Latent factor modelling of scRNA-seq data uncovers dysregulated pathways in autoimmune disease patients

Summary Latent factor modelling applied to single-cell RNA-sequencing (scRNA-seq) data is a useful approach to discover gene signatures. However, it is often unclear what methods are best suited for specific tasks and how latent factors should be interpreted. Here, we compare four state-of-the-art methods and propose an approach to assign derived latent factors to pathway activities and specific cell subsets. By applying this framework to scRNA-seq datasets from biopsies of rheumatoid arthritis and systemic lupus erythematosus patients, we discover disease-relevant gene signatures in specific cellular subsets. In rheumatoid arthritis, we identify an inflammatory OSMR signalling signature active in a subset of synovial fibroblasts and an efferocytic signature in a subset of synovial monocytes. Overall, we provide insights into latent factors models for the analysis of scRNA-seq data, develop a framework to identify cell subtypes in a phenotype-driven way, and use it to identify novel pathways dysregulated in rheumatoid arthritis.