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]