performDifferentialExpression {cTRAP}R Documentation

Perform differential gene expression based on ENCODE data

Description

Perform differential gene expression based on ENCODE data

Usage

performDifferentialExpression(counts)

Arguments

counts

Data frame: gene expression

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)

[Package cTRAP version 1.2.0 Index]