diffGene: Gene Expression Differential Analysis
In eegc: Engineering Evaluation by Gene Categorization (eegc)

Description Usage Arguments Details Value Examples

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

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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)

`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.

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.

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.

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]]