Fast searches of large collections of single-cell data using scfind

• Published in: Nature methods Vol. 18; no. 3; pp. 262 - 271
• Main Authors: Lee, Jimmy Tsz Hang, Patikas, Nikolaos, Kiselev, Vladimir Yu, Hemberg, Martin
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.03.2021; Nature Publishing Group
• Subjects: 631/114/2164; 631/114/2785; 631/1647/794; 631/208/199; 631/208/212; Algorithms; Animals; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Computational linguistics; Computer programs; Data Analysis; Data Management - methods; Databases, Genetic; Datasets; DNA sequencing; Gating; Gene expression; Gene Expression Regulation; Genes; Genetic research; Genomes; Information Storage and Retrieval - methods; Language processing; Life Sciences; Markers; Medical research; Methods; Mice; Natural language interfaces; Natural Language Processing; Nucleotide sequencing; Ontology; Optimization; Proteomics; PubMed; Queries; Query processing; R (Programming language); Science; Single-Cell Analysis - methods; Transcriptome - genetics; Transcriptomes; User-Computer Interface; United Kingdom
• ISSN: 1548-7091; 1548-7105
• DOI: 10.1038/s41592-021-01076-9

Single-cell technologies have made it possible to profile millions of cells, but for these resources to be useful they must be easy to query and access. To facilitate interactive and intuitive access to single-cell data we have developed scfind, a single-cell analysis tool that facilitates fast search of biologically or clinically relevant marker genes in cell atlases. Using transcriptome data from six mouse cell atlases, we show how scfind can be used to evaluate marker genes, perform in silico gating, and identify both cell-type-specific and housekeeping genes. Moreover, we have developed a subquery optimization routine to ensure that long and complex queries return meaningful results. To make scfind more user friendly, we use indices of PubMed abstracts and techniques from natural language processing to allow for arbitrary queries. Finally, we show how scfind can be used for multi-omics analyses by combining single-cell ATAC-seq data with transcriptome data. Advances in single-cell sequencing technologies enable generation of datasets of millions of cells. scfind facilitates efficient and sophisticated gene search in massive single-cell datasets.