dehogt_func: Differentially Expressed Heterogeneous Overdispersion Genes...

In DEHOGT: Differentially Expressed Heterogeneous Overdispersion Gene Test for Count Data

Differentially Expressed Heterogeneous Overdispersion Genes Testing for Count Data This script implements the main function of the proposed method in the above paper

Description

Differentially Expressed Heterogeneous Overdispersion Genes Testing for Count Data This script implements the main function of the proposed method in the above paper

Usage

dehogt_func(
  data,
  treatment,
  norm_factors = NULL,
  covariates = NULL,
  dist = "qpois",
  padj = TRUE,
  pval_thre = 0.05,
  l2fc = FALSE,
  l2fc_thre = 1,
  num_cores = 1
)

Arguments

data	A matrix of gene expression data where rows represent genes and columns represent samples.
treatment	A vector specifying the treatment conditions for each sample.
norm_factors	An optional vector of normalization factors for each sample. Default is NULL, which assumes equal normalization factors.
covariates	An optional matrix of gene-wise covariates. Default is NULL.
dist	The distribution family for the GLM. Can be "qpois" for quasi-Poisson or "negbin" for negative binomial. Default is "qpois".
padj	Logical value indicating whether to adjust p-values using the Benjamini-Hochberg (BH) procedure. Default is TRUE.
pval_thre	The threshold for identifying differentially expressed genes based on adjusted p-values. Default is 0.05.
l2fc	Logical value indicating whether to consider log2 fold change for identifying differentially expressed genes. Default is FALSE.
l2fc_thre	The threshold for log2 fold change in identifying differentially expressed genes. Default is 1.
num_cores	The number of CPU cores to use for parallel computing. Default is 1.

Value

A list containing:

DE_idx	A logical vector indicating differentially expressed genes.
pvals	A numeric vector of p-values for each gene.
log2fc	A numeric vector of log2 fold changes for each gene.

Examples

# simulate gene expression data
data <- matrix(rpois(1000, 10), nrow = 100, ncol = 10)
# simulate random treatment assignments
treatment <- sample(0:1, 10, replace = TRUE)
# Run main function with parallel computing using 2 cores
result <- dehogt_func(data, treatment, num_cores = 2)

DEHOGT documentation built on Sept. 14, 2024, 1:08 a.m.