diffGene {eegc} | R Documentation

## Gene Expression Differential Analysis

### Description

Identify the differentially expressed genes for each pair-wise comparison of given three types of samples.

### Usage

    diffGene(expr, array = TRUE, fpkm = FALSE, counts =FALSE, method =c("limma","DESeq2"),
    from.sample, to.sample, target.sample, filter = FALSE, filter.perc = 0.4,
    padjust ="fdr", signif = TRUE, pvalue = 0.05)

### Arguments

`expr` | a data frame with gene expression data.
`array, fpkm, counts` | logical, specifying the type of input gene expression data.
`method` | differential analysis method, alternatively to "limma" and "DESeq2", default to "limma". "DESeq2" can be chosen only when `counts` is `TRUE`.
`from.sample, to.sample, target.sample` | character to specify the name of initiating sample, derived sample and primary sample during a cellular engineering.
`filter` | logical to indicate whether the genes need to be filtered when match the parameter `filter.perc` , only applied to fpkm and counts data.
`filter.perc` | a 0 to 1 number to specify the gene filter criteria by the percentage of samples with non-zero expression. Only used to fpkm and counts data when `filter` is `TRUE`, and filter the genes with non-zero expression in less than filter.perc samples.
`padjust` | indicate the method to do p.value correction, default to "fdr". See `p.adjust`.
`signif` | logical to indicate whether only the significantly differential genes are output, default to FALSE.
`pvalue` | a cutoff p.value for the significant genes, default to 0.05, only used when `signif` is TRUE.

### Details

This function can be applied on both microarray and RNA-seq data for differential analysis when one of the "array", "fpkm", or "counts" is specified. It does differential analysis to each pair-wise sample comparison among the from.sample, to.sample and target.sample.

### Value

A list with components : a list with differential analysis result for each pair-wise comparison; a list with differential gene names for each pair-wise comparison; a data frame with filtered/unfiltered gene expression.

### Examples

    data(SandlerFPKM)

    # differential expression analysis:
    diffgene = diffGene(expr = SandlerFPKM, array=FALSE,  fpkm=TRUE,  counts=FALSE,
                       from.sample="DMEC", to.sample="rEChMPP", target.sample="CB",
                       filter=TRUE, filter.perc =0.4, pvalue = 0.05 )

    # differential analysis results
    diffgene.result = diffgene[[1]]
    # differential genes
    diffgene.genes = diffgene[[2]]
    # filtered expression data
    expr.filter = diffgene[[3]]

[Package eegc version 1.18.0 Index]