Perform differential gene expression based on ENCODE data

Description

Perform differential gene expression based on ENCODE data

Usage

performDifferentialExpression(counts)

Arguments

Value

Data frame with differential gene expression results between knockdown and control

Examples

data("ENCODEsamples")

## Download ENCODE metadata for a specific cell line and gene
# cellLine <- "HepG2"
# gene <- "EIF4G1"
# ENCODEmetadata <- downloadENCODEknockdownMetadata(cellLine, gene)

## Download samples based on filtered ENCODE metadata
# ENCODEsamples <- downloadENCODEsamples(ENCODEmetadata)

counts <- prepareENCODEgeneExpression(ENCODEsamples)

# Remove low coverage (at least 10 counts shared across two samples)
minReads   <- 10
minSamples <- 2
filter <- rowSums(counts[ , -c(1, 2)] >= minReads) >= minSamples
counts <- counts[filter, ]

## Convert ENSEMBL identifier to gene symbol
# library(biomaRt)
# mart <- useDataset("hsapiens_gene_ensembl", useMart("ensembl"))
# genes <- sapply(strsplit(counts$gene_id, "\\."), `[`, 1)
# geneConversion <- getBM(filters="ensembl_gene_id", values=genes, mart=mart,
#                         attributes=c("ensembl_gene_id", "hgnc_symbol"))
# counts$gene_id <- geneConversion$hgnc_symbol[
#     match(genes, geneConversion$ensembl_gene_id)]

## Perform differential gene expression analysis
# diffExpr <- performDifferentialExpression(counts)