Differentially Expressed Heterogeneous Overdispersion Gene Test for Count Data

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

knitr::opts_chunk$set(echo = TRUE)

Introduction

DEHOGT is designed to handle overdispersion in count data using a generalized linear model (GLM) framework. The package supports quasi-Poisson and negative binomial models, making it useful for differential expression analysis of RNA-seq and other count-based data types.

Installation

if (!require("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

BiocManager::install("DEHOGT")

Example Worklow

In this example, we simulate gene expression data and perform differential expression analysis using the quasi-Poisson model. We also show how to incorporate covariates and normalization factors.

## Simulate gene expression data (100 genes, 10 samples)
data <- matrix(rpois(1000, 10), nrow = 100, ncol = 10)

## Randomly assign treatment groups
treatment <- sample(0:1, 10, replace = TRUE)
## Load DEHOGT package
library(DEHOGT)

## Run the function with 2 CPU cores
result <- dehogt_func(data, treatment, num_cores = 2)

## Display results
head(result$pvals)

# Example: Adding covariates and normalization factors
covariates <- matrix(rnorm(1000), nrow = 100, ncol = 10)
norm_factors <- rep(1, 10)

# Run with covariates and normalization factors
result_cov <- dehogt_func(data, treatment, covariates = covariates, norm_factors = norm_factors, num_cores = 2)

Session Info

sessionInfo()

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