Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST

• Published in: Nature communications Vol. 11; no. 1; p. 3458
• Main Authors: Cao, Zhi-Jie, Wei, Lin, Lu, Shen, Yang, De-Chang, Gao, Ge
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10-07-2020; Nature Publishing Group; Nature Portfolio
• Subjects: 38/91; 49/39; 631/114/129; 631/114/1305; 631/114/2401; 631/114/794; 631/61/212/2019; Algorithms; Annotations; Benchmarks; Cell differentiation; Data bases; Differentiation (biology); Embedding; Heterogeneity; Humanities and Social Sciences; Internet; Machine Learning; multidisciplinary; Neural networks; Ribonucleic acid; RNA; RNA-Seq - methods; Science; Science (multidisciplinary); Similarity; Software; Transcriptome - genetics
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-020-17281-7

Single-cell RNA-seq (scRNA-seq) is being used widely to resolve cellular heterogeneity. With the rapid accumulation of public scRNA-seq data, an effective and efficient cell-querying method is critical for the utilization of the existing annotations to curate newly sequenced cells. Such a querying method should be based on an accurate cell-to-cell similarity measure, and capable of handling batch effects properly. Herein, we present Cell BLAST, an accurate and robust cell-querying method built on a neural network-based generative model and a customized cell-to-cell similarity metric. Through extensive benchmarks and case studies, we demonstrate the effectiveness of Cell BLAST in annotating discrete cell types and continuous cell differentiation potential, as well as identifying novel cell types. Powered by a well-curated reference database and a user-friendly Web server, Cell BLAST provides the one-stop solution for real-world scRNA-seq cell querying and annotation.
Single-cell RNA-seq (scRNA-seq) is being widely used to resolve cellular heterogeneity. Here, the authors present a cell-querying method built on a neural network-based generative model and a customized cell-to-cell similarity metric.