ReviewBriefings in bioinformatics2021
Selecting gene features for unsupervised analysis of single-cell gene expression data.
Review in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Trajectory-informed gene feature selection in single-cell analysis with SEEK-VFI.Cell reports methods · 2026Article
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- Trajectory-informed gene feature selection in single-cell analysis with SEEK-VFI.bioRxiv : the preprint server for biology · 2025Article
- Branch-specific gene discovery in cell differentiation using multi-omics graph attention.PLoS computational biology · 2025Article
- Feature selection methods affect the performance of scRNA-seq data integration and querying.Nature methods · 2025Article
- Exploring cell-to-cell variability and functional insights through differentially variable gene analysis.NPJ systems biology and applications · 2025Article
- Crafted experiments to evaluate feature selection methods for single-cell RNA-seq data.NAR genomics and bioinformatics · 2025Article
- Controlled noise: evidence of epigenetic regulation of single-cell expression variability.Bioinformatics (Oxford, England) · 2024Article
- Characterizing efficient feature selection for single-cell expression analysis.Briefings in bioinformatics · 2024Article
- Species-agnostic transfer learning for cross-species transcriptomics data integration without gene orthology.Briefings in bioinformatics · 2024Article
- Species-agnostic transfer learning for cross-species transcriptomics data integration without gene orthology.Briefings in bioinformatics · 2024Article
- Differential variability analysis of single-cell gene expression data.Briefings in bioinformatics · 2023Article
- scAce: an adaptive embedding and clustering method for single-cell gene expression data.Bioinformatics (Oxford, England) · 2023Article
- A cofunctional grouping-based approach for non-redundant feature gene selection in unannotated single-cell RNA-seq analysis.Briefings in bioinformatics · 2023Article
- Benchmarking cell-type clustering methods for spatially resolved transcriptomics data.Briefings in bioinformatics · 2023Article
- High-dimensional genomic feature selection with the ordered stereotype logit model.Briefings in bioinformatics · 2022Review
- The scINSIGHT Package for Integrating Single-Cell RNA-Seq Data from Different Biological Conditions.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data.Genome biology · 2022Article
- Identification ofComputational and mathematical methods in medicine · 2022Article
- Probe-based mass spectrometry approaches for single-cell and single-organelle measurements.Mass spectrometry reviewsReview
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Single-cell RNA sequencing (scRNA-seq) technologies facilitate the characterization of transcriptomic landscapes in diverse species, tissues, and cell types with unprecedented molecular resolution. In order to evaluate various biological hypotheses using high-dimensional single-cell gene expression data, most computational and statistical methods depend on a gene feature selection step to identify genes with high biological variability and reduce computational complexity. Even though many gene selection methods have been developed for scRNA-seq analysis, there lacks a systematic comparison of the assumptions, statistical models, and selection criteria used by these methods. In this article, we summarize and discuss 17 computational methods for selecting gene features in unsupervised analysis of single-cell gene expression data, with unified notations and statistical frameworks. Our discussion provides a useful summary to help practitioners select appropriate methods based on their assumptions and applicability, and to assist method developers in designing new computational tools for unsupervised learning of scRNA-seq data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.