Year: 2024
Type: article
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.
Source: Genome biology
Authors: Jeffrey M. Pullin, Davis J. McCarthy
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,290
Awards: Cloud-ready analysis methods and software to study effects of DNA variants on single-cell gene expression, Combining human genetics with single-cell genomics to unravel the differentiation dynamics of human induced pluripotent stem cells, Developing interpretable machine learning models for clinical imaging and single-cell genomics, G7716934159
OpenAlex ID: W4392167979