In Bioconductor/GSE62944: GEO accession data GSE62944 as a SummarizedExperiment

DE analysis with Raw TCGA data using Bioconductor's ExperimentHub and DESeq2

TCGA re-processed RNA-Seq data from 9264 Tumor Samples and 741 normal samples across 24 cancer types and made it available via GSE62944 from GEO. This data is also available as an ExpressionSet from ExperimentHub and can be used for Differential Expression Analysis.

In the below example, we show how one can download this dataset from ExperimentHub.

library(ExperimentHub)
eh = ExperimentHub()
query(eh , "GSE62944")

One can then extract the data for this using

tcga_data <- eh[["EH1"]]

The different cancer types can be accessed using -

 head(phenoData(tcga_data)$CancerType)

Above we show only the top 6 Cancer subtypes.

Case Study

We are interested in identifying the IDH1 mutant and IDH1 wild type samples from TCGA's Low Grade Glioma Samples and then conducting a differential expression analysis using DESeq2

# 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)