DEHOGT (version 0.99.0)

dehogt_func: 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
)

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.

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.

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)