Sincast: a computational framework to predict cell identities in single-cell transcriptomes using bulk atlases as references. Issue 3 (31st March 2022)

• Record Type: Journal Article
• Authors: Deng, Yidi; Choi, Jarny; Lê Cao, Kim-Anh
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 3(2022)
• Publication Date: 2022-03-31
• Subjects: scRNA-seq -- RNA-seq -- pseudo-bulk -- imputation -- cell identity prediction; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac088
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Characterizing the molecular identity of a cell is an essential step in single-cell RNA sequencing (scRNA-seq) data analysis. Numerous tools exist for predicting cell identity using single-cell reference atlases. However, many challenges remain, including correcting for inherent batch effects between reference and query data andinsufficient phenotype data from the reference. One solution is to project single-cell data onto established bulk reference atlases to leverage their rich phenotype information. Sincast is a computational framework to query scRNA-seq data by projection onto bulk reference atlases. Prior to projection, single-cell data are transformed to be directly comparable to bulk data, either with pseudo-bulk aggregation or graph-based imputation to address sparse single-cell expression profiles. Sincast avoids batch effect correction, and cell identity is predicted along a continuum to highlight new cell states not found in the reference atlas. In several case study scenarios, we show that Sincast projects single cells into the correct biological niches in the expression space of the bulk reference atlas. We demonstrate the effectiveness of our imputation approach that was specifically developed for querying scRNA-seq data based on bulk reference atlases. We show that Sincast is an efficient and powerful tool for single-cell profiling that will facilitate downstream analysis of scRNA-seq data.