Gene Expression Differential Analysis

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Description

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

Usage

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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

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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]]