ArticleiScience2020
Latent Factor Modeling of scRNA-Seq Data Uncovers Dysregulated Pathways in Autoimmune Disease Patients.
Article in iScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 6 citations in OpenAlex.
- Towards the Next Generation of Data-Driven Therapeutics Using Spatially Resolved Single-Cell Technologies and Generative AI.European journal of immunology · 2025Review
- Uncovering hidden factors of cognitive resilience in Alzheimer's disease using a conditional-Gaussian mixture variational autoencoder.NPJ dementia · 2025Article
- Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS.Nature protocols · 2023Review
- Single-Cell Sequencing in Rheumatic Diseases: New Insights from the Perspective of the Cell Type.Aging and disease · 2022Article
- Forecasting cellular states: from descriptive to predictive biology via single-cell multiomics.Current opinion in systems biology · 2021Article
- Insights From Single Cell RNA Sequencing Into the Immunology of Type 1 Diabetes- Cell Phenotypes and Antigen Specificity.Frontiers in immunology · 2021Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.