Subtyping of COVID-19 samples based on cell-cell interaction in single cell transcriptomes.

ABSTRACT: In single-cell transcriptome analysis, numerous biomarkers related to COVID-19 severity, including cell subtypes, genes, and pathways, have been identified. Nevertheless, most studies have focused on severity groups based on clinical features, neglecting immunological heterogeneity within the same severity level. In this study, we employed sample-level clustering using cell-cell interaction scores to investigate patient heterogeneity and uncover novel subtypes. The clustering results were validated using external datasets, demonstrating superior reproducibility and purity compared to gene expression- or gene set enrichment-based clustering. Furthermore, the cell-cell interaction score-based clusters exhibited a strong correlation with the WHO ordinal severity score based on clinical charac

SUBMITTER: Jeong K 

PROVIDER: S-EPMC10638268 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature