## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) ## ----cars--------------------------------------------------------------------- library(sigFeature) library(SummarizedExperiment) data(ExampleRawData, package="sigFeature") ExampleRawData ## ----------------------------------------------------------------------------- x <- t(assays(ExampleRawData)$counts) y <- colData(ExampleRawData)$sampleLabels ## ----------------------------------------------------------------------------- pvals <- sigFeaturePvalue(x,y) hist(unlist(pvals),breaks=seq(0,0.08,0.0015),col="skyblue", xlab="p value",ylab="Frequency",main="") ## ----------------------------------------------------------------------------- #system.time(sigfeatureRankedList <- sigFeature(x, y)) ## ----------------------------------------------------------------------------- data(sigfeatureRankedList) print(sigfeatureRankedList[1:10]) ## ----------------------------------------------------------------------------- library(e1071) sigFeature.model=svm(x[ ,sigfeatureRankedList[1:1000]], y, type="C-classification", kernel="linear") summary(sigFeature.model) ## ----------------------------------------------------------------------------- pred <- predict(sigFeature.model, x[ ,sigfeatureRankedList[1:1000]]) table(pred,y) ## ----------------------------------------------------------------------------- #system.time(featureRankedList <- svmrfeFeatureRanking(x, y)) data(featureRankedList) print("Top 10 features are printed below:") print(featureRankedList[1:10]) ## ----------------------------------------------------------------------------- RFE.model=svm(x[ ,featureRankedList[1:1000]], y, type="C-classification", kernel="linear") summary(RFE.model) ## ----------------------------------------------------------------------------- pred <- predict(RFE.model, x[ ,featureRankedList[1:1000]]) table(pred,y) ## ----------------------------------------------------------------------------- pvalsigFe <- sigFeaturePvalue(x, y, 100, sigfeatureRankedList) pvalRFE <- sigFeaturePvalue(x, y, 100, featureRankedList) par(mfrow=c(1,2)) hist(unlist(pvalsigFe),breaks=50, col="skyblue", main=paste("sigFeature"), xlab="p value") hist(unlist(pvalRFE),breaks=50, col="skyblue", main=paste("SVM-RFE"), xlab="p value") ## ----------------------------------------------------------------------------- mytitle<-'Box Plot' boxplot(unlist(pvalsigFe), unlist(pvalRFE), main=mytitle, names=c("sigFeature", "SVM-RFE"), ylab="p value", ylim=c(min(unlist(pvalsigFe)), max(unlist(pvalRFE)))) stripchart(unlist(pvalsigFe), vertical=TRUE, method="jitter", add=TRUE, pch=16, col=c('green')) stripchart(unlist(pvalRFE), vertical=TRUE, at=2, method="jitter", add=TRUE, pch=16, col=c('blue')) grid(nx=NULL, ny=NULL, col="black", lty="dotted") ## ----------------------------------------------------------------------------- library("pheatmap") library("RColorBrewer") pheatmap(x[ ,sigfeatureRankedList[1:20]], scale="row", clustering_distance_rows="correlation") ## ----------------------------------------------------------------------------- pheatmap(x[ ,featureRankedList[1:20]], scale="row", clustering_distance_rows="correlation") ## ----------------------------------------------------------------------------- #set.seed(1234) #results = sigFeature.enfold(x, y, "kfold", 10) data("results") str(results[1]) ## ----------------------------------------------------------------------------- FeatureBasedonFrequency <- sigFeatureFrequency(x, results, 400, 400, pf=FALSE) str(FeatureBasedonFrequency[1]) ## ----------------------------------------------------------------------------- #inputdata <- data.frame(y=as.factor(y) ,x=x) #To run the code given bellow will take huge time. Thus the process #data is given below. #featsweepSigFe = lapply(1:400, sigCVError, FeatureBasedonFrequency, inputdata) data("featsweepSigFe") str(featsweepSigFe[1]) ## ----------------------------------------------------------------------------- PlotErrors(featsweepSigFe, 0, 0.4) ## ----------------------------------------------------------------------------- #WritesigFeature(results, x) ## ----------------------------------------------------------------------------- sessionInfo()