RNAseq_cnv_reg: Perform linear regression on expression and CNV data.

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

Perform linear regression on expression and CNV data.

Description

Perform linear regression on expression (counts) and CNV data.

Usage

RNAseq_cnv_reg(data_ntrscr, data_cnv, exp_grp, gene_list, filter_indiv,
  contrast = c("tissue_status", "patho", "normal"), cnv_filter = c(0.025,
  0.975), apply_func = apply)

Arguments

data_ntrscr	A data matrix that contains normalized RNAseq counts (from DESeq2 analysis). Columns correspond to indivuals, row correspond to genes.
data_cnv	A data matrix that contains CNV data
exp_grp	A exp_grp data.frame that contains metadatas on data_trscr individuals.
gene_list	A gene_list bedfile containing the genes for which the linear regression will be perform.
filter_indiv	A vector of individual names to be screened for differential expression. Optionnal, all individual if missing.
contrast	A vector containing the constrast to be used to read metadata
cnv_filter	A vector of two values indicating between which quantiles (for cnv data) the regression should be performed, by default set to c(0.025, 0.975). Should be set to FALSE if no filter is required.
apply_func	Function to be used for apply.

Value

A gene_list table including a z_score value associated with the linear model to the gene_list used in entry.

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