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.
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See how to installReview the instructions the agent will receive before you install.
Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("glmGamPoi")glm_gp().test_de().pseudobulk().on_disk = TRUE to avoid memory limits.DESeq2 or edgeR instead because they are classical methods heavily optimized for low-replicate bulk data.glmGamPoi requires raw count data to fit Gamma-Poisson models.matrix, sparse matrix, or on-disk HDF5Array).SingleCellExperiment or SummarizedExperiment object containing unnormalized integer counts.~ 1 or ~ condition*cell + ind).TRUE or FALSE)."ratio").test_de() (e.g., using the cond() function or coefficient arithmetic).pseudobulk() with vars() to quote the grouping columns from colData.test_de() (e.g., pval).rowSums(counts) > 5) before fitting the model to improve performance and reliability.pseudobulk() to aggregate single-cell data by individual/replicate before running glm_gp() to ensure reliable statistical tests and avoid underestimating variance.cond() function within test_de() to cleanly specify complex contrasts (e.g., cond(cell = "B cells", condition = "stim") - cond(...)).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.glm_gp() will complain if factor levels have no observations; fix this by calling droplevels() on the metadata columns before fitting.pseudobulk_by argument inside test_de() is deprecated and wastes computation time; fix this by calling pseudobulk() before glm_gp().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.This skill is the knowledge layer — when, why, and how to use glmgampoi. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.
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The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
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This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte
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This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En
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apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
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ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
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AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with
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