ArticleNature methods2025
Feature selection methods affect the performance of scRNA-seq data integration and querying.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- IntegrateRigor: annotation-free integration optimization for cell identity recovery reveals cancer-immune interface niches.bioRxiv : the preprint server for biology · 2026Article
- MipSScs: Artificial neural network-based data integration of 2D/3D single-cell spatial RNA sequence data from virus-infected human cerebral organoids.bioRxiv : the preprint server for biology · 2026Article
- Quantifying Cross-Modal Association Confidence for Single-Cell RNA-ATAC Integration.bioRxiv : the preprint server for biology · 2026Article
- crocketa: an automated Snakemake framework for integrated single-cell transcriptome and immune-repertoire analysis.BMC genomics · 2026Article
- Assessment of dispersion metrics for estimating single-cell transcriptional variability.PLoS computational biology · 2026Article
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- Cell type-independent timekeeping gene modules enable embryonic stage prediction in zebrafish.bioRxiv : the preprint server for biology · 2025Article
- Effects of segmentation errors on downstream-analysis in highly-multiplexed tissue imaging.PLoS computational biology · 2025Article
- Feature selection methods affect the performance of scRNA-seq data integration and querying.Nature methods · 2025Article
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Authors and funding
10 authors.
Funding
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Abstract
The availability of single-cell transcriptomics has allowed the construction of reference cell atlases, but their usefulness depends on the quality of dataset integration and the ability to map new samples. Previous benchmarks have compared integration methods and suggest that feature selection improves performance but have not explored how best to select features. Here, we benchmark feature selection methods for single-cell RNA sequencing integration using metrics beyond batch correction and preservation of biological variation to assess query mapping, label transfer and the detection of unseen populations. We reinforce common practice by showing that highly variable feature selection is effective for producing high-quality integrations and provide further guidance on the effect of the number of features selected, batch-aware feature selection, lineage-specific feature selection and integration and the interaction between feature selection and integration models. These results are informative for analysts working on large-scale tissue atlases, using atlases or integrating their own data to tackle specific biological questions.
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Registered trials
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