| name | bioconductor-glmgampoi |
| description | Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments. |
| when_to_use | Use when: 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 cell. Not for: 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 glmGa |
| user-invocable | false |
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
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