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

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 m...