Feature selection methods affect the performance of scRNA-seq data integration and querying.

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 nu

SUBMITTER: Zappia L 

PROVIDER: S-EPMC11978513 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature