Latent Factor Modeling of scRNA-Seq Data Uncovers Dysregulated Pathways in Autoimmune Disease Patients

• Main Authors: Giovanni Palla, Enrico Ferrero
• Format: Article
• Language: English
• Published: Elsevier 2020-09-01
• Series: iScience
• Subjects: Immunology; Bioinformatics; Transcriptomics
• Online Access: http://www.sciencedirect.com/science/article/pii/S258900422030643X

Summary: Latent factor modeling 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 patients with rheumatoid arthritis and systemic lupus erythematosus, we discover disease-relevant gene signatures in specific cellular subsets. In rheumatoid arthritis, we identify an inflammatory OSMR signaling 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.