ArticleBriefings in bioinformatics2024
Characterizing efficient feature selection for single-cell expression analysis.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Systematic benchmarking and optimal strategy selection of cross-species integration methods.Briefings in bioinformatics · 2026Article
- Deciphering Cell-Type-Specific Transcriptional Regulation in Tomato Leaves Through Ensemble Machine Learning and Single-Cell Transcriptomics.Plants (Basel, Switzerland) · 2026Article
- Coralysis enables sensitive identification of imbalanced cell types and states in single-cell data via multi-level integration.Nucleic acids research · 2025Article
- Deciphering gene expression patterns using large-scale transcriptomic data and its applications.Briefings in bioinformatics · 2024Article
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5 authors.
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Abstract
Unsupervised feature selection is a critical step for efficient and accurate analysis of single-cell RNA-seq data. Previous benchmarks used two different criteria to compare feature selection methods: (i) proportion of ground-truth marker genes included in the selected features and (ii) accuracy of cell clustering using ground-truth cell types. Here, we systematically compare the performance of 11 feature selection methods for both criteria. We first demonstrate the discordance between these criteria and suggest using the latter. We then compare the distribution of selected genes in their means between feature selection methods. We show that lowly expressed genes exhibit seriously high coefficients of variation and are mostly excluded by high-performance methods. In particular, high-deviation- and high-expression-based methods outperform the widely used in Seurat package in clustering cells and data visualization. We further show they also enable a clear separation of the same cell type from different tissues as well as accurate estimation of cell trajectories.
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