Package: glmGamPoi 1.25.1

Constantin Ahlmann-Eltze

glmGamPoi: Fit a Gamma-Poisson Generalized Linear Model

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.

Authors:Constantin Ahlmann-Eltze [aut, cre], Nathan Lubock [ctb], Michael Love [ctb]

glmGamPoi_1.25.1.tar.gz
glmGamPoi_1.25.1.zip(r-4.7)glmGamPoi_1.25.1.zip(r-4.6)glmGamPoi_1.25.1.zip(r-4.5)
glmGamPoi_1.25.1.tgz(r-4.6-x86_64)glmGamPoi_1.25.1.tgz(r-4.6-arm64)glmGamPoi_1.25.1.tgz(r-4.5-x86_64)glmGamPoi_1.25.1.tgz(r-4.5-arm64)
glmGamPoi_1.25.1.tar.gz(r-4.7-arm64)glmGamPoi_1.25.1.tar.gz(r-4.7-x86_64)glmGamPoi_1.25.1.tar.gz(r-4.6-arm64)glmGamPoi_1.25.1.tar.gz(r-4.6-x86_64)
glmGamPoi_1.25.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
glmGamPoi/json (API)

# Install 'glmGamPoi' in R:

install.packages('glmGamPoi', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/const-ae/glmgampoi/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

On BioConductor:glmGamPoi-1.25.1(bioc 3.24)glmGamPoi-1.24.0(bioc 3.23)

regression rnaseq software singlecell gamma-poisson glm negative-binomial-regression on-disk openblas cpp

11.11 score 124 stars 5 packages 1.6k scripts 2 mentions 7 exports 37 dependencies

Last updated from:95cffb79b0. Checks:1 FAILURE, 3 ERROR, 2 OK, 8 WARNING. Indexed: yes.

Target	Result	Time
bioc-checks	FAILURE	344
linux-devel-arm64	ERROR	348
linux-devel-x86_64	ERROR	466
source / vignettes	OK	607
linux-release-arm64	WARNING	361
linux-release-x86_64	WARNING	410
macos-release-arm64	WARNING	236
macos-release-x86_64	WARNING	530
macos-oldrel-arm64	WARNING	259
macos-oldrel-x86_64	WARNING	744
windows-devel	ERROR	1069
windows-release	WARNING	1169
windows-oldrel	WARNING	953
wasm-release	OK	267

Exports:glm_gp loc_median_fit overdispersion_mle overdispersion_shrinkage pseudobulk test_de vars

Dependencies:abind assorthead beachmat Biobase BiocGenerics biocmake cli DelayedArray DelayedMatrixStats dir.expiry filelock generics GenomicRanges glue h5mread HDF5Array IRanges lattice lifecycle Matrix MatrixGenerics matrixStats Rcpp RcppArmadillo rhdf5 rhdf5filters Rhdf5lib rlang S4Arrays S4Vectors Seqinfo SingleCellExperiment SparseArray sparseMatrixStats SummarizedExperiment vctrs XVector

Pseudobulk and differential expression

Pseudobulk | Example | Legacy

Rendered frompseudobulk.Rmdusingknitr::rmarkdown

Last update: 2023-02-27
Started: 2023-01-03

glmGamPoi Quickstart

Rendered fromglmGamPoi.Rmdusingknitr::rmarkdown

Last update: 2020-11-17
Started: 2020-03-27

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Convert glmGamPoi object to a list	as.list.glmGamPoi
Fit a Gamma-Poisson Generalized Linear Model	glm_gp
Estimate local median fit	loc_median_fit
Estimate the Overdispersion for a Vector of Counts	overdispersion_mle
Shrink the overdispersion estimates	overdispersion_shrinkage
Predict 'link' or 'response' values for Gamma-Poisson GLMs	predict.glmGamPoi
Pretty print the result from glm_gp()	format.glmGamPoi format.summary.glmGamPoi print.glmGamPoi print.summary.glmGamPoi summary.glmGamPoi
Create a 'SingleCellExperiment' containing pseudo-bulk samples	pseudobulk
Extract Residuals of Gamma Poisson Model	residuals.glmGamPoi
Solve the equation Y = A B for A or B	solve_lm_for_A solve_lm_for_B
Test for Differential Expression	test_de
Quote grouping variables	vars