## ----get-tcga----------------------------------------------------------------- library(ExperimentHub) eh = ExperimentHub() query(eh , "GSE62944") ## ----download-tcga------------------------------------------------------------ tcga_data <- eh[["EH1"]] ## ----cancer-types------------------------------------------------------------- head(phenoData(tcga_data)$CancerType) ## ----lgg---------------------------------------------------------------------- # subset the expression Set to contain only samples from LGG. lgg_data <- tcga_data[, which(phenoData(tcga_data)$CancerType=="LGG")] # extract the IDHI mutant samples mut_idx <- which(phenoData(lgg_data)$idh1_mutation_found=="YES") mut_data <- exprs(lgg_data)[, mut_idx] # extract the IDH1 WT samples wt_idx <- which(phenoData(lgg_data)$idh1_mutation_found=="NO") wt_data <- exprs(lgg_data)[, wt_idx] # make a countTable. countData <- cbind(mut_data, wt_data) # for DE analysis with DESeq2 we need a sampleTable samples= c(colnames(mut_data), colnames(wt_data)) group =c(rep("mut",length(mut_idx)), rep("wt", length(wt_idx))) coldata <- cbind(samples, group) colnames(coldata) <- c("sampleName", "Group") coldata[,"Group"] <- factor(coldata[,"Group"], c("wt","mut")) # Now we can run DE analysis library(DESeq2) ddsMat <- DESeqDataSetFromMatrix(countData = countData, colData = DataFrame(coldata), design = ~ Group) dds <- ddsMat dds <- dds[ rowSums(counts(dds)) > 1, ] dds <- DESeq(dds) res <- results(dds) summary(res) ## ----sessionInfo-------------------------------------------------------------- sessionInfo()