glmGamPoi

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

• Version: 1.24.0 · Bioconductor: 3.23 · R: ≥ 4.6
• Imports: Rcpp, beachmat, DelayedMatrixStats, matrixStats, MatrixGenerics, SparseArray, S4Vectors, DelayedArray, HDF5Array, Matrix, SummarizedExperiment, SingleCellExperiment, BiocGenerics, rlang, vctrs
• System requirements: C++17
• Install: BiocManager::install("glmGamPoi")

When to Use

• Fitting Gamma-Poisson (negative binomial) generalized linear models on high-dimensional count data using glm_gp().
• Performing differential expression analysis on single-cell RNA-seq data using quasi-likelihood ratio testing via test_de().
• Aggregating single-cell counts into pseudobulk samples for multi-condition comparisons using pseudobulk().
• Processing massive datasets (e.g., millions of cells) efficiently by setting on_disk = TRUE to avoid memory limits.

When NOT to Use

• For standard bulk RNA-seq with small sample sizes, use DESeq2 or edgeR instead because they are classical methods heavily optimized for low-replicate bulk data.
• For normalized or log-transformed data, use standard linear models instead because glmGamPoi requires raw count data to fit Gamma-Poisson models.

Data Requirements

• A raw count matrix (dense matrix, sparse matrix, or on-disk HDF5Array).
• A SingleCellExperiment or SummarizedExperiment object containing unnormalized integer counts.

Key Parameters

• design: Formula or model matrix specifying the experimental design (e.g., ~ 1 or ~ condition*cell + ind).
• on_disk: Boolean indicating whether to force the calculation to happen on-disk (e.g., TRUE or FALSE).
• size_factor: Method to calculate size factors (e.g., "ratio").
• reference_level: String specifying the baseline level for factors in the design formula.
• contrast: The specific comparison to test in test_de() (e.g., using the cond() function or coefficient arithmetic).
• group_by: Used in pseudobulk() with vars() to quote the grouping columns from colData.
• sort_by: Column to sort the differential expression results by in test_de() (e.g., pval).

Best Practices

• Filter out genes with very low counts (e.g., rowSums(counts) > 5) before fitting the model to improve performance and reliability.
• Use pseudobulk() to aggregate single-cell data by individual/replicate before running glm_gp() to ensure reliable statistical tests and avoid underestimating variance.
• Use the cond() function within test_de() to cleanly specify complex contrasts (e.g., cond(cell = "B cells", condition = "stim") - cond(...)).

Common Pitfalls

• Overly optimistic p-values: Applying test_de() directly to single-cell data without pseudobulking treats cells from the same sample as independent replicates; fix this by aggregating data with pseudobulk() first.
• Empty factor levels: glm_gp() will complain if factor levels have no observations; fix this by calling droplevels() on the metadata columns before fitting.
• Deprecated pseudobulk workflow: Using the pseudobulk_by argument inside test_de() is deprecated and wastes computation time; fix this by calling pseudobulk() before glm_gp().

Alternatives

• DESeq2: A classical method for bulk RNA-seq differential expression, though significantly slower on large single-cell datasets.
• edgeR: Another classical method for RNA-seq that uses negative binomial models, but glmGamPoi is optimized to be faster for single-cell data.

Citations

• Kang et al. (2018). Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nature Biotechnology, 36(1), 89-94.

References

• Homepage: https://bioconductor.org/packages/glmGamPoi
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/glmGamPoi/inst/doc/glmGamPoi.html

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