In:
Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 23, No. 2 ( 2022-03-10)
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.
Type of Medium:
Online Resource
ISSN:
1467-5463
,
1477-4054
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2022
detail.hit.zdb_id:
2036055-1
SSG:
12
Permalink