| Type: | Package |
| Title: | Differentially Expressed Heterogeneous Overdispersion Gene Test for Count Data |
| Version: | 0.99.0 |
| Description: | Implements a generalized linear model approach for detecting differentially expressed genes across treatment groups in count data. The package supports both quasi-Poisson and negative binomial models to handle over-dispersion, ensuring robust identification of differential expression. It allows for the inclusion of treatment effects and gene-wise covariates, as well as normalization factors for accurate scaling across samples. Additionally, it incorporates statistical significance testing with options for p-value adjustment and log2 fold range thresholds, making it suitable for RNA-seq analysis as described in by Xu et al., (2024) <doi:10.1371/journal.pone.0300565>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Depends: | R (≥ 3.5.0) |
| Imports: | doParallel, foreach, MASS, |
| Suggests: | knitr, rmarkdown, BiocStyle |
| biocViews: | GeneExpression, DifferentialExpression, StatisticalMethod, Regression, Normalization |
| VignetteBuilder: | knitr |
| RoxygenNote: | 7.3.2 |
| URL: | https://github.com/ahshen26/DEHOGT |
| BugReports: | https://github.com/ahshen26/DEHOGT/issues |
| NeedsCompilation: | no |
| Packaged: | 2024-09-11 15:00:01 UTC; arlinashen |
| Author: | Qi Xu [aut],
Arlina Shen  [cre],
Yubai Yuan [ctb],
Annie Qu [ctb] |
| Maintainer: | Arlina Shen <ahshen24@berkeley.edu> |
| Repository: | CRAN |
| Date/Publication: | 2024-09-13 18:30:06 UTC |
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)