Hierarchical marker genes selection in scRNA-seq analysis.

Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE

Original Publication: San Francisco, CA : Public Library of Science, [2005]-

Single-Cell Analysis*/methods ; Sequence Analysis, RNA*/methods ; Computational Biology*/methods; Humans ; Gene Expression Profiling/methods ; RNA-Seq/methods ; Genetic Markers/genetics ; Animals ; Algorithms ; Cluster Analysis ; Single-Cell Gene Expression Analysis

When analyzing scRNA-seq data containing heterogeneous cell populations, an important task is to select informative marker genes to distinguish various cell clusters and annotate the clusters with biologically meaningful cell types. In existing analysis methods and pipelines, marker genes are typically identified using a one-vs-all strategy, examining differential expression between one cell cluster versus the combination of all other cell clusters. However, this strategy applied to cell clusters belonging to closely related cell types often generates overlapping marker genes, which capture the common signature of closely related cell clusters but provide limited information for distinguishing them. To address the limitations of the one-vs-all strategy, we propose a hierarchical marker gene selection strategy that groups similar cell clusters and selects marker genes in a hierarchical manner. This strategy is able to improve the accuracy and interpretability of cell type identification in single-cell RNA-seq data.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Sun, Qiu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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0 (Genetic Markers)

Date Created: 20241212 Date Completed: 20241212 Latest Revision: 20241214

20241214

PMC11637363

10.1371/journal.pcbi.1012643

39666603