A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings. Issue 2 (20th January 2022)

• Record Type: Journal Article
• Authors: Shen, Hongru; Shen, Xilin; Feng, Mengyao; Wu, Dan; Zhang, Chao; Yang, Yichen; Yang, Meng; Hu, Jiani; Liu, Jilei; Wang, Wei; Li, Yang; Zhang, Qiang; Yang, Jilong; Chen, Kexin; Li, Xiangchun
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 2(2022)
• Publication Date: 2022-01-20
• Subjects: deep learning -- gene ranking -- single-cell transcriptomes; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab573
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Advancement in single-cell RNA sequencing leads to exponential accumulation of single-cell expression data. However, there is still lack of tools that could integrate these unlimited accumulations of single-cell expression data. Here, we presented a universal approach iSEEEK for integrating super large-scale single-cell expression via exploring expression rankings of top-expressing genes. We developed iSEEEK with 11.9 million single cells. We demonstrated the efficiency of iSEEEK with canonical single-cell downstream tasks on five heterogenous datasets encompassing human and mouse samples. iSEEEK achieved good clustering performance benchmarked against well-annotated cell labels. In addition, iSEEEK could transfer its knowledge learned from large-scale expression data on new dataset that was not involved in its development. iSEEEK enables identification of gene–gene interaction networks that are characteristic of specific cell types. Our study presents a simple and yet effective method to integrate super large-scale single-cell transcriptomes and would facilitate translational single-cell research from bench to bedside.