Pseudobulk with proper offsets has the same statistical properties as generalized linear mixed models in single-cell case-control studies

2024/08/01 by Hanbin Lee, Buhm Han · 1 voice · 1 citation
Biochemistry, Genetics and Molecular Biology · #Gene Regulatory Network Analysis #Gene expression and cancer classification #Single-cell and spatial transcriptomics

paper · doi:10.1093/bioinformatics/btae498

openalex publication_date 2024/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/22

Abstract

MOTIVATION: Generalized linear mixed models (GLMMs), such as the negative-binomial or Poisson linear mixed model, are widely applied to single-cell RNA sequencing data to compare transcript expression between different conditions determined at the subject level. However, the model is computationally intensive, and its relative statistical performance to pseudobulk approaches is poorly understood. RESULTS: We propose offset-pseudobulk as a lightweight alternative to GLMMs. We prove that a count-based pseudobulk equipped with a proper offset variable has the same statistical properties as GLMMs in terms of both point estimates and standard errors. We confirm our findings using simulations based on real data. Offset-pseudobulk is substantially faster (>×10) and numerically more stable than GLMMs. AVAILABILITY AND IMPLEMENTATION: Offset pseudobulk can be easily implemented in any generalized linear model software by tweaking a few options. The codes can be found at https://github.com/hanbin973/pseudobulk_is_mm.

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Discussions

• @epigenci.bsky.social makes a pretty convincing case for pseudobulk (as opposed to GLMMs) in multi-sample single cell studies here: doi.org/10.1093/bioi... . #bioinformatics [bsky, 10 points, 0 comments]

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