A Comparison of Marker Gene Selection Methods for Single-cell RNA Sequencing Data

Overview

Journal Genome Biol

Specialties Biology
Genetics

Date 2024 Feb 26

PMID 38409056

Authors

Jeffrey M Pullin

Davis J McCarthy

Affiliations

Soon will be listed here.

Abstract

Background: The development of single-cell RNA sequencing (scRNA-seq) has enabled scientists to catalog 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 benchmark 59 computational methods for selecting marker genes in scRNA-seq data.

Results: We compare the performance of the methods using 14 real scRNA-seq datasets and over 170 additional simulated datasets. Methods are 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 are used to scrutinize the most commonly used methods, highlighting issues and inconsistencies.

Conclusions: 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.

Citing Articles

Integrated Bioinformatic Analyses Reveal Thioredoxin as a Putative Marker of Cancer Stem Cells and Prognosis in Prostate Cancer.

Sugiki S, Horie T, Kunii K, Sakamoto T, Nakamura Y, Chikazawa I Cancer Inform. 2025; 24:11769351251319872.

PMID: 40008390 PMC: 11851766. DOI: 10.1177/11769351251319872.

Advancements in single-cell RNA sequencing and spatial transcriptomics: transforming biomedical research.

Molla Desta G, Birhanu A Acta Biochim Pol. 2025; 72:13922.

PMID: 39980637 PMC: 11835515. DOI: 10.3389/abp.2025.13922.

tagtango: an application to compare single-cell annotations.

Mora B, Lindsay H, Thiebaut A, Stuart K, Gottardo R Bioinformatics. 2025; 41(2).

PMID: 39798134 PMC: 11814489. DOI: 10.1093/bioinformatics/btaf012.

CORTADO: Hill Climbing Optimization for Cell-Type Specific Marker Gene Discovery.

Lodi M, Clark L, Roy S, Ghosh P bioRxiv. 2025; .

PMID: 39763976 PMC: 11703242. DOI: 10.1101/2024.12.23.630040.

Considerations for building and using integrated single-cell atlases.

Hrovatin K, Sikkema L, Shitov V, Heimberg G, Shulman M, Oliver A Nat Methods. 2024; 22(1):41-57.

PMID: 39672979 DOI: 10.1038/s41592-024-02532-y.

References

Saelens W, Cannoodt R, Todorov H, Saeys Y . A comparison of single-cell trajectory inference methods. Nat Biotechnol. 2019; 37(5):547-554. DOI: 10.1038/s41587-019-0071-9. View

Aibar S, Gonzalez-Blas C, Moerman T, Huynh-Thu V, Imrichova H, Hulselmans G . SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017; 14(11):1083-1086. PMC: 5937676. DOI: 10.1038/nmeth.4463. View

Kleshchevnikov V, Shmatko A, Dann E, Aivazidis A, King H, Li T . Cell2location maps fine-grained cell types in spatial transcriptomics. Nat Biotechnol. 2022; 40(5):661-671. DOI: 10.1038/s41587-021-01139-4. View

Soneson C, Robinson M . Bias, robustness and scalability in single-cell differential expression analysis. Nat Methods. 2018; 15(4):255-261. DOI: 10.1038/nmeth.4612. View

Law C, Chen Y, Shi W, Smyth G . voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014; 15(2):R29. PMC: 4053721. DOI: 10.1186/gb-2014-15-2-r29. View

McCarthy D, Campbell K, Lun A, Wills Q . Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics. 2017; 33(8):1179-1186. PMC: 5408845. DOI: 10.1093/bioinformatics/btw777. View

van der Wijst M, de Vries D, Groot H, Trynka G, Hon C, Bonder M . The single-cell eQTLGen consortium. Elife. 2020; 9. PMC: 7077978. DOI: 10.7554/eLife.52155. View

Hagemann-Jensen M, Ziegenhain C, Chen P, Ramskold D, Hendriks G, Larsson A . Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nat Biotechnol. 2020; 38(6):708-714. DOI: 10.1038/s41587-020-0497-0. View

Harrison P, Pattison A, Powell D, Beilharz T . Topconfects: a package for confident effect sizes in differential expression analysis provides a more biologically useful ranked gene list. Genome Biol. 2019; 20(1):67. PMC: 6437914. DOI: 10.1186/s13059-019-1674-7. View

10.

Vargo A, Gilbert A . A rank-based marker selection method for high throughput scRNA-seq data. BMC Bioinformatics. 2020; 21(1):477. PMC: 7585212. DOI: 10.1186/s12859-020-03641-z. View

11.

Guo H, Li J . scSorter: assigning cells to known cell types according to marker genes. Genome Biol. 2021; 22(1):69. PMC: 7898451. DOI: 10.1186/s13059-021-02281-7. View

12.

Svensson V, Beltrame E, Pachter L . A curated database reveals trends in single-cell transcriptomics. Database (Oxford). 2020; 2020. PMC: 7698659. DOI: 10.1093/database/baaa073. View

13.

Kim H, Wang K, Chen C, Lin Y, Tam P, Lin D . Uncovering cell identity through differential stability with Cepo. Nat Comput Sci. 2024; 1(12):784-790. DOI: 10.1038/s43588-021-00172-2. View

14.

Zappia L, Phipson B, Oshlack A . Splatter: simulation of single-cell RNA sequencing data. Genome Biol. 2017; 18(1):174. PMC: 5596896. DOI: 10.1186/s13059-017-1305-0. View

15.

Zimmerman K, Espeland M, Langefeld C . A practical solution to pseudoreplication bias in single-cell studies. Nat Commun. 2021; 12(1):738. PMC: 7854630. DOI: 10.1038/s41467-021-21038-1. View

16.

Delaney C, Schnell A, Cammarata L, Yao-Smith A, Regev A, Kuchroo V . Combinatorial prediction of marker panels from single-cell transcriptomic data. Mol Syst Biol. 2019; 15(10):e9005. PMC: 6811728. DOI: 10.15252/msb.20199005. View

17.

Zeisel A, Munoz-Manchado A, Codeluppi S, Lonnerberg P, La Manno G, Jureus A . Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science. 2015; 347(6226):1138-42. DOI: 10.1126/science.aaa1934. View

18.

Wang F, Liang S, Kumar T, Navin N, Chen K . SCMarker: Ab initio marker selection for single cell transcriptome profiling. PLoS Comput Biol. 2019; 15(10):e1007445. PMC: 6837541. DOI: 10.1371/journal.pcbi.1007445. View

19.

Aevermann B, Zhang Y, Novotny M, Keshk M, Bakken T, Miller J . A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing. Genome Res. 2021; 31(10):1767-1780. PMC: 8494219. DOI: 10.1101/gr.275569.121. View

20.

Zappia L, Theis F . Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape. Genome Biol. 2021; 22(1):301. PMC: 8555270. DOI: 10.1186/s13059-021-02519-4. View