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GLM-PCA applies a generalized linear model framework to perform dimensionality reduction directly on raw count data. Traditional PCA requires normalized and log-transformed counts, which can introduce artifacts — particularly the mean-variance relationship present in sequencing data. GLM-PCA avoids this by modeling counts under a Poisson or negative binomial likelihood.

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Reference

Townes, F. W., Hicks, S. C., Aryee, M. J., & Irizarry, R. A. (2019). Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. Genome Biology. https://doi.org/10.1186/s13059-019-1861-6 Source: willtownes/glmpca · CRAN

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Installation

The glmpca package must be installed before using RunGLMPCA(). It is available on both CRAN and GitHub.
# Install from CRAN
install.packages("glmpca")

# Or install the development version from GitHub
remotes::install_github("willtownes/glmpca")
For deviance-based feature selection (recommended for choosing informative genes prior to GLM-PCA), install scry: 
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install("scry")

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Why GLM-PCA

Conventional scRNA-seq workflows normalize raw counts and apply a log transformation before PCA. This pipeline:
  • Distorts the mean-variance relationship of count data.
  • Can inflate the contribution of lowly-expressed genes.
  • Introduces a systematic bias when counts are sparse.
GLM-PCA operates directly on raw counts using a Poisson model by default (or negative binomial), properly accounting for the discrete, overdispersed nature of sequencing data.

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Key function

RunGLMPCA() — Runs GLM-PCA on a Seurat object and stores the result as a DimReduc object. It uses the counts slot of the specified assay as input.

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Example

library(Seurat)
library(SeuratData)
library(SeuratWrappers)
library(glmpca)
library(scry)

InstallData("pbmc3k")
data("pbmc3k")

# Select top 2000 genes by deviance (captures the most variation in counts)
m <- GetAssayData(pbmc3k, slot = "counts", assay = "RNA")
devs <- scry::devianceFeatureSelection(m)
dev_ranked_genes <- rownames(pbmc3k)[order(devs, decreasing = TRUE)]
topdev <- head(dev_ranked_genes, 2000)

# Run GLM-PCA with 10 dimensions
# Note: raw counts from the counts slot are used — do not normalize beforehand
ndims <- 10
pbmc3k <- RunGLMPCA(pbmc3k, features = topdev, L = ndims)

# Build neighbor graph and cluster using GLM-PCA embedding
pbmc3k <- FindNeighbors(pbmc3k, reduction = "glmpca", dims = 1:ndims, verbose = FALSE)
pbmc3k <- FindClusters(pbmc3k, verbose = FALSE)

# Run UMAP for visualization using GLM-PCA as input
pbmc3k <- RunUMAP(pbmc3k, reduction = "glmpca", dims = 1:ndims, verbose = FALSE)

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Visualize results

# Cluster overview
DimPlot(pbmc3k)

# Compare clusters to original annotations
with(pbmc3k[[]], table(seurat_annotations, seurat_clusters))

# Feature expression (normalize RNA assay for display purposes only)
pbmc3k <- NormalizeData(pbmc3k, verbose = FALSE)
features.plot <- c("CD3D", "MS4A1", "CD8A", "GZMK", "GZMB", "FCGR3A")
FeaturePlot(pbmc3k, features.plot, ncol = 2)

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Parameters

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object
Seurat
A Seurat object. Must contain raw counts in the counts slot of the target assay.
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L
integer
default:"5"
Number of dimensions (latent factors) to return.
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features
character vector
default:"NULL"
Features to use. Defaults to the variable features identified by FindVariableFeatures(). Providing a curated list (e.g., top deviance genes) is recommended for best results.
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assay
character
default:"NULL"
Assay to use. Defaults to the default assay of the Seurat object.
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reduction.name
character
default:"glmpca"
Name under which the resulting DimReduc object is stored in the Seurat object.
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reduction.key
character
default:"GLMPC_"
Prefix for the column names of the GLM-PCA embedding dimensions.
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...
Additional arguments passed directly to glmpca::glmpca(). Use this to set the fam argument (e.g., fam = "nb" for negative binomial) or other model options.
GLM-PCA reads from the counts slot, not the data (normalized) slot. Do not run NormalizeData() before RunGLMPCA() — the normalization is handled implicitly by the model.

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