PreprintCited by 122Single-cell and spatial transcriptomicsGene expression and cancer classificationGenomics and Phylogenetic Studies

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A comparison of marker gene selection methods for single-cell RNA sequencing data

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Pullin, Jeffrey M., McCarthy, Davis J.·2022·Genome biology

DOI 10.1101/2022.05.09.490241PMC PMC10895860

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Abstract

The development of single-cell RNA sequencing (scRNA-seq) has enabled scientists to catalogue and probe the transcriptional heterogeneity of individual cells in unprecedented detail. A common step in the analysis of scRNA-seq data is the selection of so-called marker genes, most commonly to enable annotation of the biological cell types present in the sample. In this paper we benchmarked 56 computational methods for selecting marker genes in scRNA-seq data. The performance of the methods was compared using 10 real scRNA-seq datasets and over 170 additional simulated datasets. Methods were compared on their ability to recover simulated and expert-annotated marker genes, the predictive performance and characteristics of the gene sets they select, their memory usage and speed and their implementation quality. In addition, various case studies were used to scrutinise the most commonly used methods, highlighting issues and inconsistencies. Overall, we present a comprehensive evaluation of methods for selecting marker genes in scRNA-seq data. Our results highlight the efficacy of simple methods, especially the Wilcoxon rank-sum test, Student’s t-test and logistic regression. All code used in the evaluation, including an extensible Snakemake pipeline, is available at: https://gitlab.svi.edu.au/biocellgen-public/mage_2020_marker-gene-benchmarking .

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Authors: Pullin, J. M., & McCarthy, D. J.
Affiliations: The University of Melbourne (AU)
Corresponding author: McCarthy, Davis J.
ORCID: Pullin, Jeffrey M.
McCarthy, Davis J.
Venue: Genome biology
Publisher: openRxiv
Published: 2022-05-10
Volume: 25
Issue: 1
Pages: 56-56
ISSN: 1465-6906 (ISSN-L)
DOI: 10.1101/2022.05.09.490241
PMC: PMC10895860
OpenAlex: W4392167979
Cited by: 122
References: 72
Citations by year: 2026: 43 · 2025: 46 · 2024: 23 · 2023: 10
FWCI: 15.20
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
Locations: Genome biology (publishedVersion, cc-by)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Minerva Access (University of Melbourne) (submittedVersion, cc-by)
Funders: Baker Foundation (GNT1162829)
University of Melbourne
Medical Research Council
National Health and Medical Research Council (1162829, 1112681, 1195595)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords: marker gene selection, single-cell RNA sequencing, benchmarking, cell type annotation, Mann-Whitney test, Student's t-test, logistic regression, simulated data, computational performance
MeSH terms: Exome Sequencing, Software, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis

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A comparison of marker gene selection methods for single-cell RNA sequencing data

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