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

• Published in: BMC biology Vol. 21; no. 1; p. 159
• Main Authors: Liu, Chonghui, Zhang, Yan, Gao, Xin, Wang, Guohua
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 19.07.2023; BioMed Central; BMC
• Subjects: Algorithms; Biological properties; Breast cancer; Breast carcinoma; Cancer; Cell populations; Cells; Cluster analysis; Clustering; COVID-19; Datasets; Disease; Gene expression; Gene Expression Profiling - methods; Gene sequencing; Genotype & phenotype; Health aspects; Identification and classification; Identification methods; Immunotherapy; Medical genetics; Medical research; Medicine, Experimental; Methodology; Methods; Performance evaluation; Phenotype; Phenotypes; PPI; RNA sequencing; Sequence Analysis, RNA - methods; Single-cell; Single-Cell Analysis - methods; Single-Cell Gene Expression Analysis; Spatial data; Subpopulations; Tissue analysis; Transcriptomics; China; COVID-19; Phenotype; Spatial transcriptomics; Immunotherapy; PPI; Single-cell; Cancer
• ISSN: 1741-7007; 1741-7007
• DOI: 10.1186/s12915-023-01658-3

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. 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. 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.