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

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract Accurate identification of differentially expressed genes (DEGs) in single-cell RNA sequencing (scRNA-seq) 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 parameterisation for differential expression analysis (DE). 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. Graphical abstract

Authors 7

1. Yiyi Cheng

   University of Glasgow · Bernhard Nocht Institute for Tropical Medicine

   Affiliation as printed

   Bernhard Nocht Institute of Tropical Medicine, Hamburg, Hamburg, Germany

   University of Glasgow, School of Infection & Immunity, Glasgow, UK

2. Toby Kettlewell

   University of Glasgow

   Affiliation as printed

   University of Glasgow, School of Mathematics & Statistics, Glasgow, UK

3. Ross F Laidlaw Aachen

   Leiden University · Leiden University Medical Center · University of Glasgow

   Affiliation as printed

   Leiden University Center for Infectious Diseases, LUMC, Leiden, Netherlands

   University of Glasgow, School of Infection & Immunity, Glasgow, UK

4. Olympia M Hardy

   University of Glasgow

   Affiliation as printed

   University of Glasgow, School of Infection & Immunity, Glasgow, UK

5. Andrew McCluskey

   University of Glasgow

   Affiliation as printed

   University of Glasgow, School of Infection & Immunity, Glasgow, UK

6. Thomas D. Otto

   Centre National de la Recherche Scientifique · Inserm · Universität Hamburg · Université de Montpellier · Laboratory of Pathogens and Host Immunity · University of Glasgow · Bernhard Nocht Institute for Tropical Medicine

   Affiliation as printed

   Bernhard Nocht Institute of Tropical Medicine, Hamburg, Hamburg, Germany

   LPHI, CNRS, INSERM, Université de Montpellier, Montpellier, Occitanie, France

   University of Glasgow, School of Infection & Immunity, Glasgow, UK

   University of Hamburg, Hamburg, Germany

7. Domenico Somma corresponding

   Università Cattolica del Sacro Cuore · University of Glasgow · Bernhard Nocht Institute for Tropical Medicine

   Affiliation as printed

   Bernhard Nocht Institute of Tropical Medicine, Hamburg, Hamburg, Germany

   University of Glasgow, School of Infection & Immunity, Glasgow, UK

   Università Cattolica del Sacro Cuore Division of Rheumatology and Clinical Immunology, Rome, Italy