
rseed |
Input the random seed |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Create EBAM document Note: the search for delta (SAM) and a0 (EBAM) will not be plotted it is only exploration, and may cause potential inconsistentcies.
CreateEBAMdoc(mSetObj = NA)CreateEBAMdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Metabolite enrichment analysis report, analysis
CreateEnrichAnalDoc()CreateEnrichAnalDoc()
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Metabolite enrichment analysis report data input
CreateEnrichInputDoc(mSetObj = NA)CreateEnrichInputDoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Metabolite enrichment analysis report introduction
CreateEnrichIntr()CreateEnrichIntr()
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Metabolite enrichment analysis report, over representation analysis (ORA)
CreateEnrichORAdoc(mSetObj = NA)CreateEnrichORAdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Metabolite enrichment analysis report overview
CreateEnrichOverview()CreateEnrichOverview()
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Metabolite enrichment analysis report enrichment process
CreateEnrichProcessDoc(mSetObj = NA)CreateEnrichProcessDoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Metabolite enrichment analysis report Quantitative enrichment analysis
CreateEnrichQEAdoc(mSetObj = NA)CreateEnrichQEAdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Metabolite enrichment analysis report
CreateEnrichRnwReport(mSetObj, usrName)CreateEnrichRnwReport(mSetObj, usrName)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
usrName |
Input the name of the user |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Metabolite enrichment analysis report Single sampling profiling
CreateEnrichSSPdoc(mSetObj = NA)CreateEnrichSSPdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Function for the network explorer module, prepares user's data for network exploration.
CreateGraph(mSetObj = NA)CreateGraph(mSetObj = NA)
mSetObj |
Input name of the created mSet Object |
Report generation using Sweave Function to create a summary table of mummichog analysis
Report generation using Sweave Function to create a summary table of mummichog analysis
CreateGSEAAnalTable(mSetObj = NA) CreateGSEAAnalTable(mSetObj = NA)CreateGSEAAnalTable(mSetObj = NA) CreateGSEAAnalTable(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Create hierarchical clustering document
CreateHCdoc(mSetObj = NA)CreateHCdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
CreateIntegMatchingTable
CreateIntegMatchingTable(mSetObj = NA)CreateIntegMatchingTable(mSetObj = NA)
mSetObj |
mSetObj Object |
Report generation using Sweave Puts together the analysis report
CreateIntegPathwayAnalysisRnwReport(mSetObj, usrName)CreateIntegPathwayAnalysisRnwReport(mSetObj, usrName)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
usrName |
Input the name of the user |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave integrated pathway report, data input documentation.
CreateIntegratedPathwayAnalInputDoc(mSetObj = NA)CreateIntegratedPathwayAnalInputDoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, viewingCanada License: GNU GPL (>= 2)
Report generation using Sweave Integrated pathwayr analysis report introduction
CreateIntegratedPathwayAnalIntr()CreateIntegratedPathwayAnalIntr()
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Biomarker analysis report, ROC Curve Based Model Creation and Evaluation
CreateIntegratedPathwayDoc(mSetObj = NA)CreateIntegratedPathwayDoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Function to create a table for gene name mapping
CreateIntegratedPathwayGeneMapTable(mSetObj = NA)CreateIntegratedPathwayGeneMapTable(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Function to create a table for compound name mapping
CreateIntegratedPathwayNameMapTable(mSetObj = NA)CreateIntegratedPathwayNameMapTable(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Function to create a table for pathway results
CreateIntegratedPathwayResultsTable(mSetObj = NA)CreateIntegratedPathwayResultsTable(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave For Interactive PCA
CreateiPCAdoc(mSetObj = NA)CreateiPCAdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Create Kmeans partitional clustering document
CreateKMdoc(mSetObj = NA)CreateKMdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
use leave-one-out / Nfold or bootstrape to permute data for external CV build SVM model and use mean-balanced weight to sort genes on training set and recursive elimination of least important genes
CreateLadder(Ntotal, Nmin = 5)CreateLadder(Ntotal, Nmin = 5)
Ntotal |
Total number |
Nmin |
Minimum number, default set to 5 |
Dr. Xin Lu, Research Scientist Biostatistics Department, Harvard School of Public Health create a decreasing ladder for recursive feature elimination
Creates the mapping result table
CreateMappingResultTable(mSetObj = NA)CreateMappingResultTable(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Report generation using Sweave Multivariate Bayes
CreateMBdoc(mSetObj = NA)CreateMBdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Function to create a summary table of mummichog analysis
Report generation using Sweave Function to create a summary table of mummichog analysis
CreateMetaAnalTable(mSetObj = NA) CreateMetaAnalTable(mSetObj = NA)CreateMetaAnalTable(mSetObj = NA) CreateMetaAnalTable(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Meta-Analysis, data normalization documentation.
CreateMetaAnalysisDEdoc(mSetObj = NA)CreateMetaAnalysisDEdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Power analysis report, data input documentation.
CreateMetaAnalysisInputDoc(mSetObj = NA)CreateMetaAnalysisInputDoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave MetaAnalysis analysis report introduction
CreateMetaAnalysisIntr()CreateMetaAnalysisIntr()
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Meta-Analysis, data normalization documentation.
CreateMetaAnalysisNORMdoc(mSetObj = NA)CreateMetaAnalysisNORMdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave MetaAnalysis analysis, data normalization documentation.
CreateMetaAnalysisOutput(mSetObj = NA)CreateMetaAnalysisOutput(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Power analysis report overview
CreateMetaAnalysisOverview()CreateMetaAnalysisOverview()
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Puts together the analysis report
CreateMetaAnalysisRnwReport(mSetObj, usrName)CreateMetaAnalysisRnwReport(mSetObj, usrName)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
usrName |
Input the name of the user |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Mummichog analysis report, data input documentation.
CreateMetaMummichogInputDoc(mSetObj = NA)CreateMetaMummichogInputDoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Mummichog analysis report introduction
CreateMetaMummichogIntro()CreateMetaMummichogIntro()
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Mummichog analysis report overview
CreateMetaMummichogResults(mSetObj)CreateMetaMummichogResults(mSetObj)
mSetObj |
mSetObj |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave For Metadata Overview
CreateMetaOverview(mSetObj = NA)CreateMetaOverview(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jessica Ewald [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Puts together the analysis report
CreateMetaPathRnwReport(mSetObj, usrName)CreateMetaPathRnwReport(mSetObj, usrName)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
usrName |
Input the name of the user |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Function to create a table containing meta-analysis results.
CreateMetaTable(mSetObj = NA)CreateMetaTable(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Biomarker analysis report, ROC Curve Based Model Creation and Evaluation
CreateModelBiomarkersDoc(mSetObj = NA)CreateModelBiomarkersDoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Biomarker analysis report, Multivariate Biomarker Analysis
CreateMultiBiomarkersDoc(mSetObj = NA)CreateMultiBiomarkersDoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Function to create a summary table of mummichog analysis
Report generation using Sweave Function to create a summary table of mummichog analysis
CreateMummichogAnalTable(mSetObj = NA) CreateMummichogAnalTable(mSetObj = NA)CreateMummichogAnalTable(mSetObj = NA) CreateMummichogAnalTable(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Mummichog analysis report
Report generation using Sweave Mummichog analysis report
CreateMummichogAnalysisDoc(mSetObj = NA) CreateMummichogAnalysisDoc(mSetObj = NA)CreateMummichogAnalysisDoc(mSetObj = NA) CreateMummichogAnalysisDoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Mummichog analysis report, data input documentation.
Report generation using Sweave Mummichog analysis report, data input documentation.
CreateMummichogInputDoc(mSetObj = NA) CreateMummichogInputDoc(mSetObj = NA)CreateMummichogInputDoc(mSetObj = NA) CreateMummichogInputDoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Mummichog analysis report introduction
Report generation using Sweave Mummichog analysis report introduction
CreateMummichogIntro() CreateMummichogIntro()CreateMummichogIntro() CreateMummichogIntro()
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Function to create mummichog libraries from MetaboAnalyst pathway libraries (metpa). Outputs the RDS files in the current working directory. RDS files are saved using the KEGG organism code.
CreateMummichogLibs(folder, kegg_compounds)CreateMummichogLibs(folder, kegg_compounds)
folder |
Input the path of the folder containing the metpa rda files. |
kegg_compounds |
Input the name of the KEGG dictionary containing the KEGG compound IDs, KEGG compopund names, and molecular weight. |
Report generation using Sweave Function to create a summary table of mummichog analysis
CreateMummichogMetaAnalPathTable(mSetObj)CreateMummichogMetaAnalPathTable(mSetObj)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Functional Meta-Analysis Report
CreateMummichogMetaAnalReport(mSetObj)CreateMummichogMetaAnalReport(mSetObj)
mSetObj |
mSetObj |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Mummichog analysis report overview
Report generation using Sweave Mummichog analysis report overview
CreateMummichogOverview() CreateMummichogOverview()CreateMummichogOverview() CreateMummichogOverview()
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Puts together the analysis report
CreateMummichogRnwReport(mSetObj, usrName)CreateMummichogRnwReport(mSetObj, usrName)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
usrName |
Input the name of the user |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Biomarker analysis report, ROC Curve Based Model Creation and Evaluation
CreateNetworkExplorerDoc(mSetObj = NA)CreateNetworkExplorerDoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave network explorer report, data input documentation.
CreateNetworkExplorerInputDoc(mSetObj = NA)CreateNetworkExplorerInputDoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, viewingCanada License: GNU GPL (>= 2)
Report generation using Sweave Network explorer report introduction
CreateNetworkExplorerIntr()CreateNetworkExplorerIntr()
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave for the network explorer report overview
CreateNetworkExplorerOverview()CreateNetworkExplorerOverview()
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Puts together the analysis report
CreateNetworkExplorerRnwReport(mSetObj, usrName)CreateNetworkExplorerRnwReport(mSetObj, usrName)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
usrName |
Input the name of the user |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Function to create a table for gene name mapping
CreateNetworkGeneMapTable(mSetObj = NA)CreateNetworkGeneMapTable(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Function to create a table for compound name mapping
CreateNetworkNameMapTable(mSetObj = NA)CreateNetworkNameMapTable(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Create normalization document
CreateNORMdoc(mSetObj = NA)CreateNORMdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Create OPLSDA document
CreateOPLSDAdoc(mSetObj = NA)CreateOPLSDAdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Metabolomic pathway analysis Create pathway analysis doc
CreatePathAnalDoc(mSetObj = NA)CreatePathAnalDoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Metabolomic pathway analysis Create data input doc
CreatePathInputDoc()CreatePathInputDoc()
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Metabolomic pathway analysis Introduction
CreatePathIntr()CreatePathIntr()
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Metabolomic pathway analysis Create MetPA process
CreatePathProcessDoc(mSetObj = NA)CreatePathProcessDoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Metabolomic pathway analysis Create MetPA results doc
CreatePathResultDoc(mSetObj = NA)CreatePathResultDoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Metabolomic pathway analysis write .Rnw file template
CreatePathRnwReport(mSetObj, usrName)CreatePathRnwReport(mSetObj, usrName)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
usrName |
Input the name of the user |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Create PCA document
CreatePCAdoc(mSetObj = NA)CreatePCAdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Create PLS document
CreatePLSdoc(mSetObj = NA)CreatePLSdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Power analysis report, analysis
CreatePowerAnalDoc(mSetObj)CreatePowerAnalDoc(mSetObj)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Power analysis report, data input documentation.
CreatePowerInputDoc(mSetObj = NA)CreatePowerInputDoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Power analysis report introduction
CreatePowerIntr()CreatePowerIntr()
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Power analysis report overview
CreatePowerOverview()CreatePowerOverview()
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Power analysis report, parameter selection
CreatePowerParametersDoc(mSetObj = NA)CreatePowerParametersDoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Put together the analysis report
CreatePowerRnwReport(mSetObj, usrName)CreatePowerRnwReport(mSetObj, usrName)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
usrName |
Input the name of the user |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Random Forest
CreateRandomForest(mSetObj = NA)CreateRandomForest(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jessica Ewald [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Function to create a summary table for biomarker analysis: included metabolite ratios
CreateRatioTable(mSetObj = NA)CreateRatioTable(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Write .Rnw file template
CreateRawAnalysisRnwReport(mSetObj, usrName)CreateRawAnalysisRnwReport(mSetObj, usrName)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
usrName |
Input the name of the user |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Create Random Forest document
CreateRFdoc(mSetObj = NA)CreateRFdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Create footer
CreateRHistAppendix()CreateRHistAppendix()
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Function to create a table for newly classified samples
CreateROCLabelsTable(mSetObj = NA)CreateROCLabelsTable(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Create SAM document
CreateSAMdoc(mSetObj = NA)CreateSAMdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Create semitransparant colors for a given class label
CreateSemiTransColors(cls)CreateSemiTransColors(cls)
cls |
Input class labels |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Create SOM partitional clustering document
CreateSOMdoc(mSetObj = NA)CreateSOMdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Create sPLS-DA document
CreateSPLSDAdoc(mSetObj = NA)CreateSPLSDAdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Create header
CreateStatIntr()CreateStatIntr()
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Read and process raw data
CreateStatIOdoc(mSetObj = NA)CreateStatIOdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Write .Rnw file template
CreateStatRnwReport(mSetObj, usrName)CreateStatRnwReport(mSetObj, usrName)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
usrName |
Input the name of the user |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Create a summary table for each type of uploaded data csv table has 5 col: sampleID, feature #, zero, missing #
CreateSummaryTable(mSetObj = NA)CreateSummaryTable(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Create R-SVM document
CreateSVMdoc(mSetObj = NA)CreateSVMdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Creates empty time-series analysis message
CreateTimeSeriesAnalNullMsg()CreateTimeSeriesAnalNullMsg()
Report generation using Sweave Metabolomic pathway analysis, time-series Read and process the raw data
CreateTimeSeriesIOdoc(mSetObj = NA)CreateTimeSeriesIOdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Metabolomic pathway analysis Create timeseries .Rnw file template
CreateTimeSeriesRnwReport(mSetObj, usrName)CreateTimeSeriesRnwReport(mSetObj, usrName)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
usrName |
Input the name of the user |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Biomarker analysis report, Univariate Analysis
CreateUnivarBiomarkersDoc(mSetObj = NA)CreateUnivarBiomarkersDoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Create univariate analyses document
CreateUNIVdoc(mSetObj = NA)CreateUNIVdoc(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Function to create a summary table for univariate biomarker analysis
CreateUnivROCTable()CreateUnivROCTable()
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Report generation using Sweave Function to create a table containing meta-analysis results.
CreateVennMetaTable(mSetObj = NA)CreateVennMetaTable(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Given a list of compound names or ids find matched name or IDs from selected databases
CrossReferencing( mSetObj = NA, q.type, hmdb = T, pubchem = T, chebi = F, kegg = T, metlin = F, lipid = F )CrossReferencing( mSetObj = NA, q.type, hmdb = T, pubchem = T, chebi = F, kegg = T, metlin = F, lipid = F )
mSetObj |
Input the name of the created mSetObj (see InitDataObjects). |
q.type |
Input the query type, "name" for compound names, "hmdb" for HMDB IDs, "kegg" for KEGG IDs, "pubchem" for PubChem CIDs, "chebi" for ChEBI IDs, "metlin" for METLIN IDs, and "hmdb_kegg" for a both KEGG and HMDB IDs. |
hmdb |
Logical, T to cross reference to HMDB, F to not. |
pubchem |
Logical, T to cross reference to PubChem, F to not. |
chebi |
Logical, T to cross reference to CheBI, F to not. |
kegg |
Logical, T to cross reference to KEGG, F to not. |
metlin |
Logical, T to cross reference to MetLin, F to not. |
lipid |
Logical, if features are lipids (T), a different database will be used for compound matching. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform various utilities for peak grouping
descendMin(y, istart = which.max(y))descendMin(y, istart = which.max(y))
y |
Input peaks |
istart |
Performs which.max on y |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform compound mapping
doCompoundMapping(cmpd.vec, q.type)doCompoundMapping(cmpd.vec, q.type)
cmpd.vec |
Input compound vector |
q.type |
Query type |
Gene ID mapping, gene annotation, compound mapping, KEGG mapping
doGeneIDMapping(q.vec, org, type)doGeneIDMapping(q.vec, org, type)
q.vec |
Input the query |
org |
Input the organism type |
type |
Input the type of data to annotate |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform KEGG to compound name mapping
doKEGG2NameMapping(kegg.vec)doKEGG2NameMapping(kegg.vec)
kegg.vec |
Input vector of KEGG compounds |
Returns matched KO in the same order (NA if no match)
doKOFiltering(ko.vec, type)doKOFiltering(ko.vec, type)
ko.vec |
Input the vector containing KOs |
type |
Input the type |
Develop a Logistic Regression Model with all of the combined k-fold CV subsets
doLogisticRegMdl(x.train, y.train, x.test, y.test)doLogisticRegMdl(x.train, y.train, x.test, y.test)
x.train |
Input the X training set |
y.train |
Input the Y training set |
x.test |
Input the X test set |
y.test |
Input the Y test set |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
deteriming a0, only applicable for z.ebam (default)
EBAM.Init( mSetObj = NA, isPaired, isVarEq, nonPar, A0 = -99, delta, imgA0, imgSig, dpi = 72 )EBAM.Init( mSetObj = NA, isPaired, isVarEq, nonPar, A0 = -99, delta, imgA0, imgSig, dpi = 72 )
mSetObj |
Input name of the created mSet Object |
isPaired |
Logical |
isVarEq |
Logical |
nonPar |
nonPar |
A0 |
A0 |
delta |
delta |
imgA0 |
imgA0 |
imgSig |
imgSig |
dpi |
dpi value of images |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform fold change analysis, method can be mean or median
FC.Anal(mSetObj, fc.thresh=2, cmp.type = 0, paired=FALSE)FC.Anal(mSetObj, fc.thresh=2, cmp.type = 0, paired=FALSE)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
fc.thresh |
Fold-change threshold, numeric input |
cmp.type |
Comparison type, 0 for group 1 minus group 2, and 1 for group 1 minus group 2 |
paired |
Logical, TRUE or FALSE |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Calculate correlation of all other feature to a given feature name
FeatureCorrelation(mSetObj = NA, dist.name, varName)FeatureCorrelation(mSetObj = NA, dist.name, varName)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
dist.name |
Input the name of the distance measure |
varName |
Input the variable name |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
This is a function that filters the dataset, dependent on the user-specified method for filtering. The function applies a filtering method, ranks the variables within the dataset, and removes variables based on its rank. The final dataset should contain no more than than 5000 variables for effective computing.
FilterVariable(mSetObj=NA, filter, qcFilter, rsd)FilterVariable(mSetObj=NA, filter, qcFilter, rsd)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
qc.filter |
Filter the variables based on QC samples - True (T), or use non-QC based filtering - False (F). |
rsd |
Define the relative standard deviation cut-off. Variables with a RSD greater than this number will be removed from the dataset. It is only necessary to specify this argument if qc.filter is True (T). Otherwise, it will not be used in the function. |
var.filter |
Select the filter option, "rsd" which is the relative standard deviation, "nrsd" which is the non-parametric relative standard deviation, "mean" which is the mean, "sd" which is the standard deviation, "mad" which is the median absolute deviation, or "iqr" which is the interquantile range. |
var.cutoff |
var.cutoff value |
int.filter |
int.filter value |
int.cutoff |
int.cutoff value, numeric |
filter.cutoff |
percent to be filtered, for example, 5 (5%) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform various utilities for peak grouping
findEqualGreaterM(x, values)findEqualGreaterM(x, values)
x |
Input the data |
values |
Input the values |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform Fisher LSD for ANOVA, used in higher function
FisherLSD(aov.obj, thresh)FisherLSD(aov.obj, thresh)
aov.obj |
Input the anova object |
thresh |
Numeric, input the alpha threshold |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
GeneratePeakList is used to generate the peak summary list for result page
GeneratePeakList(userPath)GeneratePeakList(userPath)
userPath |
userPath |
Zhiqiang Pang
Dummy is used only for the purpose to maintain lapply API this is used for permutation on ANOVA paritions, not on the SCA/PCA part, so the number of selected components is not applicable in this step
Get.asca.tss(dummy, perm = T)Get.asca.tss(dummy, perm = T)
dummy |
Dummy variable |
perm |
Logical, TRUE by default |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Columns have labels, x is a numeric vector, cl is consecutive integers
Get.bwss(x, cl)Get.bwss(x, cl)
x |
Numeric vector |
cl |
Columns |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Get the concentration reference
Get.ConcRef(mSetObj = NA, cmpd.nm)Get.ConcRef(mSetObj = NA, cmpd.nm)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
cmpd.nm |
Input the compound name |
note, the leverage combines all components the importance feature is for the factor not per components
Get.Leverage(XKw, Fac)Get.Leverage(XKw, Fac)
XKw |
Features |
Fac |
Factor |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Calculate partial area under ROC curve
Get.pAUC(x, y, focus, cutoff)Get.pAUC(x, y, focus, cutoff)
x |
Input X |
y |
Input Y |
focus |
Method |
cutoff |
Numeric |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Get predicted class probability, used in higher function
Get.pred(x.train, y.train, x.test, y.test, clsMethod = "pls")Get.pred(x.train, y.train, x.test, y.test, clsMethod = "pls")
x.train |
Training X |
y.train |
Training Y |
x.test |
Test X |
y.test |
Test Y |
clsMethod |
Method to predict class, by default it is PLS |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
x must be an rpart object
Get.rpart.summary(x)Get.rpart.summary(x)
x |
An Rpart object |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Users give a pls object ('oscorespls'=T), function calculates VIP score usually one VIP for each component, return is the average of all VIP
Get.VIP(pls.obj, comp = 2)Get.VIP(pls.obj, comp = 2)
pls.obj |
Input the PLS object |
comp |
Numeric, input the number of components, by default it is 2 |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Concentration or intensity data type
GetAbundanceLabel(data.type)GetAbundanceLabel(data.type)
data.type |
Input concentration or intensity data |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Export biomarker accuracy information
GetAccuracyInfo(mSetObj = NA)GetAccuracyInfo(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Get all meta-analysis name data
GetAllDataNames()GetAllDataNames()
K-means analysis - cluster
GetAllKMClusterMembers(mSetObj = NA)GetAllKMClusterMembers(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Get members for given cluster index, return a character string
GetAllSOMClusterMembers(mSetObj = NA)GetAllSOMClusterMembers(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Returns 3 coloumns - inx, name, score
GetCandidateList(mSetObj = NA, lipid)GetCandidateList(mSetObj = NA, lipid)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
lipid |
Logical |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Export information about selected circle
GetCircleInfo(mSetObj = NA)GetCircleInfo(mSetObj = NA)
mSetObj |
Input name of the created mSet Object |
For non-parametric tests, use quantiles, use normal (1.96*std.err) if parametric
GetCIs(data, param = F)GetCIs(data, param = F)
data |
Input data matrix |
param |
Logical, False by default |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Fetches the last command from the Rhistory.R file
GetCMD(regexp)GetCMD(regexp)
regexp |
Retrieve last command from Rhistory file |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Function to get adduct details from a specified compound. The results will be both printed in the console as well as saved as a csv file. Note that performing this function multiple times will overwrite previous queries.
GetCompoundDetails(mSetObj = NA, cmpd.id)GetCompoundDetails(mSetObj = NA, cmpd.id)
mSetObj |
Input the name of the created mSetObj object. |
cmpd.id |
Input the name of the selected compound. |
Obtain the full path to convert (from imagemagik) for cropping images
GetConvertFullPath()GetConvertFullPath()
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Transform two column input text to data matrix (single column data frame)
getDataFromTextArea(txtInput, sep.type = "space")getDataFromTextArea(txtInput, sep.type = "space")
txtInput |
Input text |
sep.type |
Indicate the seperator type for input text. Default set to "space" |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Extends the axis range to both ends vec is the values for that axis unit is the width to extend, 10 will increase by 1/10 of the range
GetExtendRange(vec, unit = 10)GetExtendRange(vec, unit = 10)
vec |
Input the vector |
unit |
Numeric |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Utility method to calculate FC, used in higher function
GetFC(mSetObj = NA, paired = FALSE, cmpType)GetFC(mSetObj = NA, paired = FALSE, cmpType)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
paired |
Logical, true of false |
cmpType |
Numeric, 0 or 1 |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Return a series of number for subsets selection
GetFeatureNumbers(feat.len)GetFeatureNumbers(feat.len)
feat.len |
Input the feature length |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Returns three columns: original name, HMDB name and KEGG ID, for enrichment and pathway analysis, respectively
GetFinalNameMap(mSetObj = NA, lipid = FALSE)GetFinalNameMap(mSetObj = NA, lipid = FALSE)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
lipid |
Logical |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Get fisher p-values
GetFisherPvalue(numSigMembers, numSigAll, numMembers, numAllMembers)GetFisherPvalue(numSigMembers, numSigAll, numMembers, numAllMembers)
numSigMembers |
Number of significant members |
numSigAll |
Number of all significant features |
numMembers |
Number of members |
numAllMembers |
Number of all members |
Get all group names
GetGroupNames(mSetObj = NA, exp.fac = NA)GetGroupNames(mSetObj = NA, exp.fac = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
exp.fac |
exp.fac |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Given a metset inx, return hmtl highlighted metset cmpds and references
GetHTMLMetSet(mSetObj = NA, msetNm)GetHTMLMetSet(mSetObj = NA, msetNm)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
msetNm |
Input the name of the metabolite set |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Given a metset inx, return hmtl highlighted pathway cmpds
GetHTMLPathSet(mSetObj = NA, msetNm)GetHTMLPathSet(mSetObj = NA, msetNm)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
msetNm |
Input the name of the metabolite set |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
feat.outp is a list that contains the ranked features in each cross validation (CV) and returns a two column matrix, col 1 = median ranking and col 2 = mean importance measure
GetImpFeatureMat(mSetObj = NA, feat.outp, bestFeatNum)GetImpFeatureMat(mSetObj = NA, feat.outp, bestFeatNum)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
feat.outp |
Input the list that contains the ranked features in each cross validation (CV) and returns a two column matrix, col 1 = median ranking and col 2 = mean importance measure |
bestFeatNum |
Numeric |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Retrieves KEGG node information
GetKEGGNodeInfo(pathName, g, width, height, usr = par("usr"))GetKEGGNodeInfo(pathName, g, width, height, usr = par("usr"))
pathName |
Input the path Name |
g |
Input data |
width |
Input the width |
height |
Input the height |
usr |
Input the user |
Get the cluster members for given index add HTML color to the names based on its group membership
GetKMClusterMembers(mSetObj = NA, i)GetKMClusterMembers(mSetObj = NA, i)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
i |
Input the cluster index |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Not part of default, need to perform function to compute lasso frequency msg: There are more than 500 variables and n<m You may wish to restart and set use.Gram=FALSE
GetLassoFreqs(mSetObj = NA)GetLassoFreqs(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Get result table from eBayes fit object
GetLimmaResTable(fit.obj)GetLimmaResTable(fit.obj)
fit.obj |
eBayes fit object to parse to a table |
Return results from compound name mapping in a table
GetMapTable(mSetObj = NA)GetMapTable(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Rotate PCA analysis
GetMaxPCAComp(mSetObj = NA)GetMaxPCAComp(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
perf is the performance object from ROCR
GetMeanROC(perf)GetMeanROC(perf)
perf |
Performance object from ROCR |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Single.type return logFC or p value for individual data analysis
GetMetaResultMatrix(mSetObj = NA, single.type = "fc")GetMetaResultMatrix(mSetObj = NA, single.type = "fc")
mSetObj |
Input name of the created mSet Object |
single.type |
Default is "fc" |
Given a metset inx, give its name
GetMetSetName(mSetObj = NA, msetInx)GetMetSetName(mSetObj = NA, msetInx)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
msetInx |
Input the index of the metabolite set |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Get the library check messages
GetMsetLibCheckMsg(mSetObj = NA)GetMsetLibCheckMsg(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Return the selected metset library to java for display
GetMsetNames(mSetObj = NA)GetMsetNames(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Function to get compound details from a specified pathway. The results will be both printed in the console as well as saved as a csv file. Note that performing this function multiple times will overwrite previous queries. Significant compounds will be indicated with an asterisk.
GetMummichogPathSetDetails(mSetObj = NA, msetNm)GetMummichogPathSetDetails(mSetObj = NA, msetNm)
mSetObj |
Input the name of the created mSetObj object. |
msetNm |
Input the name of the pathway |
Exports Gene-Mapping result into a table
GetNetworkGeneMappingResultTable(mSetObj = NA)GetNetworkGeneMappingResultTable(mSetObj = NA)
mSetObj |
Input name of the created mSet Object |
Obtain sample names and their class labels
GetNewSampleNames(mSetObj = NA)GetNewSampleNames(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
This function uses the httr R package to make an API call to the Metabolomics Workbench to download and save a dataset based on the Study ID into the current working directory.
GetNMDRStudy(mSetObj=NA, StudyID)GetNMDRStudy(mSetObj=NA, StudyID)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects). |
StudyID |
Input the StudyID of the study from the Metabolomics Workbench. Use the ListNMDRStudies function to obtain a list of all available studies from the Metabolomics Workbench. |
Jeff Xia [email protected], Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Export pathway names from ORA analysis
GetORA.pathNames(mSetObj = NA)GetORA.pathNames(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Only for human pathways + ath, eco, mmu & sce
GetORA.smpdbIDs(mSetObj = NA)GetORA.smpdbIDs(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Get ORA table
GetORATable(mSetObj = NA)GetORATable(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Only for human pathways + ath, eco, mmu & sce
GetQEA.keggIDs(mSetObj = NA)GetQEA.keggIDs(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Export pathway names from QEA analysis
GetQEA.pathNames(mSetObj = NA)GetQEA.pathNames(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
QEA table
GetQEATable(mSetObj = NA)GetQEATable(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Export R Command History
GetRCommandHistory(mSetObj = NA)GetRCommandHistory(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Classification performance table for random forest analysis
GetRFConf.Table(mSetObj = NA)GetRFConf.Table(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Return double confusion matrix
GetRFConfMat(mSetObj = NA)GetRFConfMat(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Get the OOB error for the last signif
GetRFOOB(mSetObj = NA)GetRFOOB(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Significance measure, double brackets
GetRFSigMat(mSetObj = NA)GetRFSigMat(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Return ROC corodinates with confidence intervals
GetROC.coords(mSetObj = NA, fld.nm, val, plot = TRUE, imgNm)GetROC.coords(mSetObj = NA, fld.nm, val, plot = TRUE, imgNm)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
fld.nm |
The kind of input coordinate |
val |
The coordinates to look for |
plot |
Logical, by default set to TRUE |
imgNm |
Input the image name |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Get p-values from lasso
GetROCLassoFreq(data, cls)GetROCLassoFreq(data, cls)
data |
Input data |
cls |
Input class labels |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
ROC p-vaues, used in higher function
GetROCTtestP(data, cls)GetROCTtestP(data, cls)
data |
Input data |
cls |
Input class labels |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Return sample size ladder, used in higher functions
GetSampleSizeLadder(maxNum)GetSampleSizeLadder(maxNum)
maxNum |
Numeric |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
generate Latex table
GetSigTable(mat, method, data.type)GetSigTable(mat, method, data.type)
mat |
Input matrix |
method |
Input method to create table |
data.type |
Input the data type |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Sig Table for Anova
GetSigTable.Anova(mSetObj = NA)GetSigTable.Anova(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Sig table for AOV2
GetSigTable.Aov2(mSetObj = NA)GetSigTable.Aov2(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Table of features well modelled by ASCA
GetSigTable.ASCA(mSetObj = NA, nm)GetSigTable.ASCA(mSetObj = NA, nm)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
nm |
Input the name of the well modelled features |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Sig table for Correlation Analysis
GetSigTable.Corr(mSetObj = NA)GetSigTable.Corr(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Sig Table for Anova
GetSigTable.Dose(mSetObj = NA)GetSigTable.Dose(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Sig table for EBAM
GetSigTable.EBAM(mSetObj = NA)GetSigTable.EBAM(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Sig Table for Fold-Change Analysis
GetSigTable.FC(mSetObj = NA)GetSigTable.FC(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Sig table for MB analysis
GetSigTable.MB(mSetObj = NA)GetSigTable.MB(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Sig table for random forest analysis
GetSigTable.RF(mSetObj = NA)GetSigTable.RF(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Sig table for SAM
GetSigTable.SAM(mSetObj = NA)GetSigTable.SAM(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Sig table for SVM
GetSigTable.SVM(mSetObj = NA)GetSigTable.SVM(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Sig Table for T-test Analysis
GetSigTable.TT(mSetObj = NA)GetSigTable.TT(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Sig table for Volcano Analysis
GetSigTable.Volcano(mSetObj = NA)GetSigTable.Volcano(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Get members for given cluster index, return a character string
GetSOMClusterMembers(mSetObj = NA, i, j)GetSOMClusterMembers(mSetObj = NA, i, j)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
i |
Index of X |
j |
Index of Y |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Replace the last column of the ssp.mat with the final selection from users
GetSSPTable(mSetObj = NA)GetSSPTable(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
obtain a default delta with reasonable number of sig features and decent FDR
GetSuggestedSAMDelta(mSetObj = NA)GetSuggestedSAMDelta(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Return significance measure, double[][]
GetSVMSigMat(mSetObj = NA)GetSVMSigMat(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Get indices of top n largest/smallest number
GetTopInx(vec, n, dec = T)GetTopInx(vec, n, dec = T)
vec |
Vector containing volcano indices |
n |
Numeric |
dec |
Logical, default set to TRUE |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Make random partitions, returns matrices indicating whether the observation is in train/test for each run note: try to get a balanced sampling for each group (classification) or each quantile (regression). This is very useful for unbalanced data
GetTrainTestSplitMat(y, propTraining = 2/3, nRuns = 30)GetTrainTestSplitMat(y, propTraining = 2/3, nRuns = 30)
y |
Input the data |
propTraining |
By default set to 2/3 |
nRuns |
By default set to 30 |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Utility method to get p values
GetTtestRes(mSetObj = NA, paired = FALSE, equal.var = TRUE, nonpar = F)GetTtestRes(mSetObj = NA, paired = FALSE, equal.var = TRUE, nonpar = F)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
paired |
Default set to FALSE |
equal.var |
Default set to TRUE |
nonpar |
Use non-parametric tests, default is set to FALSE |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Return a double matrix with 2 columns - p values and lod
GetTTSigMat(mSetObj = NA)GetTTSigMat(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
The approach is computationally expensive,and fails more often get around: make it lazy unless users request, otherwise the default t-test will also be affected
GetUnivReport(mSetObj = NA)GetUnivReport(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Determine data type, binned spectra, nmr peak, or ms peak
GetVariableLabel(data.type)GetVariableLabel(data.type)
data.type |
Input the data type |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Determine the number of rows and columns for a given total number of plots (used by Kmeans and SOM plots)
GetXYCluster(total)GetXYCluster(total)
total |
Input the total |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Group peaks from the peak list based on position using the XCMS grouping algorithm (align peaks wrt, rt, and mz). For NMR peaks, need to change ppm -> mz and add dummy rt. If the data is 2-column MS, first need to add dummy rt. If the data is 3-column MS, the data can be used directly. The default mzwid for MS is 0.25 m/z, and for NMR is 0.03 ppm. The default bw is 30 for LCMS, and 5 for GCMS.
GroupPeakList(mSetObj=NA, mzwid, bw, minfrac, minsamp, max)GroupPeakList(mSetObj=NA, mzwid, bw, minfrac, minsamp, max)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
mzwid |
define the width of overlapping m/z slices to use for creating peak density chromatograms and grouping peaks across samples |
bw |
define the bandwidth (standard deviation or half width at half maximum) of gaussian smoothing kernel to apply to the peak density chromatogram |
minfrac |
define the minimum fraction of samples necessary in at least one of the sample groups for it to be a valid group |
minsamp |
define the minimum number of samples necessary in at least one of the sample groups for it to be a valid group |
max |
define the maximum number of groups to identify in a single m/z slice |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
function to calculate tick mark based on Heckbert algorithm available in the "labeling" package implemented by Justin Talbot adapted from the imagemap package Heckbert's labeling algorithm Heckbert, P. S. (1990) Nice numbers for graph labels, Graphics Gems I, Academic Press Professional, Inc.
heckbert(dmin, dmax, m)heckbert(dmin, dmax, m)
dmin |
Heckbert |
dmax |
Heckbert |
m |
Heckbert |
Justin Talbot [email protected]
This function, when given a vector of HMDBIDs, returns a vector of KEGG ID. HMDB standing for the Human Metabolome Database.
HMDBID2KEGGID(ids)HMDBID2KEGGID(ids)
ids |
Input the vector of HMDB Ids |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
This function, when given a vector of HMDBIDs, return a vector of HMDB compound names. HMDB standing for the Human Metabolome Database.
HMDBID2Name(ids)HMDBID2Name(ids)
ids |
Input the vector of HMDB Ids |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Replace missing variables by min/mean/median/KNN/BPCA/PPCA/svdImpute.
ImputeMissingVar(mSetObj, method)ImputeMissingVar(mSetObj, method)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
method |
Select the option to replace missing variables, either replacement based on the minimum ("min), the mean ("mean"), or the median ("median") value of each feature columns, or several options to impute the missing values, using k-nearest neighbour ("KNN"), probabilistic PCA ("PPCA"), Bayesian PCA ("BPCA") method, or Singular Value Decomposition ("svdImpute") |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
This functions handles the construction of a mSetObj object for storing data for further processing and analysis. It is necessary to utilize this function to specify to MetaboAnalystR the type of data and the type of analysis you will perform.
InitDataObjects(data.type, anal.type, paired=FALSE)InitDataObjects(data.type, anal.type, paired=FALSE)
data.type |
The type of data, either list (Compound lists), conc (Compound concentration data), specbin (Binned spectra data), pktable (Peak intensity table), nmrpeak (NMR peak lists), mspeak (MS peak lists), or msspec (MS spectra data) |
anal.type |
Indicate the analysis module to be performed: stat, pathora, pathqea, msetora, msetssp, msetqea, mf, cmpdmap, smpmap, or pathinteg |
paired |
indicate if the data is paired or not. Logical, default set to FALSE |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
this function is used to initialize a plan before submit job to spring daemon
InitializaPlan()InitializaPlan()
Zhiqiang Pang
InitMSObjects
InitMSObjects(data.type = NULL, anal.type = NULL, paired = FALSE)InitMSObjects(data.type = NULL, anal.type = NULL, paired = FALSE)
data.type |
should be "raw" |
anal.type |
should be "spec" |
paired |
should be "FALSE" |
Perform power analysis, requires the SSPA R package.
InitPowerAnal(mSetObj, clsOpts)InitPowerAnal(mSetObj, clsOpts)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
clsOpts |
For data with >2 groups, specify the two classes on which to perform power analysis, otherwise for data with 2 groups, "NA" will automatically select the 2 groups. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Introduction for statistical analysis module report Initialize Statistical Analysis Report
InitStatAnalMode()InitStatAnalMode()
Report generation using Sweave Metabolomic pathway analysis, time-series analysis
InitTimeSeriesAnal()InitTimeSeriesAnal()
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform PCA analysis, prepares a JSON file for interactive liveGraphics3D, as well as interactive 3D PCA score and loading plots using the plotly R package. These plots are saved in the created mSetObj; to view these, type "mSetObj$imgSet$time$score3d" to view the interactive score plot, and "mSetObj$imgSet$time$load3d" to view the interactive loading plot.
iPCA.Anal(mSetObj, fileNm, metaCol, metaShape)iPCA.Anal(mSetObj, fileNm, metaCol, metaShape)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
fileNm |
select a file name |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Test if a sig table matrix is empty
isEmptyMatrix(mat)isEmptyMatrix(mat)
mat |
Matrix to test if empty |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Returns whether or not the sanity check found that there were too many groups in the dataset containing too few samples. It will return a 0 if the data passes the check, or will return a 1 if the data does not.
IsSmallSmplSize(mSetObj=NA)IsSmallSmplSize(mSetObj=NA)
mSetObj |
Input name of the created mSet Object |
This functionn, when given a vector of KEGGIDs, returns a vector of HMDB IDs. HMDB standing for the Human Metabolome Database.
KEGGID2HMDBID(ids)KEGGID2HMDBID(ids)
ids |
Vector of KEGG ids |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
This function, given a vector containing KEGGIDs, returns a vector of KEGG compound names.
KEGGID2Name(ids)KEGGID2Name(ids)
ids |
Vector of KEGG ids |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
This function, when given a vector of KEGG pathway IDs, return a vector of SMPDB IDs (only for hsa). SMPDB standing for the Small Molecule Pathway Database, and hsa standing for human serum albumin.
KEGGPATHID2SMPDBIDs(ids)KEGGPATHID2SMPDBIDs(ids)
ids |
Vector of KEGG pathway IDs |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform K-means analysis
Kmeans.Anal(mSetObj = NA, clust.num)Kmeans.Anal(mSetObj = NA, clust.num)
mSetObj |
Input name of the created mSet Object |
clust.num |
Numeric, input the number of clusters for K-means analysis |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform Kruskal-Wallis Test
kwtest(x, cls)kwtest(x, cls)
x |
Input data to perform Kruskal-Wallis |
cls |
Input class labels |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
This function uses the httr R package to make an API call to the Metabolomics Workbench to retrieve a table of all compatible datasets.
ListNMDRStudies(mSetObj=NA)ListNMDRStudies(mSetObj=NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects). |
Jeff Xia [email protected], Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Utility function for PerformKOEnrichAnalysis_KO01100
LoadKEGGKO_lib(category)LoadKEGGKO_lib(category)
category |
Module or pathway |
Column-wise norm methods, when x is a column Options for log, zero mean and unit variance, and several zero mean and variance/SE
LogNorm(x, min.val)LogNorm(x, min.val)
x |
Input data |
min.val |
Input minimum value |
Jeff Xia [email protected] McGill University, Canada
From a vector of m/z features, this function outputs a vector of compounds.
make_cpdlist(mSetObj=NA, input_mzs)make_cpdlist(mSetObj=NA, input_mzs)
mSetObj |
Input the name of the created mSetObj |
input_mzs |
The vector of randomly drawn m/z features. |
Jasmine Chong, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
From a vector of m/z features, this function outputs a vector of compounds.
make_ecpdlist(mSetObj=NA, input_mzs)make_ecpdlist(mSetObj=NA, input_mzs)
mSetObj |
Input the name of the created mSetObj |
input_mzs |
The vector of randomly drawn m/z features. |
Jasmine Chong, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
map variable for (s)plsda
map(Y)map(Y)
Y |
Input data |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Utility function for PrepareKeggQueryJson
MapCmpd2KEGGNodes(cmpds, net = "ko01100")MapCmpd2KEGGNodes(cmpds, net = "ko01100")
cmpds |
Input the compounds |
net |
Input the network name |
Utility function for PrepareKeggQueryJson
MapKO2KEGGEdges(kos, net = "ko01100")MapKO2KEGGEdges(kos, net = "ko01100")
kos |
Input the KOs |
net |
Input the name of the network |
Match pattern for correlation analysis
Match.Pattern(mSetObj = NA, dist.name = "pearson", pattern = NULL)Match.Pattern(mSetObj = NA, dist.name = "pearson", pattern = NULL)
mSetObj |
Input the name of the created mSetObj |
dist.name |
Input the distance method, default is set to pearson |
pattern |
Set the pattern, default is set to NULL |
This the generic melt function. See the following functions for the details about different data structures:
melt(data, ..., na.rm = FALSE, value.name = "value")melt(data, ..., na.rm = FALSE, value.name = "value")
data |
Data set to melt |
... |
further arguments passed to or from other methods. |
na.rm |
Should NA values be removed from the data set? This will convert explicit missings to implicit missings. |
value.name |
name of variable used to store values |
Utility function for PrepareKeggQueryJson
MergeDatasets(dataSet1, dataSet2)MergeDatasets(dataSet1, dataSet2)
dataSet1 |
Input the first dataset |
dataSet2 |
Input the second dataset |
dim 1 => row, dim 2 => column
MergeDuplicates(data, dim = 2)MergeDuplicates(data, dim = 2)
data |
Input the data |
dim |
Numeric, input the dimensions, default is set to 2 |
The MetaboAnalystR package provides a pipeline for metabolomics processing.
The MetaboAnalystR functions ...
For compound names to other ids, can do exact or approximate matches For other IDs, except HMDB ID, all others may return multiple/non-unique hits Multiple hits or non-unique hits will allow users to manually select
MetaboliteMappingExact(mSetObj = NA, q.type, lipid = F)MetaboliteMappingExact(mSetObj = NA, q.type, lipid = F)
mSetObj |
Input the name of the created mSetObj. |
q.type |
Inpute the query-type, "name" for compound names, "hmdb" for HMDB IDs, "kegg" for KEGG IDs, "pubchem" for PubChem CIDs, "chebi" for ChEBI IDs, "metlin" for METLIN IDs, and "hmdb_kegg" for a both KEGG and HMDB IDs. |
lipid |
Boolean, if features are lipids, a different database will be used for compound matching. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
MetaPathNormalization
MetaPathNormalization( mSetObj = NA, sampleNor, tranform, scale = "NULL", name, name2 )MetaPathNormalization( mSetObj = NA, sampleNor, tranform, scale = "NULL", name, name2 )
mSetObj |
mSetObj |
sampleNor |
sample Normalization option |
tranform |
sample transformation option |
scale |
sample scale option |
name |
file name 1 with absolute path |
name2 |
file name 2 with absolute path or "null" |
Jeff Xia[email protected] Zhiqiang Pang[email protected] McGill University, Canada License: GNU GPL (>= 2)
Get multiple category statistics
multi.stat(pred, resp)multi.stat(pred, resp)
pred |
Input predictions |
resp |
Input responses |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Adapted from the 'agricolae' package
my.lsd.test(y, trt, alpha = 0.05)my.lsd.test(y, trt, alpha = 0.05)
y |
Input Y |
trt |
Input trt |
alpha |
Numeric, default is 0.05 |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Read peak list files. This function reads peak list files and fills the data into a dataSet object. For NMR peak lists, the input should be formatted as two-columns containing numeric values (ppm, int). Further, this function will change ppm to mz, and add a dummy 'rt'. For MS peak data, the lists can be formatted as two-columns (mz, int), in which case the function will add a dummy 'rt', or the lists can be formatted as three-columns (mz, rt, int).
my.parse.peaklist(mSetObj = NA, foldername = "upload")my.parse.peaklist(mSetObj = NA, foldername = "upload")
mSetObj |
Input the name of the created mSetObj (see InitDataObjects). |
foldername |
Name of the folder containing the NMR or MS peak list files to read. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
This function performs row-wise normalization, transformation, and scaling of your metabolomic data.
Normalization(mSetObj, rowNorm, transNorm, scaleNorm, ref=NULL, ratio=FALSE, ratioNum=20)Normalization(mSetObj, rowNorm, transNorm, scaleNorm, ref=NULL, ratio=FALSE, ratioNum=20)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
rowNorm |
Select the option for row-wise normalization, "QuantileNorm" for Quantile Normalization, "CompNorm" for Normalization by a reference feature, "SumNorm" for Normalization to constant sum, "MedianNorm" for Normalization to sample median, and "SpecNorm" for Normalization by a sample-specific factor. |
transNorm |
Select option to transform the data, "LogNorm" for Log Normalization, and "CrNorm" for Cubic Root Transformation. |
scaleNorm |
Select option for scaling the data, "MeanCenter" for Mean Centering, "AutoNorm" for Autoscaling, "ParetoNorm" for Pareto Scaling, amd "RangeNorm" for Range Scaling. |
ref |
Input the name of the reference sample or the reference feature, use " " around the name. |
ratio |
This option is only for biomarker analysis. |
ratioNum |
Relevant only for biomarker analysis. |
Jeff Xia [email protected], Jasmine Chong McGill University, Canada
Orthogonal PLS-DA (from ropls) perform permutation, using training classification accuracy as indicator, for two or multi-groups
OPLSDA.Permut(mSetObj = NA, num = 100)OPLSDA.Permut(mSetObj = NA, num = 100)
mSetObj |
Input name of the created mSet Object |
num |
Input the number of permutations, default is set to 100. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Orthogonal PLS-DA (from ropls) Add reg (regression i.e. if class order matters)
OPLSR.Anal(mSetObj = NA, reg = FALSE)OPLSR.Anal(mSetObj = NA, reg = FALSE)
mSetObj |
Input name of the created mSet Object |
reg |
Logical |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Return only the signicant comparison names, used in higher function
parseFisher(fisher, cut.off)parseFisher(fisher, cut.off)
fisher |
Input fisher object |
cut.off |
Numeric, set cut-off |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Return only the signicant comparison names, used in higher function
parseTukey(tukey, cut.off)parseTukey(tukey, cut.off)
tukey |
Input tukey output |
cut.off |
Input numeric cut-off |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform PCA analysis, obtain variance explained, store item to PCA object
PCA.Anal(mSetObj = NA)PCA.Anal(mSetObj = NA)
mSetObj |
Input name of the created mSet Object McGill University, Canada License: GNU GPL (>= 2) |
Jeff Xia[email protected]
Rotate PCA analysis
PCA.Flip(mSetObj = NA, axisOpt)PCA.Flip(mSetObj = NA, axisOpt)
mSetObj |
Input name of the created mSet Object |
axisOpt |
Input the axis option |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
X is a matrix that has as columns the compounds that were considered as variables in the PCA analysis. First we center the matrix by columns (Xoff) and then we obtain the eigenvalues and the eigenvectors of the matrix Xoff use the equivalences between the loadings and scores to obtain the solution
PCA.GENES(X)PCA.GENES(X)
X |
Input matrix that has as columns the compounds that were considered as variables in the PCA analysis |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
The ASCA algorithm was adapted from the ASCA-genes method (analysis of variance (ANOVA) simultaneous component analysis) by Maria Jose Nueda ([email protected]) and Ana Conesa ([email protected])
Perform.ASCA(mSetObj = NA, a = 1, b = 2, x = 2, res = 2)Perform.ASCA(mSetObj = NA, a = 1, b = 2, x = 2, res = 2)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
a |
specify the number of components for facA |
b |
specify the number of components for facB |
x |
specify the number of components for interaction AB |
res |
specify the number of model residuals type is string, indicating the type of analysis "abc" separately "aab" facA joins with AB "bab" facB joins with AB |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform ASCA model validation by permutation we use Manly's unrestricted permutation of observations which esentially permutes the data over all cells in the designed experiment, then calculates the score for each main factor or interaction components. This will get the null distribution for all effects in one go
Perform.ASCA.permute(mSetObj=NA, perm.num)Perform.ASCA.permute(mSetObj=NA, perm.num)
mSetObj |
Input name of the created mSet Object |
perm.num |
Select the number of permutations, default is 20 |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform permutation tests for the ROC Curve Based Model Creation and Evaluation module
Perform.Permut(mSetObj=NA, perf.measure, perm.num, propTraining = 2/3)Perform.Permut(mSetObj=NA, perf.measure, perm.num, propTraining = 2/3)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
perf.measure |
Input the performance measure to rate the performance of the model, either the area under the ROC curve ("auroc") or the predictive accuracy ("accu") |
perm.num |
Input the number of permutations to perform |
propTraining |
Numeric, input the fraction of samples to set aside for training. Default is set to 2/3. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform permutation, options to change number of cores used
Perform.permutation(perm.num, fun)Perform.permutation(perm.num, fun)
perm.num |
Numeric, input the number of permutations to perform |
fun |
Dummy function |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform Classical Univariate ROC
Perform.UnivROC(mSetObj=NA, feat.nm, version, format="png", dpi=72, isAUC, isOpt, optMethod, isPartial, measure, cutoff)Perform.UnivROC(mSetObj=NA, feat.nm, version, format="png", dpi=72, isAUC, isOpt, optMethod, isPartial, measure, cutoff)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
feat.nm |
Input the name of the feature to perform univariate ROC analysis |
version |
image version mark, can be any character |
format |
Select the image format, png, of pdf. |
dpi |
Input the dpi. If the image format is pdf, users need not define the dpi. For png images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
isAUC |
Logical, select T to compute the 95 percent confidence interval band and "F" to not |
isOpt |
Logical, show the optimal cutoff, T to show it and F to not |
optMethod |
Select the optimal cutoff by using either closest.topleft for closest to top-left corner or youden for farthest to the diagonal line (Youden) |
isPartial |
Logical, input T to calculate a partial ROC curve, and F to not |
measure |
Select the parameter to limit the calculation of the partial ROC curve, se for the X-axis (maximum false-positive rate) and sp for the Y-axis, representing the minimum true positive-rate |
cutoff |
Input the threshold to limit the calculation of the partial ROC curve, the number must be between 0 and 1. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
This function reads in the user's adduct list and saves it as a matrix.
PerformAdductMapping(mSetObj = NA, add.mode)PerformAdductMapping(mSetObj = NA, add.mode)
mSetObj |
Input the name of the created mSetObj object |
add.mode |
Adduct mode, positive or negative |
Jasmine Chong, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Given a query, perform approximate compound matching
PerformApproxMatch(mSetObj = NA, q, lipid)PerformApproxMatch(mSetObj = NA, q, lipid)
mSetObj |
Input the name of the created mSetObj. |
q |
Input the q vector. |
lipid |
lipid, logical |
This function is designed to perform the batch effect correction
PerformBatchCorrection( mSetObj = NA, imgName = NULL, Method = NULL, center = NULL )PerformBatchCorrection( mSetObj = NA, imgName = NULL, Method = NULL, center = NULL )
mSetObj |
Input name of the created mSet Object |
imgName |
Input the name of the plot to create |
Method |
Batch effect correction method, default is "auto". Specific method, including "Combat", "WaveICA","EigenMS","QC_RLSC","ANCOVA","RUV_random","RUV_2","RUV_s","RUV_r","RUV_g","NOMIS" and "CCMN". |
center |
The center point of the batch effect correction, based on "QC" or "", which means correct to minimize the distance between batches. |
Zhiqiang Pang, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform compound mapping
PerformCmpdMapping(mSetObj = NA, cmpdIDs, org, idType)PerformCmpdMapping(mSetObj = NA, cmpdIDs, org, idType)
mSetObj |
Input name of the created mSet Object |
cmpdIDs |
Input the list of compound IDs |
org |
Input the organism code |
idType |
Input the ID type |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
This function maps the user selected list of compounds to its corresponding KEGG IDs and BioCyc IDs
PerformCurrencyMapping(mSetObj = NA)PerformCurrencyMapping(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj object |
Jasmine Chong, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Classification MCCV, aims to find the best feature subsets using default model parameters
PerformCV.explore(mSetObj, cls.method, rank.method="auroc", lvNum=2, propTraining=2/3)PerformCV.explore(mSetObj, cls.method, rank.method="auroc", lvNum=2, propTraining=2/3)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
cls.method |
Select the classification method, "rf" for random forest classification, "pls" for PLS-DA, and "svm" for support vector machine |
rank.method |
Select the ranking method, "rf" for random forest mean decrease accuracy, "fisher" for Fisher's univariate ranking based on area under the curve "auroc" for univariate ranking based on area under the curve, "tt" for T-test univariate ranking based on area under the curve, "pls" for partial least squares, and "svm" for support vector machine |
lvNum |
Input the number of latent variables to include in the analyis, only for PLS-DA classification |
propTraining |
Input the proportion of samples to use for training |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
MCCV for manually selected features (no additional feature selection)
PerformCV.test(mSetObj, method, lvNum, propTraining=2/3, nRuns=100)PerformCV.test(mSetObj, method, lvNum, propTraining=2/3, nRuns=100)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
method |
Select the classification method, "rf" for random forest classification, "pls" for PLS-DA, and "svm" for support vector machine |
lvNum |
Input the number of latent variables to include in the analyis, only for PLS-DA classification |
propTraining |
Input the proportion of samples to use for training, by default it is 2/3 |
nRuns |
Input the number of MCCV runs, by default it is 100 |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
PerformDataInspect is used to plot 2D/3D structure of the MS data
PerformDataInspect( datapath = NULL, rt.range = c(0, 0), mz.range = c(0, 0), dimension = "3D", res = 100 )PerformDataInspect( datapath = NULL, rt.range = c(0, 0), mz.range = c(0, 0), dimension = "3D", res = 100 )
datapath |
data file path |
rt.range |
retention time range, unit is seconds |
mz.range |
m/z range |
dimension |
view dimension, canbe "2D" or "3D" |
res |
resolution number, higher of the number means higher resolution |
Zhiqiang Pang
This function performs the raw data trimming. This function will output an trimmed MSnExp file to memory or hardisk according to the choice of users must provide the data path for 'datapath', and optionally provide other corresponding parameters.
PerformDataTrimming( datapath, mode = "ssm", write = FALSE, mz, mzdiff, rt, rtdiff, rt.idx = 1/15, rmConts = TRUE, plot = TRUE, running.controller = NULL )PerformDataTrimming( datapath, mode = "ssm", write = FALSE, mz, mzdiff, rt, rtdiff, rt.idx = 1/15, rmConts = TRUE, plot = TRUE, running.controller = NULL )
datapath |
Character, the path of the raw MS data files' or folder's path (.mzXML, .CDF and .mzML) for parameters training. |
mode |
Character, mode for data trimming to select the chraracteristic peaks. Default is 'ssm'. Users could select random trimed according to mz value (mz_random) or RT value (rt_random). Besides, specific peaks at certain mz (mz_specific) or RT (rt_specific) could also be extracted. 'none' will not trim the data. |
write |
Logical, if true, will write the trimmed data to the directory 'trimmed' folder in the datapath. The data in memory will be kept. |
mz |
Numeric, mz value(s) for specific selection. Positive values means including (the values indicted) and negative value means excluding/removing. |
mzdiff |
Numeric, the deviation (ppm) of mz value(s). |
rt |
Numeric, rt value for specific selection. Positive values means including and negative value means excluding. |
rtdiff |
Numeric, the deviation (seconds) of rt value(s). |
rt.idx |
Numeric, the relative rt (retention time) range, from 0 to 1. 1 means all retention time will be retained, while 0 means none. Default is 1/15. If default rt.idx produce too few peaks, please consider increasing this value. |
rmConts |
LOgical, whether to exclude/remove the potential contamination for parameters optimization. Default is TRUE. |
plot |
Logical, if TRUE, will plot the chromatogram of the trimmed data. |
running.controller |
The resuming pipeline running controller. Optional. Don't need to define by hand. |
will return an mSet objects with extracted ROI
Zhiqiang Pang [email protected] Jeff Xia [email protected] Mcgill University License: GNU GPL (>= 2)
Given a query, perform compound matching.
PerformDetailMatch(mSetObj = NA, q)PerformDetailMatch(mSetObj = NA, q)
mSetObj |
Input name of the created mSet Object. |
q |
Input the query. |
This function performs DE analysis on individual data using the common matrix, which will be used/compared in later steps of the analysis (according to the p-value). The DE for each feature may be adjusted using the p-value.
PerformEachDEAnal(mSetObj = NA)PerformEachDEAnal(mSetObj = NA)
mSetObj |
Input name of the created mSet Object |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Used for the pathinteg module
PerformGeneMapping(mSetObj = NA, geneIDs, org, idType)PerformGeneMapping(mSetObj = NA, geneIDs, org, idType)
mSetObj |
Input name of the created mSet Object |
geneIDs |
Input the list of gene IDs |
org |
Input the organism code |
idType |
Input the ID type |
This function performs normalization of individuall-uploaded datasets prior to meta-analysis.
PerformIndNormalization(mSetObj = NA, dataName, norm.opt, auto.opt)PerformIndNormalization(mSetObj = NA, dataName, norm.opt, auto.opt)
mSetObj |
Input name of the created mSet Object |
dataName |
Input the name of the individual dataset for normalization. |
norm.opt |
Performs log2 normalization "log", or no normalization "none". |
auto.opt |
Performs auto-scaling of data (1), or no (0). |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
used for integrative analysis as well as general pathways analysis for meta-analysis results
PerformIntegPathwayAnalysis(mSetObj, topo="dc", enrich="hyper", libOpt="integ", integOpt="query")PerformIntegPathwayAnalysis(mSetObj, topo="dc", enrich="hyper", libOpt="integ", integOpt="query")
mSetObj |
Input name of the created mSet Object |
topo |
Select the mode for topology analysis: Degree Centrality ("dc") measures the number of links that connect to a node (representing either a gene or metabolite) within a pathway; Closeness Centrality ("cc") measures the overall distance from a given node to all other nodes in a pathway; Betweenness Centrality ("bc")measures the number of shortest paths from all nodes to all the others that pass through a given node within a pathway. |
enrich |
Method to perform over-representation analysis (ORA) based on either hypergenometrics analysis ("hyper") or Fisher's exact method ("fisher"). |
libOpt |
Select the different modes of pathways, either the gene-metabolite mode ("integ") which allows for joint-analysis and visualization of both significant genes and metabolites or the gene-centric ("genetic") and metabolite-centric mode ("metab") which allows users to identify enriched pathways driven by significant genes or metabolites, respectively. |
integOpt |
integOpt,default is "query" |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
This function performs KO enrichment analysis based on the KO01100 map and saves the .JSON file
PerformKOEnrichAnalysis_KO01100(mSetObj = NA, category, file.nm)PerformKOEnrichAnalysis_KO01100(mSetObj = NA, category, file.nm)
mSetObj |
Input name of the created mSet Object |
category |
Input the option to perform enrichment analysis, "pathway" |
file.nm |
Input name of file to save |
Othman Soufan, Jeff Xia [email protected], [email protected] McGill University, Canada License: GNU GPL (>= 2)
Please note: only return hits in map KO01100
PerformKOEnrichAnalysis_List(mSetObj, file.nm)PerformKOEnrichAnalysis_List(mSetObj, file.nm)
mSetObj |
mSetObj object |
file.nm |
Input the file name |
This function performs DE analysis of individually-uploaded data prior to meta-analysis.
PerformLimmaDE(mSetObj = NA, dataName, p.lvl = 0.1, fc.lvl = 0)PerformLimmaDE(mSetObj = NA, dataName, p.lvl = 0.1, fc.lvl = 0)
mSetObj |
Input name of the created mSet Object |
dataName |
Input the name of the individual dataset for normalization. |
p.lvl |
Numeric, input the p-value (FDR) cutoff. |
fc.lvl |
Numeric, input the fold-change (FC) cutoff. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Utility function for PrepareKeggQueryJson geneIDs is text one string, need to make to vector
PerformMapping_ko01100(inputIDs, type)PerformMapping_ko01100(inputIDs, type)
inputIDs |
Input list of IDs |
type |
Input the type of IDs |
Adapted from the timecourse package by Yu Chuan Tai This method is only applicable for time-series, not for general case two/multiple factor analysis
performMB(mSetObj, topPerc)performMB(mSetObj, topPerc)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
topPerc |
select the cut-off, default is 10 |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
This function is one of three methods to perform meta-analysis. Direct merging of individual data into a mega-dataset results in an analysis of that mega-dataset as if the individual data were derived from the same experiment. This method thereby ignores any inherent bias and heterogeneity between the different data. Because of this, there exists several confounders such as different experimental protocols, technical platforms, and raw data processing procedures that can mask true underlying differences. It is therefore highly suggested that this approach be used only when individual data are very similar (i.e. from the same lab, same platform, without batch effects)."
PerformMetaMerge(mSetObj = NA, BHth = 0.05)PerformMetaMerge(mSetObj = NA, BHth = 0.05)
mSetObj |
Input name of the created mSet Object. |
BHth |
Numeric input to set the significance level. By default it is 0.05. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
This is the main function that performs either the mummichog algorithm, GSEA, or both for peak set enrichment meta-analysis.
PerformMetaPSEA( mSetObj = NA, lib, libVersion, minLib = 3, permNum = 100, metaLevel = "pathway", combine.level = "pvalue", pval.method = "fisher", es.method = "fixed", rank.metric = "mean", mutual.feats = TRUE, pooled_cutoff = 0.05 )PerformMetaPSEA( mSetObj = NA, lib, libVersion, minLib = 3, permNum = 100, metaLevel = "pathway", combine.level = "pvalue", pval.method = "fisher", es.method = "fixed", rank.metric = "mean", mutual.feats = TRUE, pooled_cutoff = 0.05 )
mSetObj |
Input the name of the created mSetObj object. |
lib |
Input the name of the organism library, default is hsa_mfn. |
libVersion |
Input the version of the KEGG pathway libraries ("current" or "old"). |
minLib |
numeric, default is 3 |
permNum |
Numeric, input the number of permutations to perform. Default is 100. |
metaLevel |
Character, input whether the meta-analysis is at the empirical compound ("ec"), compound ("cpd"), or pathway level ("pathway"). |
combine.level |
Character, input whether to combine p-values or pool the peaks. |
pval.method |
Character, input the method to perform p-value combination. |
es.method |
Character, input the method to perform effect-size meta-analysis. |
rank.metric |
Character, input how to calculate pre-ranking metric. "mean" to use the average, "min" to use the lowest score, "max" to use the highest score. |
mutual.feats |
mutual.feats, logical |
pooled_cutoff |
pooled_cutoff, numeric |
Jasmine Chong, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
PerformMirrorPlotting
PerformMirrorPlotting( mSetObj = NA, fragDB_path = NA, peak_idx, sub_idx, interactive = T, ppm, dpi, format, width, height )PerformMirrorPlotting( mSetObj = NA, fragDB_path = NA, peak_idx, sub_idx, interactive = T, ppm, dpi, format, width, height )
mSetObj |
mSetObj |
fragDB_path |
Fragmentation database path |
peak_idx |
peak_idx |
sub_idx |
sub_idx |
interactive |
interactive or not |
ppm |
ppm |
dpi |
dpi |
format |
format |
width |
width |
height |
height |
featurelabel |
featurelabel |
imageNM |
imageNM |
PerformMirrorPlottingWeb
PerformMirrorPlottingWeb( mSetObj = NA, fragDB_path, featurelabel, result_num, sub_idx, ppm, imageNM, dpi, format, width, height )PerformMirrorPlottingWeb( mSetObj = NA, fragDB_path, featurelabel, result_num, sub_idx, ppm, imageNM, dpi, format, width, height )
mSetObj |
mSetObj |
fragDB_path |
Fragmentation database path |
featurelabel |
featurelabel |
result_num |
result_num |
sub_idx |
sub_idx |
ppm |
ppm |
imageNM |
imageNM |
dpi |
dpi |
format |
format |
width |
width |
height |
height |
PerformMS1ResultsFormatting This function is used to format the results from other tools into the generic format of MetaboAnalystR Currently,we are supporting the compatibility for four commonly used open-source tools: MS-DIAL, MZmine, Asari and XCMS online The first parameter file_path should be a valid file of the result. User need to specify the type in the 2nd argument, type. This argument can be msdial, mzmine, asari and xcms Please note, if your original data does not contain meta information, you need to manually add them in the generated "metaboanalyst_input.csv" file The formatted file is 'sample in columns'.
PerformMS1ResultsFormatting(file_path, type, meta_data = NA)PerformMS1ResultsFormatting(file_path, type, meta_data = NA)
file_path |
|
type |
|
meta_data |
this is path to a table containing two columns, the first column is the sample names and second column is the group information |
PerformMS2ResultsFormatting This function is used to format the results from other tools into the generic format of MetaboAnalystR for functional analysis Currently,we are supporting the compatibility for four commonly used open-source tools: MS-FINDER, and SIRIUS The first parameter file_path should be a valid file of the result. User need to specify the type in the 2nd argument, type. This argument can be msfinder, or sirius The 3rd column
PerformMS2ResultsFormatting(file_path, type, MS1_features_list = NA)PerformMS2ResultsFormatting(file_path, type, MS1_features_list = NA)
file_path |
file path of NS2 file |
type |
type, can be msfinder or sirius |
MS1_features_list |
this is feature list used functional analysis |
performMS2searchBatch
performMS2searchBatch( mSetObj = NA, ppm1 = 10, ppm2 = 25, dbpath = "", frgdbpath = "", database = "all", similarity_meth = 0, precMZ = NA, sim_cutoff = 30, ionMode = "positive", unit1 = "ppm", unit2 = "ppm", ncores = 1 )performMS2searchBatch( mSetObj = NA, ppm1 = 10, ppm2 = 25, dbpath = "", frgdbpath = "", database = "all", similarity_meth = 0, precMZ = NA, sim_cutoff = 30, ionMode = "positive", unit1 = "ppm", unit2 = "ppm", ncores = 1 )
mSetObj |
mSetObj |
ppm1 |
ppm value for ms1 |
ppm2 |
ppm value for ms2 |
dbpath |
database path |
frgdbpath |
fragmentation database path |
database |
database option |
similarity_meth |
similarity computing method |
precMZ |
mz of precursor |
sim_cutoff |
filtration cutoff of similarity score. will be enabled soon. |
ionMode |
ion mode, for ESI+, is should be 1. for ESI-, it should be 0 |
unit1 |
ppm or da for ms1 matching |
unit2 |
ppm or da for ms2 |
ncores |
number of cpu cores used to search |
Zhiqiang Pang
performMS2searchSingle
performMS2searchSingle( mSetObj = NA, ppm1 = 10, ppm2 = 25, dbpath = "", frgdbpath = "", database = "all", similarity_meth = 0, precMZ = NA, sim_cutoff = 30, ionMode = "positive", unit1 = "ppm", unit2 = "ppm" )performMS2searchSingle( mSetObj = NA, ppm1 = 10, ppm2 = 25, dbpath = "", frgdbpath = "", database = "all", similarity_meth = 0, precMZ = NA, sim_cutoff = 30, ionMode = "positive", unit1 = "ppm", unit2 = "ppm" )
mSetObj |
mSetObj |
ppm1 |
ppm value for ms1 |
ppm2 |
ppm value for ms2 |
dbpath |
database path |
frgdbpath |
fragmentation database path |
database |
database option |
similarity_meth |
similarity computing method |
precMZ |
mz of precursor |
sim_cutoff |
filtration cutoff of similarity score. will be enabled soon. |
ionMode |
ion mode, for ESI+, is should be 1. for ESI-, it should be 0 |
unit1 |
ppm or da for ms1 matching |
unit2 |
ppm or da for ms2 |
Given a query, performs compound name matching.
PerformMultiMatch(mSetObj = NA, q, lipid)PerformMultiMatch(mSetObj = NA, q, lipid)
mSetObj |
Input name of the created mSet Object. |
q |
Input the query. |
lipid |
lipid, logical |
This function is used to optimize the critical parameters of peak picking and alignment for the following data processing. It utilizes the trimed data and the internal instrument-specific parameters. Parallel computing will be performed. The number of cores user want to use could be specified.
PerformParamsOptimization( mSet, param = NULL, method = "DoE", ncore = 4, running.controller = NULL )PerformParamsOptimization( mSet, param = NULL, method = "DoE", ncore = 4, running.controller = NULL )
mSet |
mSet object, usually generated by 'PerformROIExtraction' or 'PerformDataTrimming' here. |
param |
List, Parameters defined by 'SetPeakParam' function. |
method |
Character, method of parameters optimization, including "DoE' only. Default is "DoE". Other method is under development. |
ncore |
Numeric, CPU threads number used to perform the parallel based optimization. If thers is memory issue, please reduce the 'ncore' used here. For default, 2/3 CPU threads of total will be used. |
running.controller |
The resuming pipeline running controller. Optional. Don't need to define by hand. |
PerformParamsOptimization
will a parameter object can be used for following processing
Zhiqiang Pang [email protected] Jeff Xia [email protected] Mcgill University License: GNU GPL (>= 2)
PerformPeakProfiling
PerformPeakProfiling(mSet, Params, plotSettings, ncore)PerformPeakProfiling(mSet, Params, plotSettings, ncore)
mSet |
mSet |
Params |
Params |
plotSettings |
plotSettings |
ncore |
number of cores |
Perform power profiling of data
PerformPowerProfiling(mSetObj=NA, fdr.lvl, smplSize)PerformPowerProfiling(mSetObj=NA, fdr.lvl, smplSize)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
fdr.lvl |
Specify the false-discovery rate level. |
smplSize |
Specify the maximum sample size, the number must be between 60-1000. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
This is the main function that performs either the mummichog algorithm, GSEA, or both for peak set enrichment analysis.
PerformPSEA(mSetObj=NA, lib, libVersion, minLib, permNum = 100)PerformPSEA(mSetObj=NA, lib, libVersion, minLib, permNum = 100)
mSetObj |
Input the name of the created mSetObj object. |
lib |
Input the name of the organism library, default is hsa_mfn. |
libVersion |
Input the version of the KEGG pathway libraries ("current" or "old"). |
minLib |
Numeric, input the minimum number of metabolites needed to consider the pathway or metabolite set. |
permNum |
Numeric, input the number of permutations to perform. Default is 100. |
Jasmine Chong, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
This function is one of three methods to perform meta-analysis. Here, p-values are combined using either the Fisher's method or the Stouffer's method.
PerformPvalCombination(mSetObj = NA, method = "stouffer", BHth = 0.05)PerformPvalCombination(mSetObj = NA, method = "stouffer", BHth = 0.05)
mSetObj |
Input name of the created mSet Object. |
method |
Method of p-value combination. By default it is "stouffer", else it is "fisher". |
BHth |
Numeric input to set the significance level. By default it is 0.05. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
This function performs the raw data trimming. This function will output an trimmed MSnExp file to memory or hardisk according to the choice of users must provide the data path for 'datapath', and optionally provide other corresponding parameters.
PerformROIExtraction( datapath, mode = "ssm", write = FALSE, mz, mzdiff, rt, rtdiff, rt.idx = 1/15, rmConts = TRUE, plot = TRUE, running.controller = NULL )PerformROIExtraction( datapath, mode = "ssm", write = FALSE, mz, mzdiff, rt, rtdiff, rt.idx = 1/15, rmConts = TRUE, plot = TRUE, running.controller = NULL )
datapath |
Character, the path of the raw MS data files' or folder's path (.mzXML, .CDF and .mzML) for parameters training. |
mode |
Character, mode for data trimming to select the chraracteristic peaks. Default is 'ssm'. Users could select random trimed according to mz value (mz_random) or RT value (rt_random). Besides, specific peaks at certain mz (mz_specific) or RT (rt_specific) could also be extracted. 'none' will not trim the data. |
write |
Logical, if true, will write the trimmed data to the directory 'trimmed' folder in the datapath. The data in memory will be kept. |
mz |
Numeric, mz value(s) for specific selection. Positive values means including (the values indicted) and negative value means excluding/removing. |
mzdiff |
Numeric, the deviation (ppm) of mz value(s). |
rt |
Numeric, rt value for specific selection. Positive values means including and negative value means excluding. |
rtdiff |
Numeric, the deviation (seconds) of rt value(s). |
rt.idx |
Numeric, the relative rt (retention time) range, from 0 to 1. 1 means all retention time will be retained, while 0 means none. Default is 1/15. If default rt.idx produce too few peaks, please consider increasing this value. |
rmConts |
LOgical, whether to exclude/remove the potential contamination for parameters optimization. Default is TRUE. |
plot |
Logical, if TRUE, will plot the chromatogram of the trimmed data. |
running.controller |
The resuming pipeline running controller. Optional. Don't need to define by hand. |
will return an mSet objects with extracted ROI
Zhiqiang Pang [email protected] Jeff Xia [email protected] Mcgill University License: GNU GPL (>= 2)
This function is designed to perform the signal drift correction. Batch effect and signal drift correction will be performed with QC-RLSC method in this function.
PerformSignalDriftCorrection(mSetObj = NA, imgName = NULL)PerformSignalDriftCorrection(mSetObj = NA, imgName = NULL)
mSetObj |
Input name of the created mSet Object |
imgName |
Input the name of the plot to create |
Zhiqiang Pang, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
This function is one of three methods to perform meta-analysis. Here, significant features are selected based on a selected criteria (i.e. an adjusted p-value <0.05 and the same direction of FC) for each dataset. The votes are then calculated for each feature by counting the total of number of times a feature is significant across all included datasets. However, this method is statistically inefficient and should be considered the last resort in situations where other methods to perform meta-analysis cannot be applied.
PerformVoteCounting(mSetObj = NA, BHth = 0.05, minVote)PerformVoteCounting(mSetObj = NA, BHth = 0.05, minVote)
mSetObj |
Input name of the created mSet Object. |
BHth |
Numeric input to set the significance level. By default it is 0.05. |
minVote |
Numeric input to set the minimum vote-count. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Scatter sample trend comparison between all sample of different batches
plot_dist(mSetObj = NA, imgName = "dist", format = "png", width = NA, dpi = 72)plot_dist(mSetObj = NA, imgName = "dist", format = "png", width = NA, dpi = 72)
mSetObj |
mSetObj |
imgName |
imgName |
format |
format |
width |
width |
dpi |
dpi |
Plot results of permutation tests
Plot.Permutation(mSetObj=NA, imgName, format="png", dpi=72)Plot.Permutation(mSetObj=NA, imgName, format="png", dpi=72)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Input a name for the plot |
format |
elect the image format, "png", of "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Scatter sample trend comparison between all sample of different batches
Plot.sampletrend( mSetObj, imgName, format = "png", dpi = 72, width = NA, method )Plot.sampletrend( mSetObj, imgName, format = "png", dpi = 72, width = NA, method )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 600. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
method |
method of correction |
Zhiqiang Pang [email protected], Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot of the accuracy of classification with an increasing number of features.
PlotAccuracy(mSetObj=NA, imgName, format="png", dpi=72)PlotAccuracy(mSetObj=NA, imgName, format="png", dpi=72)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Input a name for the plot |
format |
Select the image format, "png", of "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot ANOVA
PlotANOVA(mSetObj=NA, imgName, format="png", dpi=72, width=NA)PlotANOVA(mSetObj=NA, imgName, format="png", dpi=72, width=NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot Venn diagram of ANOVA results
PlotANOVA2(mSetObj, imgName, format="png", dpi=72, width=NA)PlotANOVA2(mSetObj, imgName, format="png", dpi=72, width=NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot plsda classification performance using different components
PlotASCA.Permutation(mSetObj=NA, imgName, format="png", dpi=72, width=NA)PlotASCA.Permutation(mSetObj=NA, imgName, format="png", dpi=72, width=NA)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot the important variables for each factor
PlotAscaImpVar(mSetObj=NA, imgName, format, dpi, width=NA, type)PlotAscaImpVar(mSetObj=NA, imgName, format, dpi, width=NA, type)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
type |
select model a, b, or ab |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot ASCA interaction plots
PlotASCAInteraction(mSetObj=NA, imgName, format="png", dpi=72, colorBW=FALSE, width=NA)PlotASCAInteraction(mSetObj=NA, imgName, format="png", dpi=72, colorBW=FALSE, width=NA)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
colorBW |
Logical, use black and white (TRUE) or colors (FALSE) |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot score plots of each ASCA model for component 1 against time
PlotASCAModel(mSetObj=NA, imgName, format="png", dpi=72, width=NA, type, colorBW=FALSE)PlotASCAModel(mSetObj=NA, imgName, format="png", dpi=72, width=NA, type, colorBW=FALSE)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Input a name for the ASCA score plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300 |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
type |
select model a or b |
colorBW |
Logical, use black/white coloring (T) or not (F) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot scree plots for each model in ASCA
PlotASCAModelScree(mSetObj, imgName, format="png", dpi=72, width=NA)PlotASCAModelScree(mSetObj, imgName, format="png", dpi=72, width=NA)
mSetObj |
Input name of the created mSet Object. |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot K-means summary PCA plot
PlotClustPCA( mSetObj, imgName, format = "png", dpi = 72, width = NA, colpal = "default", anal = "km", labels = "T" )PlotClustPCA( mSetObj, imgName, format = "png", dpi = 72, width = NA, colpal = "default", anal = "km", labels = "T" )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
colpal |
palete of color |
anal |
analysis type |
labels |
labels to show, default is "T" |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot compound summary change to use dataSet$proc instead of dataSet$orig in case of too many NAs
PlotCmpdSummary( mSetObj = NA, cmpdNm, meta = "NA", meta2 = "NA", count = 0, format = "png", dpi = 72, width = NA )PlotCmpdSummary( mSetObj = NA, cmpdNm, meta = "NA", meta2 = "NA", count = 0, format = "png", dpi = 72, width = NA )
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
cmpdNm |
Input the name of the compound to plot |
meta |
meta is "NA" |
meta2 |
only applicable for multifac module, secondary factor |
count |
img count number |
format |
Input the format of the image to create |
dpi |
Input the dpi of the image to create |
width |
Input the width of the image to create |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plots a bar-graph of selected compound over groups
PlotCmpdView(mSetObj=NA, cmpdNm, format="png", dpi=72, width=NA)PlotCmpdView(mSetObj=NA, cmpdNm, format="png", dpi=72, width=NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
cmpdNm |
Input a name for the compound |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot the compound concentration data compared to the reference concentration range
PlotConcRange(mSetObj, nm, format="png", dpi=72, width=NA)PlotConcRange(mSetObj, nm, format="png", dpi=72, width=NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
nm |
of the input compound |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot correlation
PlotCorr( mSetObj = NA, imgName, searchType = "feature", format = "png", dpi = 72, width = NA )PlotCorr( mSetObj = NA, imgName, searchType = "feature", format = "png", dpi = 72, width = NA )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
searchType |
searchType, default is "feature" |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot correlation heatmap
PlotCorrHeatMap( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, target, cor.method, colors, fix.col, no.clst, fz, unit, corrCutoff = 0 )PlotCorrHeatMap( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, target, cor.method, colors, fix.col, no.clst, fz, unit, corrCutoff = 0 )
mSetObj |
Input name of the created mSet Object. |
imgName |
Input the name of the image to create |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
target |
Input "row" to select features, or "col" to select samples. |
cor.method |
Indicate the correlation method, 'pearson', 'spearman', or 'kendall'. |
colors |
Indicate the colors for the heatmap, "bwm" for default, "gbr" for red/green, "heat" for heat colors, "topo" for topo colors, and "gray" for gray scale. |
fix.col |
Logical, fix colors (TRUE) or not (FALSE). |
no.clst |
Logical, indicate if the correlations should be clustered (TRUE) or not (FALSE). |
corrCutoff |
set corrCutoff McGill University, Canada License: GNU GPL (>= 2) |
viewOpt |
Indicate "overview" to get an overview of the heatmap, and "detail" to get a detailed view of the heatmap. |
Jeff Xia[email protected]
Plot detailed ROC
PlotDetailROC(mSetObj = NA, imgName, thresh, sp, se, dpi = 72, format = "png")PlotDetailROC(mSetObj = NA, imgName, thresh, sp, se, dpi = 72, format = "png")
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Input a name for the plot |
thresh |
Input the threshold |
sp |
Specificity |
se |
Sensitivity |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
format |
Select the image format, "png", or "pdf". |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot EBAM
PlotEBAM.Cmpd(mSetObj=NA, imgName, format, dpi, width)PlotEBAM.Cmpd(mSetObj=NA, imgName, format, dpi, width)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Dot plot of enrichment analysis results.
PlotEnrichDotPlot( mSetObj = NA, enrichType = "ora", imgName, format = "png", dpi = 72, width = NA )PlotEnrichDotPlot( mSetObj = NA, enrichType = "ora", imgName, format = "png", dpi = 72, width = NA )
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
enrichType |
Input whether the enrichment analysis was over-respresentation analysis (ora) or quantitative enrichment analysis (qea). |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Used in higher functions, the color is based on p values
PlotEnrichNet.Overview(folds, pvals, layoutOpt = layout.fruchterman.reingold)PlotEnrichNet.Overview(folds, pvals, layoutOpt = layout.fruchterman.reingold)
folds |
Input fold-change for bar plot |
pvals |
Input p-values for bar plot |
layoutOpt |
Input the layout option, default is set to layout.fruchterman.reingold |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
This function creates a pie-chart for compound classes in the enrichment analysis if the library is based on chemical ontologies.
PlotEnrichPieChart( mSetObj = NA, enrichType, imgName, format = "png", dpi = 72, width = 8, maxClass = 15, colPal = "Set1" )PlotEnrichPieChart( mSetObj = NA, enrichType, imgName, format = "png", dpi = 72, width = 8, maxClass = 15, colPal = "Set1" )
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
enrichType |
enrichType |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Numeric, input the width, the default is 8. |
maxClass |
Numeric, input the maximum number of lipid classes to include in the pie-chart. By default this is set to 15. |
colPal |
Character, input the preferred R Color Brewer palette to be used for the pie chart. By default this is set to "Set1". |
Plot fold change analysis
PlotFC(mSetObj=NA, imgName, format="png", dpi=72, width=NA)PlotFC(mSetObj=NA, imgName, format="png", dpi=72, width=NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Dendogram
PlotHCTree( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, smplDist, clstDist )PlotHCTree( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, smplDist, clstDist )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
smplDist |
Method to calculate sample distance |
clstDist |
Method to calculate clustering distance |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot a heatmap based on results from t-tests/ANOVA, VIP or randomforest
PlotHeatMap( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, dataOpt, scaleOpt, smplDist, clstDist, palette, fzCol, fzRow, fzAnno, annoPer, unitCol, unitRow, rowV = T, colV = T, var.inx = NULL, border = T, grp.ave = F, show.legend = T, show.annot.legend = T, showColnm = T, showRownm = T, maxFeature = 2000 )PlotHeatMap( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, dataOpt, scaleOpt, smplDist, clstDist, palette, fzCol, fzRow, fzAnno, annoPer, unitCol, unitRow, rowV = T, colV = T, var.inx = NULL, border = T, grp.ave = F, show.legend = T, show.annot.legend = T, showColnm = T, showRownm = T, maxFeature = 2000 )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
dataOpt |
Set data options |
scaleOpt |
Set the image scale |
smplDist |
Input the sample distance method |
clstDist |
Input the clustering distance method |
palette |
Input color palette choice |
rowV |
Default is set to T |
colV |
Default is set to T |
var.inx |
Default is set to NA |
border |
Indicate whether or not to show cell-borders, default is set to T |
grp.ave |
Logical, default is set to F |
viewOpt |
Set heatmap options, default is set to "detail" |
metadata |
metadata |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot heatmap visualization for time-series data
PlotHeatMap2( mSetObj = NA, imgName, dataOpt = "norm", scaleOpt = "row", format = "png", dpi = 72, width = NA, smplDist = "pearson", clstDist = "average", colorGradient = "npj", fzCol, fzRow, fzAnno, annoPer, unitCol, unitRow, rankingMethod = "mean", topFeature = 2000, useTopFeature = F, drawBorder = T, show.legend = T, show.annot.legend = T, showColnm = T, showRownm = F, maxFeature = 2000 )PlotHeatMap2( mSetObj = NA, imgName, dataOpt = "norm", scaleOpt = "row", format = "png", dpi = 72, width = NA, smplDist = "pearson", clstDist = "average", colorGradient = "npj", fzCol, fzRow, fzAnno, annoPer, unitCol, unitRow, rankingMethod = "mean", topFeature = 2000, useTopFeature = F, drawBorder = T, show.legend = T, show.annot.legend = T, showColnm = T, showRownm = F, maxFeature = 2000 )
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Input a name for the plot |
dataOpt |
dataOpt, default is "norm" |
scaleOpt |
scaleOpt |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
smplDist |
Select distance measure: euclidean, pearson, or minkowski |
clstDist |
Select clustering algorithm: ward, average, complete, single |
colorGradient |
Select heatmap colors: bwm, gray |
rankingMethod |
rankingMethod |
topFeature |
topFeature |
useTopFeature |
Use significant features only: F or T (default false) |
drawBorder |
Show cell borders: F or T (default F) |
viewOpt |
Select overview or detailed view: overview or detail |
includeRowNames |
includeRowNames, logical |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot the important variables of single biomarker model ranked by order of importance
PlotImpBiomarkers(mSetObj=NA, imgName, format="png", dpi=72, mdl.inx, measure = "freq", feat.num = 15)PlotImpBiomarkers(mSetObj=NA, imgName, format="png", dpi=72, mdl.inx, measure = "freq", feat.num = 15)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Input a name for the plot |
format |
elect the image format, "png", of "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
mdl.inx |
Model index, -1 selects the model with the best AUC, input 1-6 to view the important features of one of the top six models |
measure |
Choose to rank features by the frequency of being selected "freq", or the mean importance measure "mean" |
feat.num |
Input the number of features to include in the plot, by default it is 15. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot PLS important variables, BHan: added bgcolor parameter for B/W color
PlotImpVar(mSetObj = NA, imp.vec, xlbl, feat.num = 15, color.BW = FALSE)PlotImpVar(mSetObj = NA, imp.vec, xlbl, feat.num = 15, color.BW = FALSE)
mSetObj |
Input name of the created mSet Object |
imp.vec |
Input the vector of important variables |
xlbl |
Input the x-label |
feat.num |
Numeric, set the feature numbers, default is set to 15 |
color.BW |
Use black-white for plot (T) or colors (F) |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot PLS important variables, BHan: added bgcolor parameter for B/W color
PlotImpVarMeta( mSetObj = NA, imp.vec, xlbl, feat.num = 15, color.BW = FALSE, type = "meta" )PlotImpVarMeta( mSetObj = NA, imp.vec, xlbl, feat.num = 15, color.BW = FALSE, type = "meta" )
mSetObj |
Input name of the created mSet Object |
imp.vec |
Input the vector of important variables |
xlbl |
Input the x-label |
feat.num |
Numeric, set the feature numbers, default is set to 15 |
color.BW |
Use black-white for plot (T) or colors (F) |
type |
type, default is "type" |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot an igraph object and return the node information (position and labels) Used in a higher function
PlotInmexGraph( mSetObj, pathName, g, width = NA, height = NA, bg.color = NULL, line.color = NULL, format = "png", dpi = NULL )PlotInmexGraph( mSetObj, pathName, g, width = NA, height = NA, bg.color = NULL, line.color = NULL, format = "png", dpi = NULL )
mSetObj |
Input name of the created mSet Object |
pathName |
Input the pathway name |
g |
Input the graph |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
height |
Input the height of the graph to create |
bg.color |
Set the background color, default is set to NULL |
line.color |
Set the line color, default is set to NULL |
format |
image format |
dpi |
dpi of the image |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Only update the background info for matched node
PlotInmexPath( mSetObj = NA, pathName, width = NA, height = NA, format = "png", dpi = NULL )PlotInmexPath( mSetObj = NA, pathName, width = NA, height = NA, format = "png", dpi = NULL )
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
pathName |
Input the Name of the pathway to plot. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
height |
Input the height of the image to create. |
format |
format of the image |
dpi |
dpi, dpi of the image |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot KEGG pathway graph
PlotKEGGPath( mSetObj = NA, pathName, width = NA, height = NA, format = "png", dpi = NULL )PlotKEGGPath( mSetObj = NA, pathName, width = NA, height = NA, format = "png", dpi = NULL )
mSetObj |
Input name of the created mSet Object |
pathName |
Input the name of the selected pathway |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
height |
Input the height of the created plot. |
format |
format of the image. |
dpi |
dpi of the image. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot K-means analysis
PlotKmeans( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, colpal = "default", facet = FALSE )PlotKmeans( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, colpal = "default", facet = FALSE )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
colpal |
Character, input "default" to use the default ggplot color scheme or "colblind" to use the color-blind friendly palette. |
facet |
logical, TRUE to plot in multiple facets |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot MB Time Profile
PlotMBTimeProfile( mSetObj = NA, cmpdNm, version, format = "png", dpi = 72, width = NA )PlotMBTimeProfile( mSetObj = NA, cmpdNm, version, format = "png", dpi = 72, width = NA )
mSetObj |
Input name of the created mSet Object |
cmpdNm |
Input the name of the compound |
version |
image mark |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot correlation coefficients between metadata
PlotMetaCorrHeatmap( mSetObj = NA, cor.opt = "pearson", imgName, format = "png", dpi = 96, width = NA )PlotMetaCorrHeatmap( mSetObj = NA, cor.opt = "pearson", imgName, format = "png", dpi = 96, width = NA )
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
cor.opt |
Meethod for computing correlation coefficient |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot a heatmap showing clustering patterns among the metadata
PlotMetaHeatmap( mSetObj = NA, clustSelOpt = "both", smplDist = "pearson", clstDist = "average", colorGradient = "bwm", includeRowNames = T, imgName, format = "png", dpi = 96, width = NA )PlotMetaHeatmap( mSetObj = NA, clustSelOpt = "both", smplDist = "pearson", clstDist = "average", colorGradient = "bwm", includeRowNames = T, imgName, format = "png", dpi = 96, width = NA )
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
viewOpt |
high-level summary or plotting the names inside cell |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot KEGG pathway
PlotMetpaPath( mSetObj = NA, pathName, width = NA, height = NA, format = "png", dpi = NULL )PlotMetpaPath( mSetObj = NA, pathName, width = NA, height = NA, format = "png", dpi = NULL )
mSetObj |
Input name of the created mSet Object |
pathName |
Input the name of the selected KEGG pathway |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
height |
height value for the image. |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
plotMirror
plotMirror( mSetObj = NA, featureidx = 1, precMZ, ppm, imageNM = "", dpi = 300, format = "png", width = 8, height = 8, cutoff_relative = 5 )plotMirror( mSetObj = NA, featureidx = 1, precMZ, ppm, imageNM = "", dpi = 300, format = "png", width = 8, height = 8, cutoff_relative = 5 )
mSetObj |
mSetObj |
featureidx |
index of feature |
precMZ |
mz of precursor |
ppm |
ppm for ms2 fragment matching mz error |
imageNM |
image name |
dpi |
dpi of images |
format |
format of images |
width |
width of images |
height |
height of images |
cutoff_relative |
cutoff of relative intensity to filter out |
Zhiqiang Pang
Barplot height is enrichment fold change color is based on p values, used in higher functions
PlotMSEA.Overview(folds, pvals)PlotMSEA.Overview(folds, pvals)
folds |
Input the fold-change values |
pvals |
Input the p-values |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
plotMSfeature is used to plot MS feature stats for different groups
plotMSfeature(FeatureNM, format = "png", dpi = 72, width = NA)plotMSfeature(FeatureNM, format = "png", dpi = 72, width = NA)
FeatureNM |
FeatureNM |
format |
format |
dpi |
dpi |
width |
width |
Zhiqiang Pang
Plot compound summary for multi-linear regression tool
PlotMultiFacCmpdSummary( mSetObj = NA, cmpdNm, meta, meta2, version, format = "png", dpi = 72, width = NA )PlotMultiFacCmpdSummary( mSetObj = NA, cmpdNm, meta, meta2, version, format = "png", dpi = 72, width = NA )
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
cmpdNm |
Input the name of the compound to plot |
meta |
Input the metadata to visualize |
version |
version |
format |
Input the format of the image to create |
dpi |
Input the dpi of the image to create |
width |
Input the width of the image to create |
Jessica Ewald[email protected] McGill University, Canada License: GPL-3 License
For each plot, the top is a box plot, bottom is a density plot
PlotNormSummary(mSetObj, imgName, format, dpi, width)PlotNormSummary(mSetObj, imgName, format, dpi, width)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia [email protected], Jasmine Chong McGill University, Canada
PlotOPLS.Imp OPLS VIP plotting function
PlotOPLS.Imp( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, type = "vip", feat.nm = "tscore", feat.num = 15, color.BW = FALSE )PlotOPLS.Imp( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, type = "vip", feat.nm = "tscore", feat.num = 15, color.BW = FALSE )
mSetObj |
mSetObj objects generated from last step |
imgName |
image name |
format |
image format, can be "png", "jpg", "tiff", "pdf" and "svg" |
dpi |
numeric, dpi number |
width |
numeric, width number |
type |
analysis type, can be "vip" only |
feat.nm |
feature name, should be "tscore" for now |
feat.num |
feature number |
color.BW |
color information |
Plot OPLS
PlotOPLS.MDL(mSetObj = NA, imgName, format = "png", dpi = 72, width = NA)PlotOPLS.MDL(mSetObj = NA, imgName, format = "png", dpi = 72, width = NA)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Orthogonal PLS-DA (from ropls) perform permutation, using training classification accuracy as indicator, for two or multi-groups
PlotOPLS.Permutation( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA )PlotOPLS.Permutation( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Orthogonal PLS-DA (from ropls) S-plot for important features from OPLS-DA
PlotOPLS.Splot( mSetObj = NA, imgName, plotType = "all", format = "png", dpi = 72, width = NA )PlotOPLS.Splot( mSetObj = NA, imgName, plotType = "all", format = "png", dpi = 72, width = NA )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
plotType |
plotType for the image, can be "all" or "custom" |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Orthogonal PLS-DA (from ropls) score plot
PlotOPLS2DScore( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, inx1, inx2, reg = 0.95, show = 1, grey.scale = 0, cex.opt = "na" )PlotOPLS2DScore( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, inx1, inx2, reg = 0.95, show = 1, grey.scale = 0, cex.opt = "na" )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
inx1 |
Numeric, indicate the number of the principal component for the x-axis of the loading plot. |
inx2 |
Numeric, indicate the number of the principal component for the y-axis of the loading plot. |
reg |
Numeric |
show |
Show variable labels, 1 or O |
grey.scale |
Numeric, indicate grey-scale, 0 for no, and 1 for yes |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot over-representation analysis (ORA)
PlotORA(mSetObj=NA, imgName, imgOpt, format="png", dpi=72, width=NA)PlotORA(mSetObj=NA, imgName, imgOpt, format="png", dpi=72, width=NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Input a name for the plot |
imgOpt |
"net" |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
x axis is the pathway impact factor y axis is the p value (from ORA or globaltest) return the circle information
PlotPathSummary( mSetObj = NA, show.grid, imgName, format = "png", dpi = 72, width = NA, xlim = NA, ylim = NA )PlotPathSummary( mSetObj = NA, show.grid, imgName, format = "png", dpi = 72, width = NA, xlim = NA, ylim = NA )
mSetObj |
Input name of the created mSet Object |
show.grid |
logical, show grid or not |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. |
xlim |
limit of x axis |
ylim |
limit of y axis |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Function to create summary plot of MS Peaks to Paths meta-analysis at the pathway level. This function creates a summary plot of the MS Peaks to Paths meta-analysis at the pathway level. The plot can either be a heatmap or a network, both of which can be made interactive. NETWORK: The size of the nodes in the network correspond to the number of studies in which that pathway was significant. The color of the nodes correspond to the meta-p-value for each pathway, with (default coloring) red being the most significant and yellow the least.
PlotPathwayMetaAnalysis( mSetObj = NA, imgName, plotType = "heatmap", heatmap_colorType = "brewer", heatmap_palette = "RdYlBu", heatmap_interactive = FALSE, heatmap_square = TRUE, heatmap_allPaths = TRUE, heatmap_npaths = 25, heatmap_vertical = TRUE, heatmap_fontSize = 9, pvalCutoff = 0.05, overlap = 0.25, networkType = "static", layout = "kk", net_palette = "YlOrRd", netTextSize = 2.5, netPlotSize = 7.5, bubble_interactive = FALSE, bubbleMaxPaths = 15, bubble_colorType = "brewer", bubble_palette = "RdBu", bubbleFontSize = 9, bubblePlotSize = 7, format = "png", width = 7, height = 5, dpi = 300 )PlotPathwayMetaAnalysis( mSetObj = NA, imgName, plotType = "heatmap", heatmap_colorType = "brewer", heatmap_palette = "RdYlBu", heatmap_interactive = FALSE, heatmap_square = TRUE, heatmap_allPaths = TRUE, heatmap_npaths = 25, heatmap_vertical = TRUE, heatmap_fontSize = 9, pvalCutoff = 0.05, overlap = 0.25, networkType = "static", layout = "kk", net_palette = "YlOrRd", netTextSize = 2.5, netPlotSize = 7.5, bubble_interactive = FALSE, bubbleMaxPaths = 15, bubble_colorType = "brewer", bubble_palette = "RdBu", bubbleFontSize = 9, bubblePlotSize = 7, format = "png", width = 7, height = 5, dpi = 300 )
mSetObj |
Input the name of the created mSetObj object. |
imgName |
name of image. |
plotType |
Use "heatmap" to create a heatmap summary, "network" to create a network summary, or "bubble" to create a bubble plot summary of the meta-analysis results. |
heatmap_colorType |
Character, "brewer" for R Color Brewer scales or "viridis" for viridis color scales. Used for creating the heatmap color scheme. |
heatmap_palette |
Character, input the preferred color palette according to R Color Brewer or viridis (e.g. "RdBu"). |
heatmap_interactive |
Boolean. FALSE to create a non-interactive plot and TRUE for plotly generated interactive plot. |
heatmap_square |
Boolean. TRUE for the heatmap to be squares versus rectangles (FALSE). |
heatmap_allPaths |
Boolean. TRUE to use all paths when plotting the heatmap. FALSE to use a subset of paths, number defined in npaths. |
heatmap_npaths |
Numeric. The number of pathways to subset the pathway results. |
heatmap_vertical |
Boolean. TRUE, heatmap plot will be vertical. FALSE, heatmap plot will be horizontal. |
heatmap_fontSize |
Numeric, input the preferred font size to be used in the heatmap plot. |
pvalCutoff |
The size of the nodes in the network correspond to the number of studies in which that pathway was significant. This pvalCutoff (Numeric) is thus used to determine whether or not a pathway was found to be significant in each individual study. |
overlap |
Numeric, this number is used to create edges between the nodes. By default it is set to 0.25, meaning that if 2 pathways (nodes) share 25 the same compounds/empirical compounds, they will be connected by a node. |
networkType |
Character, "static" to create a static image or "interactive" to create an interactive network saved as an html in your working directory. |
layout |
Character, layout from ggraph. "kk" for the spring-based algorithm by Kamada and Kawai as default. "drl" for force directed algorithm from the DrL toolbox. "lgl" for Large Graph Layout. "fr" for force-directed of Fruchterman and Reingold. |
net_palette |
Character, input the color code for the nodes in the network. Default is "YlOrRd". Uses the hcl palettes from the grDevices. Use hcl.pals() to view the name of all available palettes. |
netTextSize |
Numeric, input the preferred font size to be used in the network plot. |
netPlotSize |
Numeric, input the preferred dimensions (in inches) of the network to be saved. |
bubble_interactive |
logical |
bubbleMaxPaths |
maximum number of pathways |
bubble_colorType |
Character, "brewer" for R Color Brewer scales or "viridis" for viridis color scales. Used for creating the bubble plot color scheme. |
bubble_palette |
Character, use two/three colors max if using R ColorBrewer palettes for pleasing looking plots. |
bubbleFontSize |
font size of the bubble plot |
bubblePlotSize |
plot size of the bubble plot |
format |
format of the image |
width |
width of the image |
height |
height of the image |
dpi |
dpi of the image |
Jasmine Chong, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Scatter plot colored by different batches
PlotPCA.overview( mSetObj, imgName, format = "png", dpi = 72, width = NA, method )PlotPCA.overview( mSetObj, imgName, format = "png", dpi = 72, width = NA, method )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 600. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
method |
method of correction |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Rotate PCA analysis
PlotPCA2DScore(mSetObj=NA, imgName, format="png", dpi=72, width=NA, pcx, pcy, reg = 0.95, show=1, grey.scale = 0)PlotPCA2DScore(mSetObj=NA, imgName, format="png", dpi=72, width=NA, pcx, pcy, reg = 0.95, show=1, grey.scale = 0)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
pcx |
Specify the principal component on the x-axis |
pcy |
Specify the principal component on the y-axis |
reg |
Numeric, input a number between 0 and 1, 0.95 will display the 95 percent confidence regions, and 0 will not. |
show |
Display sample names, 1 = show names, 0 = do not show names. |
grey.scale |
Use grey-scale colors, 1 = grey-scale, 0 = not grey-scale. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
PlotPCA3DLoading
PlotPCA3DLoading(mSetObj = NA, imgName, format = "json", inx1, inx2, inx3)PlotPCA3DLoading(mSetObj = NA, imgName, format = "json", inx1, inx2, inx3)
mSetObj |
mSetObj |
imgName |
imgName |
format |
format |
inx1 |
inx1 |
inx2 |
inx2 |
inx3 |
inx3 |
Rotate PCA analysis
PlotPCA3DScore(mSetObj=NA, imgName, format="json", inx1, inx2, inx3)PlotPCA3DScore(mSetObj=NA, imgName, format="json", inx1, inx2, inx3)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
inx1 |
Numeric, indicate the number of the principal component for the x-axis of the loading plot. |
inx2 |
Numeric, indicate the number of the principal component for the y-axis of the loading plot. |
inx3 |
Numeric, indicate the number of the principal component for the z-axis of the loading plot. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
This function creates both a static 3D PCA score plot as well as an interactive 3D PCA score plot using the plotly R package. The 3D PCA score plot is stored in the mSetObj (mSetObj$imgSet$pca.3d). To view the plot, if your mSetObj is named mSet, type "mSet$imgSet$pca.3d" inro your R console, and the 3D plot will appear.
PlotPCA3DScoreImg(mSetObj=NA, imgName, format="png", dpi=72, width=NA, inx1, inx2, inx3, angl)PlotPCA3DScoreImg(mSetObj=NA, imgName, format="png", dpi=72, width=NA, inx1, inx2, inx3, angl)
mSetObj |
Input name of the created mSet Object. |
imgName |
Input a name for the plot. |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
inx1 |
Numeric, indicate the number of the principal component for the x-axis of the loading plot. |
inx2 |
Numeric, indicate the number of the principal component for the y-axis of the loading plot. |
inx3 |
Numeric, indicate the number of the principal component for the z-axis of the loading plot. |
angl |
Input the angle |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Rotate PCA analysis
PlotPCABiplot(mSetObj=NA, imgName, format="png", dpi=72, width=NA, inx1, inx2)PlotPCABiplot(mSetObj=NA, imgName, format="png", dpi=72, width=NA, inx1, inx2)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
inx1 |
Numeric, indicate the number of the principal component for the x-axis of the loading plot. |
inx2 |
Numeric, indicate the number of the principal component for the y-axis of the loading plot. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Rotate PCA analysis
PlotPCALoading(mSetObj=NA, imgName, format="png", dpi=72, width=NA, inx1, inx2)PlotPCALoading(mSetObj=NA, imgName, format="png", dpi=72, width=NA, inx1, inx2)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
inx1 |
Numeric, indicate the number of the principal component for the x-axis of the loading plot. |
inx2 |
Numeric, indicate the number of the principal component for the y-axis of the loading plot. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Rotate PCA analysis
PlotPCAPairSummary(mSetObj=NA, imgName, format="png", dpi=72, width=NA, pc.num)PlotPCAPairSummary(mSetObj=NA, imgName, format="png", dpi=72, width=NA, pc.num)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
pc.num |
Numeric, input a number to indicate the number of principal components to display in the pairwise score plot. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Rotate PCA analysis
PlotPCAScree(mSetObj=NA, imgName, format="png", dpi=72, width=NA, scree.num)PlotPCAScree(mSetObj=NA, imgName, format="png", dpi=72, width=NA, scree.num)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
scree.num |
Numeric, input a number to indicate the number of principal components to display in the scree plot. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plots either the original mummichog or GSEA results.
PlotPeaks2Paths( mSetObj = NA, imgName = "", format = "png", dpi = 72, width = 9, labels = "default", num_annot = 5, interactive = F )PlotPeaks2Paths( mSetObj = NA, imgName = "", format = "png", dpi = 72, width = 9, labels = "default", num_annot = 5, interactive = F )
mSetObj |
Input the name of the created mSetObj object |
imgName |
Input a name for the plot |
format |
Character, input the format of the image to create. |
dpi |
Numeric, input the dpi of the image to create. |
width |
Numeric, input the width of the image to create. |
labels |
Character, indicate if the plot should be labeled. By default it is set to "default", and the 5 top-ranked pathways per each algorithm will be plotted. Users can adjust the number of pathways to be annotated per pathway using the "num_annot" parameter. |
num_annot |
number of annotations for top plotting |
Jasmine Chong, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot plsda classification performance using different components
PlotPLS.Classification( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA )PlotPLS.Classification( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot PLS important features, BHan: added bgcolor parameter for B/W color
PlotPLS.Imp( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, type, feat.nm, feat.num, color.BW = FALSE )PlotPLS.Imp( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, type, feat.nm, feat.num, color.BW = FALSE )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
type |
Indicate the type variables of importance to use, "vip" to use VIp scores, or "type" for coefficients |
feat.nm |
Feature name |
feat.num |
Feature numbers |
color.BW |
Logical, true to use black and white, or false to not |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot plsda classification performance using different components
PlotPLS.Permutation( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA )PlotPLS.Permutation( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot PLS score plot
PlotPLS2DScore( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, inx1, inx2, reg = 0.95, show = 1, grey.scale = 0, cex.opt = "na" )PlotPLS2DScore( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, inx1, inx2, reg = 0.95, show = 1, grey.scale = 0, cex.opt = "na" )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
inx1 |
Numeric, indicate the number of the principal component for the x-axis of the loading plot. |
inx2 |
Numeric, indicate the number of the principal component for the y-axis of the loading plot. |
reg |
Numeric, default is 0.95 |
show |
Show labels, 1 or 0 |
grey.scale |
Numeric, use a grey scale (0) or not (1) |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
PlotPLS3DLoading
PlotPLS3DLoading(mSetObj = NA, imgName, format = "json", inx1, inx2, inx3)PlotPLS3DLoading(mSetObj = NA, imgName, format = "json", inx1, inx2, inx3)
mSetObj |
mSetObj |
imgName |
imgName |
format |
format |
inx1 |
inx1 |
inx2 |
inx2 |
inx3 |
inx3 |
Plot PLS 3D score plot
PlotPLS3DScore(mSetObj = NA, imgName, format = "json", inx1, inx2, inx3)PlotPLS3DScore(mSetObj = NA, imgName, format = "json", inx1, inx2, inx3)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
inx1 |
Numeric, indicate the number of the principal component for the x-axis of the loading plot. |
inx2 |
Numeric, indicate the number of the principal component for the y-axis of the loading plot. |
inx3 |
Numeric, indicate the number of the principal component for the z-axis of the loading plot. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
This function creates two 3D PLS-DA score plots, the first is static for Analysis Report purposes, as well as an interactive 3D plot using the plotly R package. The 3D score plot is saved in the created mSetObj (mSetObj$imgSet$plsda.3d). To view the score plot, if the name of your mSetObj is mSet, enter "mSet$imgSet$plsda.3d" to view the interactive score plot.
PlotPLS3DScoreImg( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, inx1, inx2, inx3, angl )PlotPLS3DScoreImg( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, inx1, inx2, inx3, angl )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
inx1 |
Numeric, indicate the number of the principal component for the x-axis of the loading plot. |
inx2 |
Numeric, indicate the number of the principal component for the y-axis of the loading plot. |
inx3 |
Numeric, indicate the number of the principal component for the z-axis of the loading plot. |
angl |
Input the angle |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot PLS loading plot, also set the loading matrix for display
PlotPLSLoading( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, inx1, inx2 )PlotPLSLoading( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, inx1, inx2 )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
inx1 |
Numeric, indicate the number of the principal component for the x-axis of the loading plot. |
inx2 |
Numeric, indicate the number of the principal component for the y-axis of the loading plot. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot PLS pairwise summary
PlotPLSPairSummary( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, pc.num )PlotPLSPairSummary( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, pc.num )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
pc.num |
Numeric, indicate the number of principal components |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot power profile, specifying FDR level and sample size. It will return the image as well as the predicted power at various sample sizes.
PlotPowerProfile(mSetObj=NA, fdr.lvl, smplSize, imgName, format, dpi, width)PlotPowerProfile(mSetObj=NA, fdr.lvl, smplSize, imgName, format, dpi, width)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
fdr.lvl |
Specify the false-discovery rate level. |
smplSize |
Specify the maximum sample size, the number must be between 60-1000. |
imgName |
Specify the name to save the image as. |
format |
Specify the format of the image to save it as, either "png" or "pdf". |
dpi |
Specify the dots-per-inch (dpi). By default it is 72, for publications the recommended dpi is 300. |
width |
Specify the width of the image. NA specifies a width of 9, 0 specifies a width of 7, otherwise input a chosen width. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Create plot for power statistics
PlotPowerStat(mSetObj, imgName, format="png", dpi=72, width=NA)PlotPowerStat(mSetObj, imgName, format="png", dpi=72, width=NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Specify the name to save the image as. |
format |
Specify the format of the image to save it as, either "png" or "pdf" |
dpi |
Specify the dots-per-inch (dpi). By default it is 72, for publications the recommended dpi is 300. |
width |
Specify the width of the image. NA or 0 specifies a width of 10, otherwise input a chosen width. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot of predicted class probabilities. On the x-axis is the proability, and the y-axis is the index of each predicted sample based on the probility. The samples are turned into separations at the x-axis. This plot can be created for multivariate ROC curve analysis using SVM, PLS, and RandomForest. Please note that sometimes, not all samples will be tested, instead they will be plotted at the 0.5 neutral line.
PlotProbView(mSetObj=NA, imgName, format="png", dpi=72, mdl.inx, show, showPred)PlotProbView(mSetObj=NA, imgName, format="png", dpi=72, mdl.inx, show, showPred)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Input a name for the plot |
format |
Select the image format, "png", of "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
mdl.inx |
Model index, 0 means to compare all models, -1 means to use the best model, input 1-6 to plot a ROC curve for one of the top six models |
show |
1 or 0, if 1, label samples classified to the wrong groups |
showPred |
Show predicted samples |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot of predicted class probabilities. On the x-axis is the proability, and the y-axis is the index of each predicted sample based on the probility. The samples are turned into separations at the x-axis. This plot can be created for multivariate ROC curve analysis using SVM, PLS, and RandomForest. Please note that sometimes, not all samples will be tested, instead they will be plotted at the 0.5 neutral line.
PlotProbViewTest(mSetObj=NA, imgName, format="png", dpi=72, mdl.inx, show, showPred)PlotProbViewTest(mSetObj=NA, imgName, format="png", dpi=72, mdl.inx, show, showPred)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Input a name for the plot |
format |
Select the image format, "png", of "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
mdl.inx |
Model index, 0 means to compare all models, -1 means to use the best model, input 1-6 to plot a ROC curve for one of the top six models |
show |
1 or 0, if 1, label samples classified to the wrong groups |
showPred |
Show predicted samples |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Colored by experimental conditions, used in higher function
plotProfile(mSetObj = NA, varName)plotProfile(mSetObj = NA, varName)
mSetObj |
Input name of the created mSet Object |
varName |
Input the name of the variable |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plots both the original mummichog and the GSEA results by combining p-values using the Fisher's method (sumlog).
PlotPSEAIntegPaths( mSetObj = NA, imgName = "", format = "png", dpi = 72, width = 9, labels = "default", labels.x = 5, labels.y = 5, scale.axis = TRUE, interactive = F )PlotPSEAIntegPaths( mSetObj = NA, imgName = "", format = "png", dpi = 72, width = 9, labels = "default", labels.x = 5, labels.y = 5, scale.axis = TRUE, interactive = F )
mSetObj |
Input the name of the created mSetObj object |
imgName |
Input a name for the plot |
format |
Character, input the format of the image to create. |
dpi |
Numeric, input the dpi of the image to create. |
width |
Numeric, input the width of the image to create. |
labels |
Character, indicate if the plot should be labeled. By default it is set to "default", and the 5 top-ranked pathways per each algorithm will be plotted. Users can adjust the number of pathways to be annotated per pathway using the "labels.x" and "labels.y" parameters. Users can set this to "none" for no annotations, or "all" to annotate all pathways. |
labels.x |
Numeric, indicate the number of top-ranked pathways using the fGSEA algorithm to annotate on the plot. |
labels.y |
Numeric, indicate the number of top-ranked pathways using the original mummichog algorithm to annotate on the plot. |
scale.axis |
logical, TRUE to scale |
Jasmine Chong, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
View individual compounds related to a given metabolite set Functions for various plots for enrichment analysis
PlotQEA.MetSet(mSetObj=NA, setNM, format="png", dpi=72, width=NA)PlotQEA.MetSet(mSetObj=NA, setNM, format="png", dpi=72, width=NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
setNM |
Input the name of the metabolite set |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot QEA overview
PlotQEA.Overview(mSetObj=NA, imgName, imgOpt, format="png", dpi=72, width=NA)PlotQEA.Overview(mSetObj=NA, imgName, imgOpt, format="png", dpi=72, width=NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Input a name for the plot |
imgOpt |
"net" |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Random Forest plot
PlotRF.Classify(mSetObj, imgName, format, dpi, width)PlotRF.Classify(mSetObj, imgName, format, dpi, width)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Random Forest plot
PlotRF.ClassifyMeta(mSetObj, imgName, format, dpi, width, type)PlotRF.ClassifyMeta(mSetObj, imgName, format, dpi, width, type)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
type |
plotting type, default is "meta". |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Random Forest plot of outliers
PlotRF.Outlier(mSetObj=NA, imgName, format="png", dpi=72, width=NA)PlotRF.Outlier(mSetObj=NA, imgName, format="png", dpi=72, width=NA)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Random Forest plot of variable importance ranked by MeanDecreaseAccuracy
PlotRF.VIP(mSetObj=NA, imgName, format, dpi, width)PlotRF.VIP(mSetObj=NA, imgName, format, dpi, width)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Random Forest plot of variable importance ranked by MeanDecreaseAccuracy
PlotRF.VIPMeta(mSetObj=NA, imgName, format, dpi, width, type)PlotRF.VIPMeta(mSetObj=NA, imgName, format, dpi, width, type)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
type |
type of image |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Pred and auroc are lists containing predictions and labels from different cross-validations
PlotROC(mSetObj=NA, imgName, format="png", dpi=72, mdl.inx, avg.method, show.conf, show.holdout, focus="fpr", cutoff = 1.0)PlotROC(mSetObj=NA, imgName, format="png", dpi=72, mdl.inx, avg.method, show.conf, show.holdout, focus="fpr", cutoff = 1.0)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Input a name for the plot |
format |
Select the image format, "png", of "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
mdl.inx |
Model index, 0 means to compare all models, input 1-6 to plot a ROC curve for one of the top six models |
avg.method |
Input the method to compute the average ROC curve, either "threshold", "vertical" or "horizontal" |
show.conf |
Logical, if 1, show confidence interval, if 0 do not show |
show.holdout |
Logical, if 1, show the ROC curve for hold-out validation, if 0 do not show |
focus |
"fpr" |
cutoff |
Input the threshold to limit the calculation of the ROC curve, the number must be between 0 and 1. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot ROC for the logistic regression model
PlotROC.LRmodel( mSetObj = NA, imgName, format = "png", dpi = 72, show.conf = FALSE, sp.bin = 0.01 )PlotROC.LRmodel( mSetObj = NA, imgName, format = "png", dpi = 72, show.conf = FALSE, sp.bin = 0.01 )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
show.conf |
Logical, show confidence intervals |
sp.bin |
Numeric, default is set to 0.01. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot the ROC curve of the biomarker model created using a user-selected subset of features. Pred and auroc are lists containing predictions and labels from different cross-validations.
PlotROCTest(mSetObj=NA, imgName, format="png", dpi=72, mdl.inx, avg.method, show.conf, show.holdout, focus="fpr", cutoff = 1.0)PlotROCTest(mSetObj=NA, imgName, format="png", dpi=72, mdl.inx, avg.method, show.conf, show.holdout, focus="fpr", cutoff = 1.0)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Input a name for the plot |
format |
Select the image format, "png", of "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
mdl.inx |
Model index, 0 means to compare all models, input 1-6 to plot a ROC curve for one of the top six models |
avg.method |
Input the method to compute the average ROC curve, either "threshold", "vertical" or "horizontal" |
show.conf |
Logical, if 1, show confidence interval, if 0 do not show |
show.holdout |
Logical, if 1, show the ROC curve for hold-out validation, if 0 do not show |
focus |
"fpr" |
cutoff |
Input the threshold to limit the calculation of the ROC curve, the number must be between 0 and 1. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plots a boxplot of the selected compound's concentration between the groups.
PlotRocUnivBoxPlot( mSetObj, feat.nm, version, format = "png", dpi = 72, isOpt, isQuery )PlotRocUnivBoxPlot( mSetObj, feat.nm, version, format = "png", dpi = 72, isOpt, isQuery )
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
feat.nm |
Input the name of the selected compound. |
version |
version mark for image name |
format |
Select the image format, "png", of "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
isOpt |
logical |
isQuery |
logical |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot recursive SVM classification
PlotRSVM.Classification(mSetObj, imgName, format, dpi, width)PlotRSVM.Classification(mSetObj, imgName, format, dpi, width)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot recursive SVM variables of importance if too many, plot top 15
PlotRSVM.Cmpd(mSetObj=NA, imgName, format="png", dpi=72, width=NA)PlotRSVM.Cmpd(mSetObj=NA, imgName, format="png", dpi=72, width=NA)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot SAM with positive and negative metabolite sets
PlotSAM.Cmpd(mSetObj = NA, imgName, format = "png", dpi = 72, width = NA)PlotSAM.Cmpd(mSetObj = NA, imgName, format = "png", dpi = 72, width = NA)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot SAM Delta Plot (FDR)
PlotSAM.FDR(mSetObj = NA, imgName, format = "png", dpi = 72, width = NA)PlotSAM.FDR(mSetObj = NA, imgName, format = "png", dpi = 72, width = NA)
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
For each plot, the top is a density plot and the bottom is a box plot.
PlotSampleNormSummary(mSetObj=NA, imgName, format="png", dpi=72, width=NA)PlotSampleNormSummary(mSetObj=NA, imgName, format="png", dpi=72, width=NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Input a name for the plot |
format |
Select the image format, "png", of "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia [email protected], Jasmine Chong McGill University, Canada
This function plots a box-plot of the expression pattern of a user-selected feature across the different datasets included in meta-analysis.
PlotSelectedFeature(mSetObj = NA, gene.id, format = "png", dpi = 72)PlotSelectedFeature(mSetObj = NA, gene.id, format = "png", dpi = 72)
mSetObj |
Input name of the created mSet Object. |
gene.id |
Input the name of the selected feature. |
format |
format, in "png" etc. |
dpi |
dpi value for the image. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Supporting function for plotting important variables for each factor note, by control xpd to plot legend outside the plotting area without using layout
PlotSigVar(x, y, xline, yline, title)PlotSigVar(x, y, xline, yline, title)
x |
Input the X variable |
y |
Input the Y variable |
xline |
Input the xline |
yline |
Input the yline |
title |
Input the title |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
plotSingleTIC is used to plot single TIC
plotSingleTIC(filename, imageNumber, format = "png", dpi = 72, width = NA)plotSingleTIC(filename, imageNumber, format = "png", dpi = 72, width = NA)
filename |
filename |
imageNumber |
imageNumber |
format |
format |
dpi |
dpi |
width |
width |
Zhiqiang Pang
Plot SOM map for less than 20 clusters
PlotSOM( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, colpal = "default", facet = TRUE )PlotSOM( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, colpal = "default", facet = TRUE )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
colpal |
Character, input "default" to use the default ggplot color scheme or "colblind" to use the color-blind friendly palette. |
facet |
logical |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Sparse PLS-DA (from mixOmics) score plot
PlotSPLS2DScore( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, inx1, inx2, reg = 0.95, show = 1, grey.scale = 0, cex.opt = "na" )PlotSPLS2DScore( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, inx1, inx2, reg = 0.95, show = 1, grey.scale = 0, cex.opt = "na" )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
inx1 |
Numeric, indicate the number of the principal component for the x-axis of the loading plot. |
inx2 |
Numeric, indicate the number of the principal component for the y-axis of the loading plot. |
reg |
Numeric, between 1 and 0 |
show |
Numeric, 1 or 0 |
grey.scale |
Numeric, use grey-scale, 0 for no, and 1 for yes. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
PlotSPLS3DLoading
PlotSPLS3DLoading(mSetObj = NA, imgName, format = "json", inx1, inx2, inx3)PlotSPLS3DLoading(mSetObj = NA, imgName, format = "json", inx1, inx2, inx3)
mSetObj |
mSetObj |
imgName |
imgName |
format |
format |
inx1 |
inx1 |
inx2 |
inx2 |
inx3 |
inx3 |
Sparse PLS-DA (from mixOmics) 3D score plot
PlotSPLS3DScore( mSetObj = NA, imgName, format = "json", inx1 = 1, inx2 = 2, inx3 = 3 )PlotSPLS3DScore( mSetObj = NA, imgName, format = "json", inx1 = 1, inx2 = 2, inx3 = 3 )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
inx1 |
Numeric, indicate the number of the principal component for the x-axis of the loading plot. |
inx2 |
Numeric, indicate the number of the principal component for the y-axis of the loading plot. |
inx3 |
Numeric, indicate the number of the principal component for the z-axis of the loading plot. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
This function creates two 3D sPLS-DA score plots, the first is static for Analysis Report purposes, as well as an interactive 3D plot using the plotly R package. The 3D score plot is saved in the created mSetObj (mSetObj$imgSet$splsda.3d). To view the score plot, if the name of your mSetObj is mSet, enter "mSet$imgSet$splsda.3d" to view the interactive score plot.
This function creates two 3D sPLS-DA score plots, the first is static for Analysis Report purposes, as well as an interactive 3D plot using the plotly R package. The 3D score plot is saved in the created mSetObj (mSetObj$imgSet$splsda.3d). To view the score plot, if the name of your mSetObj is mSet, enter "mSet$imgSet$splsda.3d" to view the interactive score plot.
PlotSPLS3DScoreImg( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, inx1, inx2, inx3, angl ) PlotSPLS3DScoreImg( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, inx1, inx2, inx3, angl )PlotSPLS3DScoreImg( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, inx1, inx2, inx3, angl ) PlotSPLS3DScoreImg( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, inx1, inx2, inx3, angl )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
inx1 |
Numeric, indicate the number of the principal component for the x-axis of the loading plot. |
inx2 |
Numeric, indicate the number of the principal component for the y-axis of the loading plot. |
inx3 |
Numeric, indicate the number of the principal component for the z-axis of the loading plot. |
angl |
Input the angle |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Sparse PLS-DA (from mixOmics) plot of classification performance using different components
PlotSPLSDA.Classification( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA )PlotSPLSDA.Classification( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Sparse PLS-DA (from mixOmics) loading plot
PlotSPLSLoading( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, inx, viewOpt = "detail" )PlotSPLSLoading( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, inx, viewOpt = "detail" )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
inx |
Input the model index |
viewOpt |
Detailed view "detail" |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Sparse PLS-DA (from mixOmics) pairwise summary plot
PlotSPLSPairSummary( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, pc.num )PlotSPLSPairSummary( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, pc.num )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
pc.num |
Numeric, indicate the number of principle components |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
'@param #same as PlotCorrHeatMap
PlotStaticCorrHeatMap( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, target, cor.method, colors, viewOpt, fix.col, no.clst, corrCutoff = 0 )PlotStaticCorrHeatMap( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, target, cor.method, colors, viewOpt, fix.col, no.clst, corrCutoff = 0 )
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
'@param #same as PlotHeatMap
PlotStaticHeatMap( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, dataOpt, scaleOpt, smplDist, clstDist, palette, fzCol, fzRow, viewOpt = "detail", rowV = T, colV = T, var.inx = NULL, border = T, grp.ave = F, show.legend = T, show.annot.legend = T, includeRowNames = T )PlotStaticHeatMap( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, dataOpt, scaleOpt, smplDist, clstDist, palette, fzCol, fzRow, viewOpt = "detail", rowV = T, colV = T, var.inx = NULL, border = T, grp.ave = F, show.legend = T, show.annot.legend = T, includeRowNames = T )
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
'@param #same as PlotHeatMap2
PlotStaticHeatMap2( mSetObj = NA, imgName, dataOpt = "norm", scaleOpt = "row", format = "png", dpi = 72, width = NA, smplDist = "pearson", clstDist = "average", colorGradient = "bwm", fzCol, fzRow, viewOpt = "overview", rankingMethod = "mean", topFeature = 2000, useTopFeature = F, drawBorder = T, show.legend = T, show.annot.legend = T, includeRowNames = T )PlotStaticHeatMap2( mSetObj = NA, imgName, dataOpt = "norm", scaleOpt = "row", format = "png", dpi = 72, width = NA, smplDist = "pearson", clstDist = "average", colorGradient = "bwm", fzCol, fzRow, viewOpt = "overview", rankingMethod = "mean", topFeature = 2000, useTopFeature = F, drawBorder = T, show.legend = T, show.annot.legend = T, includeRowNames = T )
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot a heatmap showing clustering patterns among the metadata
PlotStaticMetaHeatmap( mSetObj = NA, viewOpt = "detailed", clustSelOpt = "both", smplDist = "pearson", clstDist = "average", colorGradient = "bwm", includeRowNames = T, imgName, format = "png", dpi = 96, width = NA )PlotStaticMetaHeatmap( mSetObj = NA, viewOpt = "detailed", clustSelOpt = "both", smplDist = "pearson", clstDist = "average", colorGradient = "bwm", includeRowNames = T, imgName, format = "png", dpi = 96, width = NA )
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
viewOpt |
high-level summary or plotting the names inside cell |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot a sub heatmap based on results from t-tests/ANOVA, VIP or randomforest
PlotSubHeatMap( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, dataOpt, scaleOpt, smplDist, clstDist, palette, fzCol, fzRow, fzAnno, annoPer, unitCol, unitRow, method.nm, top.num, rowV = T, colV = T, border = T, grp.ave = F, show.legend = T, show.annot.legend = T, showColnm = T, showRownm = T, viewOpt, download = F )PlotSubHeatMap( mSetObj = NA, imgName, format = "png", dpi = 72, width = NA, dataOpt, scaleOpt, smplDist, clstDist, palette, fzCol, fzRow, fzAnno, annoPer, unitCol, unitRow, method.nm, top.num, rowV = T, colV = T, border = T, grp.ave = F, show.legend = T, show.annot.legend = T, showColnm = T, showRownm = T, viewOpt, download = F )
mSetObj |
Input name of the created mSet Object |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
dataOpt |
Set data options |
scaleOpt |
Set the image scale |
smplDist |
Input the sample distance method |
clstDist |
Input the clustering distance method |
palette |
Input color palette choice |
method.nm |
Input the method for sub-heat map |
top.num |
Input the top number |
rowV |
Default is set to T |
colV |
Default is set to T |
border |
Indicate whether or not to show cell-borders, default is set to T |
grp.ave |
Logical, default is set to F |
viewOpt |
Set heatmap options, default is set to "detail" |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot of the accuracy of classification with an increasing number of features.
PlotTestAccuracy(mSetObj=NA, imgName, format="png", dpi=72)PlotTestAccuracy(mSetObj=NA, imgName, format="png", dpi=72)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Input a name for the plot |
format |
Select the image format, "png", of "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Plot t-test
PlotTT(mSetObj=NA, imgName, format="png", dpi=72, width=NA)PlotTT(mSetObj=NA, imgName, format="png", dpi=72, width=NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Input a name for the plot |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
For labelling interesting points, it is defined by the following rules: need to be signficant (sig.inx) and or 2. top 5 p, or 2. top 5 left, or 3. top 5 right.
PlotVolcano(mSetObj=NA, imgName, plotLbl, plotTheme, format="png", dpi=72, width=NA)PlotVolcano(mSetObj=NA, imgName, plotLbl, plotTheme, format="png", dpi=72, width=NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
imgName |
Input a name for the plot |
plotLbl |
Logical, plot labels, 1 for yes and 0 for no. |
plotTheme |
plotTheme, numeric, canbe 0, 1 or 2 |
format |
Select the image format, "png", or "pdf". |
dpi |
Input the dpi. If the image format is "pdf", users need not define the dpi. For "png" images, the default dpi is 72. It is suggested that for high-resolution images, select a dpi of 300. |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
PlotXIC is used to plot both MS XIC/EIC features of different group and samples
PlotXIC(featureNum, format = "png", dpi = 72, width = NA)PlotXIC(featureNum, format = "png", dpi = 72, width = NA)
featureNum |
featureNum |
format |
format |
dpi |
dpi |
width |
width |
Zhiqiang Pang
PLS-DA classification and feature selection
PLSDA.CV( mSetObj = NA, cvOpt = "loo", foldNum = 5, compNum = GetDefaultPLSCVComp(mSetObj), choice = "Q2", segments = 10 )PLSDA.CV( mSetObj = NA, cvOpt = "loo", foldNum = 5, compNum = GetDefaultPLSCVComp(mSetObj), choice = "Q2", segments = 10 )
mSetObj |
Input name of the created mSet Object |
compNum |
GetDefaultPLSCVComp() |
choice |
Input the choice, by default it is Q2 |
methodName |
Logical, by default set to TRUE |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform PLS-DA permutation using training classification accuracy as indicator, for two or multi-groups
PLSDA.Permut(mSetObj = NA, num = 100, type = "accu")PLSDA.Permut(mSetObj = NA, num = 100, type = "accu")
mSetObj |
Input name of the created mSet Object |
num |
Numeric, input the number of permutations |
type |
Type of accuracy, if "accu" indicate prediction accuracy, else "sep" is separation distance |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
PLS analysis using oscorespls
PLSR.Anal(mSetObj = NA, reg = FALSE)PLSR.Anal(mSetObj = NA, reg = FALSE)
mSetObj |
Input name of the created mSet Object |
reg |
Logical |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Get predicted class probability
Predict.class(x.train, y.train, x.test, clsMethod = "pls", lvNum, imp.out = F)Predict.class(x.train, y.train, x.test, clsMethod = "pls", lvNum, imp.out = F)
x.train |
Input the x training samples |
y.train |
Input the y training samples |
x.test |
Input the x testing samples |
clsMethod |
Se the classification method, default is set to pls |
lvNum |
Input the number of levels |
imp.out |
Logical, set to F by default |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Used for the pathinteg module.
PrepareIntegData(mSetObj = NA)PrepareIntegData(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
This function prepares the user's data for the KEGG Global Metabolic Network
PrepareKeggQueryJson(mSetObj = NA)PrepareKeggQueryJson(mSetObj = NA)
mSetObj |
Input name of the created mSet Object |
Othman Soufan, Jeff Xia [email protected], [email protected] McGill University, Canada License: GNU GPL (>= 2)
PrepareMetaPath
PrepareMetaPath( mSetObj = NA, mode = "negative", ppm = 30, version = "v2", pcutoff = 0.05, rt.type = "seconds", dataName, dataName2 )PrepareMetaPath( mSetObj = NA, mode = "negative", ppm = 30, version = "v2", pcutoff = 0.05, rt.type = "seconds", dataName, dataName2 )
mSetObj |
mSetObj |
mode |
ion mode, can be "positive" or "negative" |
ppm |
mass error, default is 30 |
version |
mummichog version, can be "v1" or "v2" |
pcutoff |
p value cut-off, default is 0.05 |
rt.type |
character, retention time type, can be "minutes" or "seconds" |
dataName |
file name 1 with absolute path |
dataName2 |
file name 2 with absolute path or "null" |
Jeff Xia[email protected] Zhiqiang Pang[email protected] McGill University, Canada License: GNU GPL (>= 2)
Function for the network explorer module, prepares user's data for network exploration.
PrepareNetworkData(mSetObj = NA)PrepareNetworkData(mSetObj = NA)
mSetObj |
Input name of the created mSet Object |
Report generation using Sweave Note: most analyses were already performed, only need to embed the results to the right place without rerunning the whole analysis through Sweave. Only some auxilliary info (i.e. time, version etc need to run in R through Sweave
PreparePDFReport(mSetObj = NA, usrName)PreparePDFReport(mSetObj = NA, usrName)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
usrName |
Input the name of the user |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
PreparePeakTable4PSEA
PreparePeakTable4PSEA(mSetObj = NA)PreparePeakTable4PSEA(mSetObj = NA)
mSetObj |
mSet Objective from previous step |
Zhiqiang Pang, Jeff Xia
Function to prepare a report for permutation tests, used in higher functions
PreparePermResult(perm.vec)PreparePermResult(perm.vec)
perm.vec |
Input permutation vector |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Function should always be initialized (new or overwrite previous prenorm object).
PreparePrenormData(mSetObj = NA)PreparePrenormData(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Prepare data for ROC analysis
PrepareROCData(mSetObj = NA)PrepareROCData(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
ROC with CI for AUC
PrepareROCDetails(mSetObj = NA, feat.nm)PrepareROCDetails(mSetObj = NA, feat.nm)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
feat.nm |
Input the feature name |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Prepare data for Upset diagram
PrepareUpsetData(mSetObj = NA, fileNm)PrepareUpsetData(mSetObj = NA, fileNm)
mSetObj |
Input name of the created mSet Object |
fileNm |
json file name to save |
processMSMSupload
processMSMSupload(mSetObj = NA, spectrum)processMSMSupload(mSetObj = NA, spectrum)
mSetObj |
mSetObj |
spectrum |
spectrum for uploading |
Zhiqiang Pang
Ranks features based on various importance measures, return imp.vec which contains the importance measures of unordered features
RankFeatures(x.in, y.in, method, lvNum)RankFeatures(x.in, y.in, method, lvNum)
x.in |
Input the X features |
y.in |
Input the Y features |
method |
Input the method |
lvNum |
Input the number of levels |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Read multiple user uploaded CSV data one by one format: row, col
Read.BatchDataBC(mSetObj = NA, filePath, format, label, missingEstimate)Read.BatchDataBC(mSetObj = NA, filePath, format, label, missingEstimate)
mSetObj |
Input name of the created mSet Object |
filePath |
Input the path to the batch files |
format |
Input the format of the batch files |
label |
Input the label-type of the files |
missingEstimate |
Approach to estimate the missing values |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Read peak data tale. format: row, col
Read.BatchDataTB(mSetObj = NA, filePath, format, missingEstimate)Read.BatchDataTB(mSetObj = NA, filePath, format, missingEstimate)
mSetObj |
Input name of the created mSet Object |
filePath |
Input the path to the batch files |
format |
Input the format of the batch files |
missingEstimate |
Approach to estimate the missing values |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Read an mzTab tab separated file from the passed in file. Adapted from: https://github.com/lifs-tools/rmzTab-m/blob/master/R/MzTabReader.r
Read.mzTab(mSetObj = NA, filename, identifier = "name")Read.mzTab(mSetObj = NA, filename, identifier = "name")
mSetObj |
Input the name of the created mSetObj (see InitDataObjects). |
filename |
The name of the mzTab file to parse. |
identifier |
The identifier to be used when the table is parsed. Use "name" to use the chemical_name, "mass" to use the theoretical_neutral_mass and "sml_id" to use the SML_ID. If the number of missing name and mass entries is greater than 90 then the SML_ID will be used. |
This function reads peak list files and fills the data into a dataSet object. For NMR peak lists, the input should be formatted as two-columns containing numeric values (ppm, int). Further, this function will change ppm to mz, and add a dummy 'rt'. For MS peak data, the lists can be formatted as two-columns (mz, int), in which case the function will add a dummy 'rt', or the lists can be formatted as three-columns (mz, rt, int).
Read.PeakList(mSetObj=NA, foldername)Read.PeakList(mSetObj=NA, foldername)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects). |
foldername |
Name of the folder containing the NMR or MS peak list files to read. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
This function handles reading in CSV or TXT files and filling in the mSet object for mummichog analysis. It makes sure that all necessary columns are present.
Read.PeakListData(mSetObj=NA, filename = NA, meta.anal = FALSE, method = "pvalue")Read.PeakListData(mSetObj=NA, filename = NA, meta.anal = FALSE, method = "pvalue")
mSetObj |
Input the name of the created mSetObj. |
filename |
Input the path name for the CSV/TXT files to read. |
meta.anal |
Logical, TRUE if data will be used for meta-analysis. |
method |
Input the type of statistical scores included in the mummichog input. "pvalue" for p-values, "es" for effect-sizes, and "both" for both p-values and effect-sizes. |
Jasmine Chong, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Read peak data tale. format: row, col
Read.SignalDriftData(mSetObj = NA, filePath, format)Read.SignalDriftData(mSetObj = NA, filePath, format)
mSetObj |
Input name of the created mSet Object |
filePath |
Input the path to the batch files |
format |
Input the format of the batch files |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
This function handles reading in CSV or TXT files and filling in the dataSet object created using "InitDataObjects".
Read.TextData( mSetObj = NA, filePath, format = "rowu", lbl.type = "disc", nmdr = FALSE )Read.TextData( mSetObj = NA, filePath, format = "rowu", lbl.type = "disc", nmdr = FALSE )
mSetObj |
Input the name of the created mSetObj (see InitDataObjects). |
filePath |
Input the path name for the CSV/TXT files to read. |
format |
Specify if samples are paired and in rows (rowp), unpaired and in rows (rowu), in columns and paired (colp), or in columns and unpaired (colu). |
lbl.type |
Specify the data label type, either categorical (disc) or continuous (cont). |
nmdr |
Boolean. Default set to FALSE (data is uploaded by the user and not fetched through an API call to the Metabolomics Workbench). |
Jeff Xia [email protected], Jasmine Chong McGill University, Canada License: GNU GPL (>= 2)
Read.TextDataTs is used to read metabolomics data for co-vairiate analysis
Read.TextDataTs(mSetObj = NA, filePath, format = "rowu")Read.TextDataTs(mSetObj = NA, filePath, format = "rowu")
mSetObj |
metaboanalyst object, initialized by InitDataObjects("pktable", "mf", FALSE) |
filePath |
file path of data |
format |
format of data table, can be "rowu" or "colu" |
This function determines reads in user's individual data for meta-analysis.
ReadIndData(mSetObj = NA, dataName, format = "colu")ReadIndData(mSetObj = NA, dataName, format = "colu")
mSetObj |
Input name of the created mSet Object |
dataName |
Name of inputted dataset. |
format |
Specify if samples are paired and in rows (rowp), unpaired and in rows (rowu), in columns and paired (colp), or in columns and unpaired (colu). |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
ReadMetaData
ReadMetaData(mSetObj = NA, metafilename)ReadMetaData(mSetObj = NA, metafilename)
mSetObj |
metaboanalyst object, initialized by InitDataObjects("pktable", "mf", FALSE) |
metafilename |
file path of data |
ReadMetaPathTable
ReadMetaPathTable(mSetObj = NA, dataNM, dataFormat, dataType)ReadMetaPathTable(mSetObj = NA, dataNM, dataFormat, dataType)
mSetObj |
mSetObj |
dataNM |
file name with absolute path |
dataFormat |
data format, can be colu or rowu |
dataType |
data type, usually "massPeaks" |
Jeff Xia[email protected] Zhiqiang Pang[email protected] McGill University, Canada License: GNU GPL (>= 2)
ReadMetaPathTableMix
ReadMetaPathTableMix(mSetObj = NA, dataNM, dataNM2, dataFormat, dataType)ReadMetaPathTableMix(mSetObj = NA, dataNM, dataNM2, dataFormat, dataType)
mSetObj |
mSetObj |
dataNM |
file name 1 with absolute path (should be ESI+) |
dataNM2 |
file name 2 with absolute path (should be ESI-) |
dataFormat |
data format, can be colu or rowu |
dataType |
data type, usually "massPeaks" |
Jeff Xia[email protected] Zhiqiang Pang[email protected] McGill University, Canada License: GNU GPL (>= 2)
This function reads paired peak lists or spectra files. The pair information is stored in a file where each line is a pair and names are separated by ":".
ReadPairFile(filePath = "pairs.txt")ReadPairFile(filePath = "pairs.txt")
filePath |
Set file path |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Record R Commands
RecordRCommand(mSetObj = NA, cmd)RecordRCommand(mSetObj = NA, cmd)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
cmd |
Commands |
Perform various utilities for peak grouping
rectUnique(m, order = seq(length = nrow(m)), xdiff = 0, ydiff = 0)rectUnique(m, order = seq(length = nrow(m)), xdiff = 0, ydiff = 0)
m |
Peaks |
order |
Performs seq(length = nrow(m)) |
xdiff |
Default set to 0 |
ydiff |
Default set to 0 |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
When there are multiple datasets, record their name and save the inputted data as a .qs file to save memory. Note, the memory will only contain one mSetObj$dataSet object. By default the last one will be the most recent/current dataSet object. Users can switch which data to load into memory.
RegisterData(mSetObj = NA, dataSet)RegisterData(mSetObj = NA, dataSet)
mSetObj |
Input name of the created mSet Object |
dataSet |
Input dataset to be registered in R. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Remove compounds
RemoveCmpd(mSetObj = NA, inx)RemoveCmpd(mSetObj = NA, inx)
mSetObj |
Input name of the created mSet Object |
inx |
Input the index of compound to remove |
Remove data object, the current dataSet will be the last one by default
RemoveData(dataName)RemoveData(dataName)
dataName |
Input name of data to remove |
Dups is the one with duplicates
RemoveDuplicates(data, lvlOpt = "mean", quiet = T)RemoveDuplicates(data, lvlOpt = "mean", quiet = T)
data |
Input data to remove duplicates |
lvlOpt |
Set options, default is mean |
quiet |
Set to quiet, logical, default is T |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Remove file
RemoveFile(fileName)RemoveFile(fileName)
fileName |
Input name of file to remove |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Remove folder
RemoveFolder(folderName)RemoveFolder(folderName)
folderName |
Input name of folder to remove |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Remove selected genes based on an index
RemoveGene(mSetObj = NA, inx)RemoveGene(mSetObj = NA, inx)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
inx |
Input compound index |
Remove variables based upon a user-defined percentage cut-off of missing values. If a user specifies a threshold of 20 in at least 20
RemoveMissingPercent(mSetObj, percent)RemoveMissingPercent(mSetObj, percent)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
percent |
Input the percentage cut-off you wish to use. For instance, 50 percent is represented by percent=0.5. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
This function will replace zero/missing values by half of the smallest positive value in the original dataset. This method will be called after all missing value imputations are conducted. Also, it directly modifies the mSet$dataSet$proc if executed after normalization, or the mSet$dataSet$norm if before normalization.
ReplaceMin(mSetObj=NA)ReplaceMin(mSetObj=NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Redraw current graph for zooming or clipping then return a value
RerenderMetPAGraph(mSetObj = NA, imgName, width, height, zoom.factor = NA)RerenderMetPAGraph(mSetObj = NA, imgName, width, height, zoom.factor = NA)
mSetObj |
Input name of the created mSet Object |
imgName |
Input the name of the plot |
width |
Input the width, there are 2 default widths, the first, width = NULL, is 10.5. The second default is width = 0, where the width is 7.2. Otherwise users can input their own width. |
height |
Input the height of the created plot. |
zoom.factor |
zoom factor, numeric |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform Random Forest
RF.Anal(mSetObj = NA, treeNum = 500, tryNum = 7, randomOn = 1)RF.Anal(mSetObj = NA, treeNum = 500, tryNum = 7, randomOn = 1)
mSetObj |
Input name of the created mSet Object |
treeNum |
Input the number of trees to create, default is set to 500 |
tryNum |
Set number of tries, default is 7 |
randomOn |
Set random, default is 1 |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform Random Forest
RF.AnalMeta( mSetObj = NA, treeNum = 500, tryNum = 7, randomOn = 1, selectedMeta )RF.AnalMeta( mSetObj = NA, treeNum = 500, tryNum = 7, randomOn = 1, selectedMeta )
mSetObj |
Input name of the created mSet Object |
treeNum |
Input the number of trees to create, default is set to 500 |
tryNum |
Set number of tries, default is 7 |
randomOn |
Set random, default is 1 |
selectedMeta |
selected Meta elements |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Function to create the table of newly classified samples
ROCPredSamplesTable(mSetObj = NA)ROCPredSamplesTable(mSetObj = NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) Function to create the table of newly classified samples |
Core code to perform R-SVM
RSVM(x, y, ladder, CVtype, CVnum = 0)RSVM(x, y, ladder, CVtype, CVnum = 0)
x |
Row matrix of data |
y |
Class label: 1 / -1 for 2 classes |
ladder |
Input the ladder |
CVtype |
Integer (N fold CV), "LOO" leave-one-out CV, "bootstrape" bootstrape CV |
CVnum |
Number of CVs, LOO: defined as sample size, Nfold and bootstrape: user defined, default as sample size outputs a named list Error: a vector of CV error on each level SelFreq: a matrix for the frequency of each gene being selected in each level with each column corresponds to a level of selection and each row for a gene The top important gene in each level are those high-freqent ones |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
recursive SVM for feature selection and classification
RSVM.Anal(mSetObj = NA, cvType)RSVM.Anal(mSetObj = NA, cvType)
mSetObj |
Input name of the created mSet Object |
cvType |
Cross-validation type |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
SaintyCheckMSPfile
SaintyCheckMSPfile( mSetObj = NA, filename = "", format_type = "mzmine", keepAllspec = FALSE )SaintyCheckMSPfile( mSetObj = NA, filename = "", format_type = "mzmine", keepAllspec = FALSE )
mSetObj |
mSetObj |
filename |
filename with path |
format_type |
format type, can be 'mzmine' or 'msdial' |
keepAllspec |
if you want to search all spectra from your local, turn keepAllspec to TRUE. it is FALSE by default. |
Zhiqiang Pang
Perform SAM
SAM.Anal( mSetObj = NA, method = "d.stat", paired = FALSE, varequal = TRUE, delta = 0, imgName, dpi = 72 )SAM.Anal( mSetObj = NA, method = "d.stat", paired = FALSE, varequal = TRUE, delta = 0, imgName, dpi = 72 )
mSetObj |
Input name of the created mSet Object |
method |
Method for SAM analysis, default is set to "d.stat", alternative is "wilc.stat" |
paired |
Logical, indicates if samples are paired or not. Default is set to FALSE |
varequal |
Logical, indicates if variance is equal. Default is set to TRUE |
delta |
numeric |
imgName |
image name, character |
dpi |
image dpi, integer |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
SanityCheckData is used for data processing, and performs a basic sanity check of the uploaded content, ensuring that the data is suitable for further analysis. The function will return a message if the data has successfully passed the check and is deemed suitable for further analysis. If it fails, the function will return a 0. The function will perform the check directly onto the mSet$dataSet object, and must be performed immediately after reading in data. The sanity check function evaluates the accuracy of sample and class labels, data structure, deals with non-numeric values, removes columns that are constant across all samples (variance = 0), and by default replaces missing values with half of the original minimal positive value in your dataset.
SanityCheckData(mSetObj=NA)SanityCheckData(mSetObj=NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Performs a sanity check on each-uploaded dataset for meta-analysis. Briefly, this function will exclude empty rows, check class labels, ensure only 2 groups are being compared within the dataset, ensure sample names are unique, remove low quality samples/features, and replace missing values.
SanityCheckIndData(mSetObj = NA, dataName)SanityCheckIndData(mSetObj = NA, dataName)
mSetObj |
Input name of the created mSet Object |
dataName |
Input name of the dataset to perform the sanity check. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
SanityCheckMeta#'
SanityCheckMeta(mSetObj = NA, init = 1)SanityCheckMeta(mSetObj = NA, init = 1)
mSetObj |
metaboanalyst object |
init |
can be 0 or 1 |
SanityCheckMetaPathTable
SanityCheckMetaPathTable(mSetObj = NA, dataName, dataName2)SanityCheckMetaPathTable(mSetObj = NA, dataName, dataName2)
mSetObj |
mSetObj |
dataName |
file name 1 with absolute path |
dataName2 |
file name 2 with absolute path or "null" |
Jeff Xia[email protected] Zhiqiang Pang[email protected] McGill University, Canada License: GNU GPL (>= 2)
SanityCheckData is used for data processing, and performs a basic sanity check of the uploaded data, ensuring that the data is suitable for further analysis. The function ensure that all parameters are properly set based on updated parameters.
SanityCheckMummichogData(mSetObj=NA)SanityCheckMummichogData(mSetObj=NA)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects). |
Jasmine Chong, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Function to save each mSetObj as a RDS file to be used later in PerformMetaPSEA. Should be called after SetPeakEnrichMethod/SetMummichogPval
savePeakListMetaData(mSetObj = NA)savePeakListMetaData(mSetObj = NA)
mSetObj |
mSetObj |
This function saves the processed data with class names as CSV files. Several files may be generated, the original data, processed data, peak normalized, and/or normalized data.
SaveTransformedData(mSetObj = NA)SaveTransformedData(mSetObj = NA)
mSetObj |
Input name of the created mSet Object |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Search for compound from all member compounds of metabolite set
SearchByCompound(mSetObj = NA, query)SearchByCompound(mSetObj = NA, query)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
query |
Input the query to search |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Given a metabolite set name, search its index
SearchByName(mSetObj = NA, query)SearchByName(mSetObj = NA, query)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
query |
Input the query to search |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Search metabolite set libraries
SearchMsetLibraries(mSetObj = NA, query, type)SearchMsetLibraries(mSetObj = NA, query, type)
mSetObj |
Input name of the created mSet Object |
query |
Input the query to search |
type |
Input the data type (name or compound) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
This function performs mapping of user's data to the internal network to create a network from the seed nodes
SearchNetDB( mSetObj = NA, db.type, table.nm, require.exp = TRUE, min.score = 900 )SearchNetDB( mSetObj = NA, db.type, table.nm, require.exp = TRUE, min.score = 900 )
mSetObj |
Input name of the created mSet Object |
db.type |
Input the database type |
table.nm |
Input the organism code for the sqlite table (ppi). For chemical type, the table.nm is drugbank of ctd |
require.exp |
Logical, only used for the STRING database |
min.score |
Input the minimal score, only used for the STRING database |
Othman Soufan, Jeff Xia [email protected], [email protected] McGill University, Canada License: GNU GPL (>= 2)
This function selects one or more datasets to be used for meta-analysis. 1 is used to indicate that a dataset is selected and by default, all datasets will be selected for meta-analysis.
SelectMultiData(mSetObj = NA)SelectMultiData(mSetObj = NA)
mSetObj |
Input name of the created mSet Object |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
ROC utilities
SetAnalysisMode(mSetObj, mode)SetAnalysisMode(mSetObj, mode)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects)s |
mode |
Input the selected mode for biomarker analysis, "univ" for univariate ROC curve analysis, "explore" for multivariate ROC curve analysis, and "test" for ROC curve based model creation and evaluation. McGill University, Canada License: GNU GPL (>= 2) |
Jeff Xia [email protected]
Set cachexia set used
SetCachexiaSetUsed(mSetObj = NA, used)SetCachexiaSetUsed(mSetObj = NA, used)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
used |
Set data to be used |
Note: to change object in the enclosing enviroment, use "<<-"
SetCandidate(mSetObj = NA, query_nm, can_nm)SetCandidate(mSetObj = NA, query_nm, can_nm)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects). |
query_nm |
Input the query name. |
can_nm |
Input the candidate name. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Choose two groups (when more than two groups uploaded)
SetCurrentGroups(mSetObj = NA, grps)SetCurrentGroups(mSetObj = NA, grps)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
grps |
Input the groups |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
if enrichment analysis, also prepare lib by creating a list of metabolite sets
SetCurrentMsetLib(mSetObj=NA, libname, excludeNum)SetCurrentMsetLib(mSetObj=NA, libname, excludeNum)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
libname |
Input user selected name of library, "self", "kegg_pathway", "smpdb_pathway", "blood", "urine", "csf", "snp", "predicted", "location", and "drug". |
excludeNum |
Users input the mimimum number compounds within selected metabolite sets (metabolitesets < excludeNum) |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
The "selected.cmpds" should be for extraction
SetCustomData(mSetObj = NA, selected.cmpds, selected.smpls)SetCustomData(mSetObj = NA, selected.cmpds, selected.smpls)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
selected.cmpds |
Input the vector containing the compounds |
selected.smpls |
Input the vector containing the samples |
SetDataTypeOfMeta
SetDataTypeOfMeta(mSetObj = NA)SetDataTypeOfMeta(mSetObj = NA)
mSetObj |
metaboanalyst object |
For two factor time series only
SetDesignType(mSetObj = NA, design)SetDesignType(mSetObj = NA, design)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
design |
Input the design type |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
setInclusionDataSets#'
setInclusionDataSets(mSetObj = NA, datasVec)setInclusionDataSets(mSetObj = NA, datasVec)
mSetObj |
mSetObj |
datasVec |
a vector of all files |
Jeff Xia[email protected] Zhiqiang Pang[email protected] McGill University, Canada License: GNU GPL (>= 2)
#setInclusionDataSets(c("A1_pos.csv","B1_pos.csv","C1_pos.csv"));#setInclusionDataSets(c("A1_pos.csv","B1_pos.csv","C1_pos.csv"));
note, this process can be long, need to return a value to force Java to wait
SetKEGG.PathLib(mSetObj = NA, libNm, lib.version)SetKEGG.PathLib(mSetObj = NA, libNm, lib.version)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
libNm |
lib name option KEGG pathway library or "v2018" for the KEGG pathway library version prior to November 2019. |
lib.version |
Input the KEGG pathway version. "current" for the latest |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Set metabolome filter
SetMetabolomeFilter(mSetObj = NA, TorF)SetMetabolomeFilter(mSetObj = NA, TorF)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
TorF |
Input metabolome filter |
setMS2DBOpt
setMS2DBOpt(mSetObj = NA, DBoption = "regular")setMS2DBOpt(mSetObj = NA, DBoption = "regular")
mSetObj |
mSetObj object |
DBoption |
database option to define neutral loss or not, can be either 'regualr" or 'nl'. |
Zhiqiang Pang
Set the p-value cutoff for mummichog analysis.
SetMummichogPval(mSetObj = NA, cutoff)SetMummichogPval(mSetObj = NA, cutoff)
mSetObj |
Input the name of the created mSetObj. |
cutoff |
cutoff value for mummichog running |
Jasmine Chong, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Set the p-value cutoff for mummichog analysis.
SetMummichogPvalFromPercent(mSetObj = NA, fraction)SetMummichogPvalFromPercent(mSetObj = NA, fraction)
mSetObj |
Input the name of the created mSetObj. |
fraction |
fraction value for mummichog running |
Jasmine Chong, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Set organism for further analysis
Set organism for further analysis
SetOrganism(mSetObj = NA, org) SetOrganism(mSetObj = NA, org)SetOrganism(mSetObj = NA, org) SetOrganism(mSetObj = NA, org)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
org |
Set organism ID |
This function sets the peak enrichment method.
SetPeakEnrichMethod(mSetObj = NA, algOpt, version = "v2")SetPeakEnrichMethod(mSetObj = NA, algOpt, version = "v2")
mSetObj |
Input the name of the created mSetObj. |
algOpt |
algorithm option, can be "gsea", "mum" and "integ" |
version |
version of mummichog |
Jasmine Chong, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Set the peak format for mummichog analysis.
SetPeakFormat(mSetObj = NA, type = "mpt")SetPeakFormat(mSetObj = NA, type = "mpt")
mSetObj |
mSetObj |
type |
Input the name of mummichog analysis type, usually 'mpt'. |
Jasmine Chong, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Set peak list group values
SetPeakList.GroupValues(mSetObj = NA)SetPeakList.GroupValues(mSetObj = NA)
mSetObj |
Input name of mSetObj, the data used is the nmr.xcmsSet object |
SetPeakParam, used to set the peak param
SetPeakParam( platform = "general", Peak_method = "centWave", RT_method = "loess", mzdiff, snthresh, bw, ppm, min_peakwidth, max_peakwidth, noise, prefilter, value_of_prefilter, fwhm, steps, sigma, peakBinSize, max, criticalValue, consecMissedLimit, unions, checkBack, withWave, profStep, minFraction, minSamples, maxFeatures, mzCenterFun, integrate, extra, span, smooth, family, fitgauss, polarity, perc_fwhm, mz_abs_iso, max_charge, max_iso, corr_eic_th, mz_abs_add, adducts, rmConts, BlankSub )SetPeakParam( platform = "general", Peak_method = "centWave", RT_method = "loess", mzdiff, snthresh, bw, ppm, min_peakwidth, max_peakwidth, noise, prefilter, value_of_prefilter, fwhm, steps, sigma, peakBinSize, max, criticalValue, consecMissedLimit, unions, checkBack, withWave, profStep, minFraction, minSamples, maxFeatures, mzCenterFun, integrate, extra, span, smooth, family, fitgauss, polarity, perc_fwhm, mz_abs_iso, max_charge, max_iso, corr_eic_th, mz_abs_add, adducts, rmConts, BlankSub )
platform |
platform |
Peak_method |
Peak_method |
RT_method |
RT_method |
mzdiff |
mzdiff |
snthresh |
snthresh |
bw |
bw |
ppm |
ppm |
min_peakwidth |
min_peakwidth |
max_peakwidth |
max_peakwidth |
noise |
noise |
prefilter |
prefilter |
value_of_prefilter |
value_of_prefilter |
fwhm |
fwhm |
steps |
steps |
sigma |
sigma |
peakBinSize |
peakBinSize |
max |
max |
criticalValue |
criticalValue |
consecMissedLimit |
consecMissedLimit |
unions |
unions |
checkBack |
checkBack |
withWave |
withWave |
profStep |
profStep |
minFraction |
minFraction |
minSamples |
minSamples |
maxFeatures |
maxFeatures |
mzCenterFun |
mzCenterFun |
integrate |
integrate |
extra |
extra |
span |
span |
smooth |
smooth |
family |
family |
fitgauss |
fitgauss |
polarity |
polarity |
perc_fwhm |
perc_fwhm |
mz_abs_iso |
mz_abs_iso |
max_charge |
max_charge |
max_iso |
max_iso |
corr_eic_th |
corr_eic_th |
mz_abs_add |
mz_abs_add |
adducts |
adducts |
rmConts |
rmConts |
Zhiqiang Pang
SetRTincluded
SetRTincluded(mSetObj = NA, rt = "no")SetRTincluded(mSetObj = NA, rt = "no")
mSetObj |
mSetObj |
rt |
retention time types, "minutes", "seconds" or "no" |
mSetObj
note, this process can be long, need to return a value to force Java to wait
SetSMPDB.PathLib(mSetObj = NA, libNm)SetSMPDB.PathLib(mSetObj = NA, libNm)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
libNm |
Input library name |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Save adduct names for mapping
Setup.AdductData(mSetObj = NA, qvec)Setup.AdductData(mSetObj = NA, qvec)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
qvec |
Input the vector to query |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Save biofluid type for SSP
Setup.BiofluidType(mSetObj = NA, type)Setup.BiofluidType(mSetObj = NA, type)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
type |
Input the biofluid type |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Save concentration data
Setup.ConcData(mSetObj = NA, conc)Setup.ConcData(mSetObj = NA, conc)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
conc |
Input the concentration data |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Read user uploaded metabolome as a list of HMDB compound names
Setup.HMDBReferenceMetabolome(mSetObj = NA, filePath)Setup.HMDBReferenceMetabolome(mSetObj = NA, filePath)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
filePath |
Input the path to the user's list of HMDB compound names |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Read user uploaded metabolome as a list of KEGG pathway ids
Setup.KEGGReferenceMetabolome(mSetObj = NA, filePath)Setup.KEGGReferenceMetabolome(mSetObj = NA, filePath)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
filePath |
Input the path to the user's list of KEGG pathway ids |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Save compound name for mapping
Setup.MapData(mSetObj = NA, qvec)Setup.MapData(mSetObj = NA, qvec)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
qvec |
Input the vector to query |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Return two col csv file, first name, second cmpd list
Setup.UserMsetLibData(mSetObj = NA, filePath)Setup.UserMsetLibData(mSetObj = NA, filePath)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
filePath |
Input the path to the user's uploaded metabolite set library |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Only works for human (hsa.rda) data 2018 - works for ath, eco, mmu, sce
SetupKEGGLinks(smpdb.ids)SetupKEGGLinks(smpdb.ids)
smpdb.ids |
Input the list of SMPD ids to add SMPDB links |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Only works for human (hsa.rda) data 2018 - works for ath, eco, mmu, sce
SetupSMPDBLinks(kegg.ids)SetupSMPDBLinks(kegg.ids)
kegg.ids |
Input the list of KEGG ids to add SMPDB links |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
SOM analysis
SOM.Anal(mSetObj = NA, x.dim, y.dim, initMethod, neigb = "gaussian")SOM.Anal(mSetObj = NA, x.dim, y.dim, initMethod, neigb = "gaussian")
mSetObj |
Input name of the created mSet Object |
x.dim |
Input X dimension for SOM analysis |
y.dim |
Input Y dimension for SOM analysis |
initMethod |
Input the method |
neigb |
Default is set to 'gaussian' |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Runs sgccak() modified from RGCCA
sparse.mint.block_iteration( A, design, study = NULL, keepA.constraint = NULL, keepA = NULL, scheme = "horst", init = "svd", max.iter = 100, tol = 1e-06, verbose = TRUE, bias = FALSE, penalty = NULL )sparse.mint.block_iteration( A, design, study = NULL, keepA.constraint = NULL, keepA = NULL, scheme = "horst", init = "svd", max.iter = 100, tol = 1e-06, verbose = TRUE, bias = FALSE, penalty = NULL )
A |
Data |
design |
Set design |
study |
Default set to NULL |
keepA.constraint |
Default set to NULL |
keepA |
Default set to NULL |
scheme |
Scheme, default set to "horst" |
init |
Init mode, default set to "svd" |
max.iter |
Max number of iterations, numeric, default set to 100 |
tol |
Tolerance, numeric, default set to 1e-06 |
verbose |
Default set to TRUE |
bias |
Default set to FALSE |
penalty |
Default set to NULL |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Sparse PLS functions (adapted from mixOmics package for web-based usage) this function is a particular setting of internal_mint.block the formatting of the input is checked in internal_wrapper.mint
splsda( X, Y, ncomp = 2, mode = c("regression", "canonical", "invariant", "classic"), keepX, keepX.constraint = NULL, scale = TRUE, tol = 1e-06, max.iter = 100, near.zero.var = FALSE, logratio = "none", multilevel = NULL )splsda( X, Y, ncomp = 2, mode = c("regression", "canonical", "invariant", "classic"), keepX, keepX.constraint = NULL, scale = TRUE, tol = 1e-06, max.iter = 100, near.zero.var = FALSE, logratio = "none", multilevel = NULL )
X |
numeric matrix of predictors |
Y |
a factor or a class vector for the discrete outcome |
ncomp |
the number of components to include in the model. Default to 2. |
mode |
Default set to c("regression", "canonical", "invariant", "classic") |
keepX |
Number of |
keepX.constraint |
A list containing which variables of X are to be kept on each of the first PLS-components. |
scale |
Boleean. If scale = TRUE, each block is standardized to zero means and unit variances (default: TRUE). |
tol |
Convergence stopping value. |
max.iter |
integer, the maximum number of iterations. |
near.zero.var |
boolean, see the internal |
logratio |
"None" by default, or "CLR" |
multilevel |
Designate multilevel design, "NULL" by default |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Sparse PLS-DA (from mixOmics)
SPLSR.Anal( mSetObj = NA, comp.num, var.num, compVarOpt, validOpt = "Mfold", foldNum = 5, doCV = FALSE )SPLSR.Anal( mSetObj = NA, comp.num, var.num, compVarOpt, validOpt = "Mfold", foldNum = 5, doCV = FALSE )
mSetObj |
Input name of the created mSet Object |
comp.num |
Input the number of computations to run |
var.num |
Input the number of variables |
compVarOpt |
Input the option to perform SPLS-DA |
validOpt |
INput the valid option |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Row-wise norm methods, when x is a row. Normalize by a sum of each sample, assume constant sum (1000). Options for normalize by sum median, reference sample, reference reference (compound), or quantile normalization
SumNorm(x)SumNorm(x)
x |
Input data to normalize |
Jeff Xia [email protected] McGill University, Canada
Run template on all the high region effect genes
template.match(x, template, dist.name)template.match(x, template, dist.name)
x |
Input data |
template |
Input template |
dist.name |
Input distance method |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
This function is used to perform t-test analysis.
Ttests.Anal( mSetObj = NA, nonpar = F, threshp = 0.05, paired = FALSE, equal.var = TRUE, pvalType = "fdr", all_results = FALSE )Ttests.Anal( mSetObj = NA, nonpar = F, threshp = 0.05, paired = FALSE, equal.var = TRUE, pvalType = "fdr", all_results = FALSE )
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
nonpar |
Logical, use a non-parametric test, T or F. False is default. |
threshp |
Numeric, enter the adjusted p-value (FDR) cutoff |
paired |
Logical, is data paired (T) or not (F). |
equal.var |
Logical, evaluates if the group variance is equal (T) or not (F). |
pvalType |
pvalType, can be "fdr" etc. |
all_results |
Logical, if TRUE, returns T-Test analysis results for all compounds. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Unzips uploaded .zip files, removes the uploaded file, checks for success
UnzipUploadedFile(inPath, outPath, rmFile = T)UnzipUploadedFile(inPath, outPath, rmFile = T)
inPath |
Input the path of the zipped files |
outPath |
Input the path to directory where the unzipped files will be deposited |
rmFile |
Logical, input whether or not to remove files. Default set to T |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Function to update the mSetObj after removing features or samples.
UpdateData(mSetObj = NA, order.group = FALSE)UpdateData(mSetObj = NA, order.group = FALSE)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
This functions handles updating the mSet object for mummichog analysis.
UpdateEC_Rules(mSetObj = NA, force_primary_ion, rt_tol)UpdateEC_Rules(mSetObj = NA, force_primary_ion, rt_tol)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects). |
force_primary_ion |
Character, if "yes", only mz features that match compounds with a primary ion are kept. |
rt_tol |
Numeric. Input the retention time tolerance used for determining ECs (in seconds). |
Jasmine Chong, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Function to update the graph settings.
UpdateGraphSettings(mSetObj = NA, colVec, shapeVec)UpdateGraphSettings(mSetObj = NA, colVec, shapeVec)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
colVec |
colVec |
shapeVec |
shapeVec |
This functions handles updating the mSet object for mummichog analysis. It is necessary to utilize this function to specify to the organism's pathways to use (libOpt), the mass-spec mode (msModeOpt) and mass-spec instrument (instrumentOpt).
UpdateInstrumentParameters(mSetObj=NA, instrumentOpt, msModeOpt, force_primary_ion, rt_frac, rt_tol)UpdateInstrumentParameters(mSetObj=NA, instrumentOpt, msModeOpt, force_primary_ion, rt_frac, rt_tol)
mSetObj |
Input the name of the created mSetObj (see InitDataObjects). |
instrumentOpt |
Numeric. Define the mass-spec instrument used to perform untargeted metabolomics. |
msModeOpt |
Character. Define the mass-spec mode of the instrument used to perform untargeted metabolomics. |
force_primary_ion |
Character, if "yes", only mz features that match compounds with a primary ion are kept. |
rt_frac |
rt_frac. |
rt_tol |
rt_tol. |
Jasmine Chong, Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
used for integrative analysis as well as general pathways analysis for meta-analysis results
UpdateIntegPathwayAnalysis(mSetObj=NA, qids, file.nm, topo="dc", enrich="hyper", libOpt="integ")UpdateIntegPathwayAnalysis(mSetObj=NA, qids, file.nm, topo="dc", enrich="hyper", libOpt="integ")
mSetObj |
Input name of the created mSet Object |
qids |
Input the query IDs |
file.nm |
Input the name of the file |
topo |
Select the mode for topology analysis: Degree Centrality ("dc") measures the number of links that connect to a node (representing either a gene or metabolite) within a pathway; Closeness Centrality ("cc") measures the overall distance from a given node to all other nodes in a pathway; Betweenness Centrality ("bc")measures the number of shortest paths from all nodes to all the others that pass through a given node within a pathway. |
enrich |
Method to perform over-representation analysis (ORA) based on either hypergenometrics analysis ("hyper") or Fisher's exact method ("fisher"). |
libOpt |
Select the different modes of pathways, either the gene-metabolite mode ("integ") which allows for joint-analysis and visualization of both significant genes and metabolites or the gene-centric ("genetic") and metabolite-centric mode ("metab") which allows users to identify enriched pathways driven by significant genes or metabolites, respectively. |
Jeff Xia [email protected] McGill University, Canada License: GNU GPL (>= 2)
Update the OPLS loadings
UpdateOPLS.Splot(mSetObj = NA, plotType)UpdateOPLS.Splot(mSetObj = NA, plotType)
mSetObj |
Input name of the created mSet Object |
plotType |
Set annotation type, "all" to label all variables and "none" to label no variables. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Update the PCA loadings
UpdatePCA.Loading(mSetObj = NA, plotType)UpdatePCA.Loading(mSetObj = NA, plotType)
mSetObj |
Input name of the created mSet Object |
plotType |
Set annotation type, "all" to label all variables and "none" to label no variables. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Update the PLS loadings
UpdatePLS.Loading(mSetObj = NA, plotType)UpdatePLS.Loading(mSetObj = NA, plotType)
mSetObj |
Input name of the created mSet Object |
plotType |
Set annotation type, "all" to label all variables and "none" to label no variables. |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Convert user coords (as used in current plot) to pixels in a png adapted from the imagemap package
usr2png(xy, im)usr2png(xy, im)
xy |
Input coordinates |
im |
Input coordinates |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
Perform volcano analysis
Volcano.Anal(mSetObj=NA, paired=FALSE, fcthresh, cmpType, nonpar=F, threshp, equal.var=TRUE, pval.type="raw")Volcano.Anal(mSetObj=NA, paired=FALSE, fcthresh, cmpType, nonpar=F, threshp, equal.var=TRUE, pval.type="raw")
mSetObj |
Input the name of the created mSetObj (see InitDataObjects) |
paired |
Logical, T if data is paired, F if data is not. |
fcthresh |
Numeric, input the fold change threshold |
cmpType |
Comparison type, 0 indicates group 1 vs group 2, and 1 indicates group 2 vs group 1 |
nonpar |
Logical, indicate if a non-parametric test should be used (T or F) |
threshp |
Numeric, indicate the p-value threshold |
equal.var |
Logical, indicates if the group variance is equal (T) or unequal (F) |
pval.type |
To indicate raw p-values, use "raw". To indicate FDR-adjusted p-values, use "fdr". |
Jeff Xia[email protected] McGill University, Canada License: GNU GPL (>= 2)
This function converts processed raw LC/MS data from XCMS to a usable data object (mSet) for MetaboAnalyst. The immediate next step following using this function is to perform a SanityCheck, and then further data processing and analysis can continue.
XSet2MSet(xset, dataType, analType, paired = F, format, lbl.type)XSet2MSet(xset, dataType, analType, paired = F, format, lbl.type)
xset |
The name of the xcmsSet object created. |
dataType |
The type of data, either list (Compound lists), conc (Compound concentration data), specbin (Binned spectra data), pktable (Peak intensity table), nmrpeak (NMR peak lists), mspeak (MS peak lists), or msspec (MS spectra data). |
analType |
Indicate the analysis module to be performed: stat, pathora, pathqea, msetora, msetssp, msetqea, mf, cmpdmap, smpmap, or pathinteg. |
paired |
Logical, is data paired (T) or not (F). |
format |
Specify if samples are paired and in rows (rowp), unpaired and in rows (rowu), in columns and paired (colp), or in columns and unpaired (colu). |
lbl.type |
Specify the data label type, either categorical (disc) or continuous (cont). |