pmartRseq_DESeq2: DESeq2 analysis of omicsData objects

Description Usage Arguments Details Value Author(s) References Examples

Description

Differential abundance analysis of count data using DESeq2

Usage

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pmartRseq_DESeq2(omicsData, norm_factors = NULL, test = "wald", pairs, adj,
  thresh)

Arguments

omicsData

an object of the class 'seqData' created by as.seqData.

norm_factors

Named vector of normalization parameters to put into DESeq2. If NULL, will use DESeq2's inbuilt normalization. Default is NULL.

test

name of which differential expression test to use, options are "wald" or "paired". Default is "wald". See details for further explanation.

pairs

a matrix dictating which pairwise comparisons to make

adj

multiple comparison adjustment method to use.

thresh

p-value threshold for significance.

Details

Performs differential abundance testing on two groups using DESeq2.

Value

DESeqREsults object, which is a simple subclass of DataFrame. Columns include baseMean, log2FoldChange, lfcSE (standard error of log2FoldChange), stat (Wald statistic), pvalue, and padj (BH adjusted p-values). Use mcols(res)$description.

Author(s)

Allison Thompson

References

Love MI, Huber W and Anders S (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15, pp. 550.

Examples

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## Not run: 
library(mintJansson)
data(cDNA_hiseq_data)
mycdnadata <- group_designation(omicsData = cDNA_hiseq_data, main_effects = c("treatment"), time_course=NULL)
mycdnadata_norm <- normalize_data(omicsData = mycdnadata, norm_fn = "percentile")
mycdnadata_DESeq2 <- pmartRseq_DESeq2(omicsData = mycdnadata_norm, test="wald", pairs = cbind(list("Neg","Plus")), adj = "BH", thresh = 0.05)
mycdnadata_DESeq2 <- mint_DESeq2(omicsData = mycdnadata_norm, test="wald", pairs = cbind(list("Neg","Plus")), adj = "BH", thresh = 0.05)

## End(Not run)

pmartR/pmartRseq documentation built on May 25, 2019, 9:20 a.m.