Identification of cell subpopulations associated with disease phenotypes from scRNA-seq data using PACSI

Chonghui Liu, Yan Zhang, Xin Gao, Guohua Wang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Background: Single-cell RNA sequencing (scRNA-seq) has revolutionized the transcriptomics field by advancing analyses from tissue-level to cell-level resolution. Despite the great advances in the development of computational methods for various steps of scRNA-seq analyses, one major bottleneck of the existing technologies remains in identifying the molecular relationship between disease phenotype and cell subpopulations, where “disease phenotype” refers to the clinical characteristics of each patient sample, and subpopulation refer to groups of single cells, which often do not correspond to clusters identified by standard single-cell clustering analysis. Here, we present PACSI, a method aimed at distinguishing cell subpopulations associated with disease phenotypes at the single-cell level. Results: PACSI takes advantage of the topological properties of biological networks to introduce a proximity-based measure that quantifies the correlation between each cell and the disease phenotype of interest. Applied to simulated data and four case studies, PACSI accurately identified cells associated with disease phenotypes such as diagnosis, prognosis, and response to immunotherapy. In addition, we demonstrated that PACSI can also be applied to spatial transcriptomics data and successfully label spots that are associated with poor survival of breast carcinoma. Conclusions: PACSI is an efficient method to identify cell subpopulations associated with disease phenotypes. Our research shows that it has a broad range of applications in revealing mechanistic and clinical insights of diseases.
Original languageEnglish (US)
JournalBMC biology
Volume21
Issue number1
DOIs
StatePublished - Jul 19 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-07-24
Acknowledged KAUST grant number(s): FCC/1/1976-44-01, FCC/1/1976-45-01, REI/1/4940-01-01, REI/1/5202-01-01, RGC/3/4816-01-01, URF/1/4663-01-01
Acknowledgements: This work was supported by the following funding: the National Key R&D Program of China (2021YFC2100101); the National Natural Science Foundation of China (62072095, 62225109); the Fundamental Research Funds for the Central Universities (2572021CG03); the King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA) under Award No FCC/1/1976-44-01, FCC/1/1976-45-01, URF/1/4663-01-01, REI/1/5202-01-01, REI/1/4940-01-01, and RGC/3/4816-01-01.

ASJC Scopus subject areas

  • General Agricultural and Biological Sciences
  • General Biochemistry, Genetics and Molecular Biology

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