dea: Differential Expression Analysis

In jlaffy/scrabble: Perform Exploratory Computational Analyses on Pre-Processed Single-Cell RNA-Sequencing Data

Description

Differential Expression Analysis. Compute gene-level fold changes and (adjusted) p-values between cell clusters of an expression matrix.

Usage

dea(x, y, is.log = T, fc = 2L, p = 0.05, use.wilcox = F,
  adjust.method = unique(c("BH", stats::p.adjust.methods)),
  sort.by = c("fc", "p"), output = c("fc", "p"))

Arguments

x	matrix or character vector with matrix column names
y	matrix to compare against or matrix with x and y column names
is.log	logical indicating whether expression data is in log2 space. Fold change cutoff and calculation are changed accordingly. Default: T
fc	fold change cutoff. Set to NULL to not filter genes based on fold change. Default: 2
p	p value cutoff. Set to NULL to not filter genes based on p-values. Default: 0.05
use.wilcox	logical value to use Wilcoxon-Mann-Whitney test instead of Student's t-test. Default: F
adjust.method	correction for multiple testing? 'none' if not desired. Default: unique(c("BH", stats::p.adjust.methods))
sort.by	sort output by 'fc' or 'p' value (best first). Set sort.by = NULL to preserve original gene (row) order. Default: 'fc'
output	output 'fc' values or 'p' values (per gene). If output = NULL, a list containing both is returned. Default: 'fc'

Value

a numeric vector of gene fold changes or p-values or a list containing both. skip.genes = F to return information on every gene in the expression matrix (regardless of it passing cutoffs).

See Also

character(0)

jlaffy/scrabble documentation built on Nov. 16, 2019, 7:56 a.m.