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Pseudobulk with proper offsets has the same statistical properties as generalized linear mixed models in single-cell
Hanbin Lee1,2, Buhm Han1,3,4
1Department of Medicine, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
Bioinformatics (Oxford, England)
|August 8, 2024
Summary
Offset-pseudobulk offers a faster and more stable alternative to generalized linear mixed models (GLMMs) for single-cell RNA sequencing analysis. This method maintains the same statistical properties as GLMMs while significantly improving computational efficiency.
Area of Science:
- Computational biology
- Bioinformatics
- Genomics
Background:
- Generalized linear mixed models (GLMMs) are standard for analyzing single-cell RNA sequencing (scRNA-seq) data, comparing gene expression across conditions.
- GLMMs are computationally intensive, and their performance relative to pseudobulk methods is not well-established.
Purpose of the Study:
- To introduce offset-pseudobulk as an efficient and statistically equivalent alternative to GLMMs for scRNA-seq data analysis.
- To demonstrate the computational advantages and statistical validity of the proposed method.
Main Methods:
- Developed and theoretically validated the offset-pseudobulk method, a count-based pseudobulk approach with an offset variable.
- Utilized simulations based on real data to compare offset-pseudobulk with GLMMs.
Main Results:
- Offset-pseudobulk provides identical point estimates and standard errors compared to GLMMs.
- The proposed method is over 10 times faster and numerically more stable than traditional GLMMs.
- Offset-pseudobulk is easily implementable in standard generalized linear model software.
Conclusions:
- Offset-pseudobulk is a computationally efficient and statistically robust alternative to GLMMs for scRNA-seq analysis.
- This method simplifies complex analyses without compromising statistical accuracy.
- Open-source code is available for easy adoption by the research community.
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