scSTAR reveals hidden heterogeneity with a real-virtual cell pair structure across conditions in single-cell RNA sequencing data

Jie Hao, Jiawei Zou, Jiaqiang Zhang, Ke Chen, Duojiao Wu, Wei Cao, Guoguo Shang, Jean Y H Yang, KongFatt Wong-Lin, Hourong Sun, Zhen Zhang, Xiangdong Wang, Wantao Chen, Xin Zou

Research output: Contribution to journalArticlepeer-review

Abstract

Cell-state transition can reveal additional information from single-cell ribonucleic acid (RNA)-sequencing data in time-resolved biological phenomena. However, most of the current methods are based on the time derivative of the gene expression state, which restricts them to the short-term evolution of cell states. Here, we present single-cell State Transition Across-samples of RNA-seq data (scSTAR), which overcomes this limitation by constructing a paired-cell projection between biological conditions with an arbitrary time span by maximizing the covariance between two feature spaces using partial least square and minimum squared error methods. In mouse ageing data, the response to stress in CD4+ memory T cell subtypes was found to be associated with ageing. A novel Treg subtype characterized by mTORC activation was identified to be associated with antitumour immune suppression, which was confirmed by immunofluorescence microscopy and survival analysis in 11 cancers from The Cancer Genome Atlas Program. On melanoma data, scSTAR improved immunotherapy-response prediction accuracy from 0.8 to 0.96.
Original languageEnglish
Pages (from-to)1-13
Number of pages14
JournalBriefings in Bioinformatics
Volume24
Issue number2
Early online date22 Feb 2023
DOIs
Publication statusPublished online - 22 Feb 2023

Bibliographical note

© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Keywords

  • Molecular Biology
  • Information Systems
  • scRNA-seq
  • partial least square regression
  • cell state dynamics

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