RNAseq_diffAnalysis: Perform differential analysis of RNAseq data.

In magrichard/dmprocr: A R package that performs differential methylation profiles clustering

Perform differential analysis of RNAseq data.

Description

Perform differential analysis of transcriptomic data (RNAseq) using DESeq2 R package.

Usage

RNAseq_diffAnalysis(data_trscr, exp_grp, gene_list,
  filter_indiv = "no_filter", alpha = 0.05, contrast = c("tissue_status",
  "patho", "normal"), fitType = "parametric",
  normalization_factor = "no_factor")

Arguments

data_trscr	A data matrix that contains transcriptome information (RNAseq counts from HTseq). Columns correspond to indivuals, row correspond to genes.
exp_grp	A exp_grp dataframe that contains metadatas on data_trscr individuals.
gene_list	A gene_list bedfile containing the genes to screen for differential expression.
filter_indiv	A vector of individual names to be screened for differential expression. Optionnal (set on "no_filter" by default).
alpha	A parameter to indicate the significance cutoff used by DESeq2::results funtion for optimizing the independent filtering (by default 0.05). If the adjusted p-value cutoff (FDR) will be a value other than 0.1, alpha should be set to that value.
contrast	A vector containing the constrast to be used to estimate the logarithmic fols change. By default: c("tissue_status","patho","normal")
fitType	A DESeq fitting paramater, by default set to "parametric"
normalization_factor	A matrix of normalization to preempt DESeq2 sizeFactors. Optionnal (set on "no_factor" by default).

Value

A gene_list table including log2FoldChange and adjusted p-value (padj) computed by DESeq2 and a data_ntrscr matrix of normalized counts.

magrichard/dmprocr documentation built on July 21, 2023, 11:01 p.m.