getEMapInput {netDx} | R Documentation |
write enrichment map for consensus nets
getEMapInput(featScores, namedSets, netInfo, pctPass = 0.7, minScore = 1, maxScore = 10, trimFromName = c(".profile", "_cont"), verbose = FALSE)
featScores |
(data.frame) network scores across rounds of cross validation. Rows are networks and columns are network name followed by scores for cross-validation rounds. Output of getFeatureScores() |
namedSets |
(list) list of nets and units (e.g.e pathway names and genes). Should only contain units profiled in this dataset |
netInfo |
(data.frame) Table of network name (netName) and type (netType). Type is used to assign shapes to nodes: clinical clinical rna GUANOSINE_NUCLEOTIDES__I_DE_NOVO__I__BIOSYNTHESIS rna RETINOL_BIOSYNTHESIS |
pctPass |
(numeric between 0 and 1) fraction of splits for which the highest score for the network is required, for that to be the network's maxScore |
minScore |
(integer) features with score below this cutoff are excluded from downstream analyses |
maxScore |
(integer) maximum possible score in one round of cross- validation. e.g. for 10-fold cross-validation, maxScore=10. |
trimFromName |
(char) strings to trim from name with sub() |
verbose |
(logical) print messages |
(list) Length two. 1) nodeAttrs: data.frame of node attributes 2) featureSets: key-value pairs of selected feature sets (e.g. if pathway features are used, keys are pathway names, and values are member genes).
inDir <- system.file("extdata","example_output",package="netDx") outDir <- paste(tempdir(),'plots',sep='/') if (!file.exists(outDir)) dir.create(outDir) featScores <- getFeatureScores(inDir,predClasses=c('LumA','notLumA')) gp <- names(featScores)[1] pathwayList <- readPathways(fetchPathwayDefinitions()) pathwayList <- pathwayList[seq_len(5)] netInfoFile <- system.file("extdata","example_output/inputNets.txt",package="netDx") netInfo <- read.delim(netInfoFile,sep='\t',h=FALSE,as.is=TRUE) emap_input <- getEMapInput(featScores[[gp]],pathwayList,netInfo) summary(emap_input)