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Peer-reviewedCC BY 4.0NAR Genomics and Bioinformatics

Cell DiffErential Expression by Pooling (CellDEEP) highlights issues in differential gene expression in scRNA-seq

  • Yiyi Cheng1,2
  • Toby Kettlewell3
  • Ross F Laidlaw1,4
  • Olympia M Hardy1
  • Andrew McCluskey1,2
  • Thomas D Otto1,2,5,6
  • Domenico Somma1,2,7
7 affiliations
  1. 1School of Infection & Immunity, University of Glasgow , G12 8TA,Glasgow ,
  2. 2Bernhard Nocht Institute of Tropical Medicine , 20359,Hamburg ,
  3. 3School of Mathematics & Statistics, University of Glasgow , G12 8QQ,Glasgow ,
  4. 4Center for Infectious Diseases, Leiden University , 2300 RC,LUMC, Leiden ,
  5. 5University of Hamburg , 20148,Hamburg ,
  6. 6LPHI, CNRS, INSERM, Université de Montpellier , 34095,Montpellier, Occitanie ,
  7. 7Division of Rheumatology and Clinical Immunology, Università Cattolica del Sacro Cuore , 00168,Rome ,
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Abstract

Accurate identification of differentially expressed genes (DEGs) in single-cell RNA sequencing data remains challenging. Single-cell-specific statistical models often report large numbers of candidate genes but can exhibit inflated false positive rates, whereas pseudobulk approaches improve false discovery control at the cost of reduced sensitivity. To overcome the noise and bias that other tools have, and allow the user to have more control of the DEG process, we present CellDEEP, which uses a cell aggregation (metacell) approach. This tool provides a framework for flexible selection of pooling strategies and parameterization for differential expression analysis. Benchmarking on simulated and real datasets, including COVID-19 and rheumatoid arthritis, shows that CellDEEP often outperforms other methods, consistently reduces false positives compared to single-cell methods and recovers more true positives than pseudobulk methods. Our work shifts the focus from selecting a single ‘best’ method to an approach that reduces cell-level noise while preserving biological signal, together with transparent validation framework, advancing more reliable differential-expression analysis in single-cell transcriptomics.

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Cite

BibTeXRIS
BibTeX
@article{cheng2026cell,
  title = {{Cell DiffErential Expression by Pooling (CellDEEP) highlights issues in differential gene expression in scRNA-seq}},
  author = {Yiyi Cheng and Toby Kettlewell and Ross F Laidlaw and Olympia M Hardy and Andrew McCluskey and Thomas D Otto and Domenico Somma},
  journal = {NAR Genomics and Bioinformatics},
  year = {2026},
  month = oct,
  doi = {10.1093/nargab/lqag128},
  url = {https://academic.oup.com/nargab/article/doi/10.1093/nargab/lqag128/8960103},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Cell DiffErential Expression by Pooling (CellDEEP) highlights issues in differential gene expression in scRNA-seq
AU  - Yiyi Cheng
AU  - Toby Kettlewell
AU  - Ross F Laidlaw
AU  - Olympia M Hardy
AU  - Andrew McCluskey
AU  - Thomas D Otto
AU  - Domenico Somma
PY  - 2026
DA  - 2026/10/21
JO  - NAR Genomics and Bioinformatics
DO  - 10.1093/nargab/lqag128
UR  - https://academic.oup.com/nargab/article/doi/10.1093/nargab/lqag128/8960103
AB  - Accurate identification of differentially expressed genes (DEGs) in single-cell RNA sequencing data remains challenging. Single-cell-specific statistical models often report large numbers of candidate genes but can exhibit inflated false positive rates, whereas pseudobulk approaches improve false discovery control at the cost of reduced sensitivity. To overcome the noise and bias that other tools have, and allow the user to have more control of the DEG process, we present CellDEEP, which uses a cell aggregation (metacell) approach. This tool provides a framework for flexible selection of pooling strategies and parameterization for differential expression analysis. Benchmarking on simulated and real datasets, including COVID-19 and rheumatoid arthritis, shows that CellDEEP often outperforms other methods, consistently reduces false positives compared to single-cell methods and recovers more true positives than pseudobulk methods. Our work shifts the focus from selecting a single ‘best’ method to an approach that reduces cell-level noise while preserving biological signal, together with transparent validation framework, advancing more reliable differential-expression analysis in single-cell transcriptomics.
SN  - 2631-9268
ER  -