Mining single-cell data for cell type-disease associations.

ABSTRACT: A robust understanding of the cellular mechanisms underlying diseases sets the foundation for the effective design of drugs and other interventions. The wealth of existing single-cell atlases offers the opportunity to uncover high-resolution information on expression patterns across various cell types and time points. To better understand the associations between cell types and diseases, we leveraged previously developed tools to construct a standardized analysis pipeline and systematically explored associations across four single-cell datasets, spanning a range of tissue types, cell types and developmental time periods. We utilized a set of existing tools to identify co-expression modules and temporal patterns per cell type and then investigated these modules for known disease and phenoty

SUBMITTER: Chen KG 

PROVIDER: S-EPMC11655289 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature