bioDistWPlot {STATegRa} | R Documentation |
Function that plots the "distance relation" between features computed through different surrogate features.
bioDistWPlot(referenceFeatures, listDistW, method.cor)
referenceFeatures |
The set of features to be used. |
listDistW |
A list of bioDistWclass objects. |
method.cor |
Method to compute distances between the elements in the listDistW. The default is spearman correlation. |
Makes a plot with the projected distance between the listDistW objects.
David Gomez-Cabrero
data(STATegRa_S1) data(STATegRa_S2) require(Biobase) # Truncate data for brevity Block1 <- Block1[1:100,] Block2 <- Block2[1:100,] ## Create ExpressionSets mRNA.ds <- createOmicsExpressionSet(Data=Block1,pData=ed,pDataDescr=c("classname")) miRNA.ds <- createOmicsExpressionSet(Data=Block2,pData=ed,pDataDescr=c("classname")) ## Create the bioMap map.gene.miRNA<-bioMap(name = "Symbol-miRNA", metadata = list(type_v1="Gene",type_v2="miRNA", source_database="targetscan.Hs.eg.db", data_extraction="July2014"), map=mapdata) # Create Gene-gene distance computed through miRNA data bioDistmiRNA<-bioDist(referenceFeatures = rownames(Block1), reference = "Var1", mapping = map.gene.miRNA, surrogateData = miRNA.ds, ### miRNA data referenceData = mRNA.ds, ### mRNA data maxitems=2, selectionRule="sd", expfac=NULL, aggregation = "sum", distance = "spearman", noMappingDist = 0, filtering = NULL, name = "mRNAbymiRNA") # Create Gene-gene distance through mRNA data bioDistmRNA<-new("bioDistclass", name = "mRNAbymRNA", distance = cor(t(exprs(mRNA.ds)),method="spearman"), map.name = "id", map.metadata = list(), params = list()) ###### Generation of the list of Surrogated distances. bioDistList<-list(bioDistmRNA,bioDistmiRNA) sample.weights<-matrix(0,4,2) sample.weights[,1]<-c(0,0.33,0.67,1) sample.weights[,2]<-c(1,0.67,0.33,0) ###### Generation of the list of bioDistWclass objects. bioDistWList<-bioDistW(referenceFeatures = rownames(Block1), bioDistList = bioDistList, weights=sample.weights) ###### Plot of distances. bioDistWPlot(referenceFeatures = rownames(Block1) , listDistW = bioDistWList, method.cor="spearman") ###### Computing the matrix of features/distances associated. fm<-bioDistFeature(Feature = rownames(Block1)[1] , listDistW = bioDistWList, threshold.cor=0.7) bioDistFeaturePlot(data=fm)