Exploring and mitigating shortcomings in single-cell differential expression analysis with a new statistical paradigm

By using absolute RNA expression rather than relative abundance, GLIMES improves sensitivity, reduces false discoveries, and enhances biological interpretability. This paradigm shift challenges existing workflows and highlights the need for careful consideration of normalization strategies, ultimately paving the way for more accurate and robust single-cell transcriptomic analyses.

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Publication Details

Journal
Genome biology
Published
2025-03-17
DOI
https://doi.org/10.1186/s13059-025-03525-6
Citations
7
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
5.64
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article

Exploring and mitigating shortcomings in single-cell differential expression analysis with a new statistical paradigm

Mengjie Chen, Xiang Zhou, Chih-Hsuan Wu
7 citations
Genome biology
Single-cell and spatial transcriptomics
5.64
article

Exploring and mitigating shortcomings in single-cell differential expression analysis with a new statistical paradigm

Mengjie Chen, Xiang Zhou, Chih-Hsuan Wu
article en
7 citations

Abstract

By using absolute RNA expression rather than relative abundance, GLIMES improves sensitivity, reduces false discoveries, and enhances biological interpretability. This paradigm shift challenges existing workflows and highlights the need for careful consideration of normalization strategies, ultimately paving the way for more accurate and robust single-cell transcriptomic analyses.

Genome biologyVol. 26(1)
University of Michigan (US), University of Chicago (US)
Openalex Percentile: Top 4%
Single-cell and spatial transcriptomics
5.64
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