deg_deseq2: Differential expression analysis using DESeq2

In wt12318/Easy_Bioinfo: Make some common bioinformatic workfolw easier to operate

Differential expression analysis using DESeq2

Description

Differential expression analysis using DESeq2

Usage

deg_deseq2(
  dt,
  control_label,
  control_counts,
  treatment_lable,
  treatment_counts,
  parallel = FALSE,
  ncores = 1
)

Arguments

dt	A data frame, rows are gene expression (counts),columns are sample names; the order of columns must be control samples followed by treatment samples.
control_label	A character vector, name for control samples,such as "ctrl".
control_counts	A numeric vector, how many samples are control.
treatment_lable	A character vector, name for treatment samples,such as "KD"(Knockdown).
treatment_counts	A numeric vector, how many samples are treatment.
parallel	if FALSE, no parallelization. if TRUE, parallel execution using BiocParallel.
ncores	the number of cores for parallelization.

Value

Return a result dataframe from a DESeq analysis giving base means across samples, log2 fold changes, standard errors, test statistics, p-values and adjusted p-values

Examples

res <- deg_deseq2(count_df,control_label="ctrl",control_counts=2,treatment_lable="KD",treatment_counts=2)

wt12318/Easy_Bioinfo documentation built on June 17, 2022, 9:22 a.m.