Differential expression and co-expression reveal cell types relevant to rare disease phenotypes

Abstract

Disease phenotypes, serving as valuable descriptors for delineating the spectrum of human pathologies, play a critical role in understanding disease mechanisms. Integration of these phenotypes with single-cell RNA sequencing (scRNA-seq) data facilitates the elucidation of potential associations between phenotypes and specific cell types underlying them, which sheds light on the underlying physiological processes related to these phenotypes. In this study, we utilized scRNA-seq data to infer potential associations between rare disease phenotypes and cell types. Differential expression and co-expression analyses of genes linked to abnormal phenotypes were employed as metrics to identify the involved cell types. Comparative assessments were made against existing phenotype-cell type associations documented in the literature. Our findings underscore the utility of differential expression and co-expression analyses in identifying significant relationships. Moreover, co-expression analysis unveils cell types potentially linked to abnormal phenotypes not extensively characterized in prior studies. Key points- Cell types underling rare disease phenotypes remain largely unknown - Single-cell RNA-seq data from healthy tissues can be analyzed to reveal these cell types - We employed differential expression and co-expression analysis to identify cell types associated with rare disease phenotypes - We validated our results with known relations described in the literature