Description Usage Arguments Details Value Author(s) References Examples
Differential abundance analysis of count data using DESeq2
1 2 | pmartRseq_DESeq2(omicsData, norm_factors = NULL, test = "wald", pairs, adj,
thresh)
|
omicsData |
an object of the class 'seqData' created by |
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. |
Performs differential abundance testing on two groups using DESeq2.
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.
Allison Thompson
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.
1 2 3 4 5 6 7 8 9 | ## 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)
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