Combined_method: Combined_method: a R function for RNA-Seq data differential...
In DongmeiLi2017/Combine: ComSeq: An ensemble method for RNA-Seq differential analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/Combined_method.R

This ensemble method combined four current methods (DESeq2, EBSeq, SAMSeq, NOISeq) with equal weight on their resulting FDR-adjusted P-values to identify significantly differentially expressed genes from RNA-Seq data.

1	Combined_method(RNAseqcount, label, alpha)

`RNAseqcount`	The input RNA-Seq count matrix with rows and columns denoting features and samples, respectively. The first column of the matrix denotes names of features.
`label`	A vector for group notation such as 1s denote treatment group and 0s denote control group
`alpha`	The signifiance level

The Combined method combined the analysis results from four current popular RNA-Seq differential analysis methods (DESeq2, EBSeq, SAMSeq, NOISeq) and declare a gene is significantly differentially expressed only when the gene is identified by all four methods.

Combined_method produces a named list with the following components:

`DESeq2.sig`	Feature names identified by DESeq2 differential analysis method
`EBSeq.sig`	Feature names identified by EBSeq differential analysis method
`SAMSeq.sig`	Feature names identified by SAMSeq differential analysis method
`NOISeq.sig`	Feature names identified by NOISeq differential analysis method
`Combined.sig`	Feature names identified by Combined differential analysis method
`Combined.sig.table`	Significant feature tables from combined method including original feature counts from all samples, test statistics from DESeq2 method, and adjusted P-values from DESeq2 method

Dongmei Li

Love MI, Huber W and Anders S (2014). <e2><80><9c>Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.<e2><80><9d> Genome Biology, 15, pp. 550. doi: 10.1186/s13059-014-0550-8. Leng N and Kendziorski C (2015). EBSeq: An R package for gene and isoform differential expression analysis of RNA-seq data. R package version 1.14.0. Tarazona S, Garcia-Alcalde F, Dopazo J, Ferrer A and Conesa A (2011). <e2><80><9c>Differential expression in RNA-seq: a matter of depth.<e2><80><9d> Genome Research, 21(12), pp. 4436.

The DESeq2, EBSeq, SAMSeq, and NOISeq packages.

system.file("data", package = "ComSeq")
data("Bottomly_count")
summary(Bottomly_count)
label <- c(rep(0, 10), rep(1, 11))
Result <- Combined_method(RNAseqcount = Bottomly_count, label = label, alpha = 0.05)
summary(Result)
Result