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DEHOGT: Differentially Expressed Heterogeneous Overdispersion Gene Test for Count Data
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>.
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Package details |
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| Author | Qi Xu [aut], Arlina Shen [cre] (<https://orcid.org/0009-0008-5330-6659>), Yubai Yuan [ctb], Annie Qu [ctb] |
| Bioconductor views | DifferentialExpression GeneExpression Normalization Regression StatisticalMethod |
| Maintainer | Arlina Shen <ahshen24@berkeley.edu> |
| License | GPL-3 |
| Version | 0.99.0 |
| URL | https://github.com/ahshen26/DEHOGT |
| Package repository | View on CRAN |
| Installation |
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