摘要：SummaryThe power of single-cell RNA sequencing (scRNA-seq) stems from its ability to uncover cell type-dependent phenotypes, which rests on the accuracy of cell type identification. However, resolving cell types within and, thus, comparison of scRNA-seq data across conditions is challenging owing to technical factors such as sparsity, low number of cells, and batch effect. To address these challenges, we developed scID (SingleCellIDentification), which uses the Fisher's Linear Discriminant Analysis-like framework to identify transcriptionally related cell types between scRNA-seq datasets. We demonstrate the accuracy and performance of scID relative to existing methods on several published datasets. By increasing power to identify transcriptionally similar cell types across datasets with batch effect, scID enhances investigator's ability to integrate and uncover development-, disease-, and perturbation-associated changes in scRNA-seq data.Graphical AbstractDisplay OmittedHighlights•scID identifies transcriptionally equivalent cell populations across datasets•scID's accuracy relies on the goodness of cluster enriched genes in the reference•scID can also be used to score data against a user-provided gene list•R package and use cases are athttps://github.com/BatadaLab/scIDBiological Sciences; Bioinformatics; Mathematical Biosciences; Omics; Transcriptomics
