Dataset Information

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

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.

SUBMITTER: Palla G

PROVIDER: S-EPMC7452208 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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

Palla Giovanni G Ferrero Enrico E

iScience 20200812 9

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 arthr ...[more]

PMID: 32853994

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Project description:BackgroundSingle-cell RNA sequencing (scRNA-seq) has emerged has a main strategy to study transcriptional activity at the cellular level. Clustering analysis is routinely performed on scRNA-seq data to explore, recognize or discover underlying cell identities. The high dimensionality of scRNA-seq data and its significant sparsity accentuated by frequent dropout events, introducing false zero count observations, make the clustering analysis computationally challenging. Even though multiple scRNA-seq clustering techniques have been proposed, there is no consensus on the best performing approach. On a parallel research track, self-supervised contrastive learning recently achieved state-of-the-art results on images clustering and, subsequently, image classification.ResultsWe propose contrastive-sc, a new unsupervised learning method for scRNA-seq data that perform cell clustering. The method consists of two consecutive phases: first, an artificial neural network learns an embedding for each cell through a representation training phase. The embedding is then clustered in the second phase with a general clustering algorithm (i.e. KMeans or Leiden community detection). The proposed representation training phase is a new adaptation of the self-supervised contrastive learning framework, initially proposed for image processing, to scRNA-seq data. contrastive-sc has been compared with ten state-of-the-art techniques. A broad experimental study has been conducted on both simulated and real-world datasets, assessing multiple external and internal clustering performance metrics (i.e. ARI, NMI, Silhouette, Calinski scores). Our experimental analysis shows that constastive-sc compares favorably with state-of-the-art methods on both simulated and real-world datasets.ConclusionOn average, our method identifies well-defined clusters in close agreement with ground truth annotations. Our method is computationally efficient, being fast to train and having a limited memory footprint. contrastive-sc maintains good performance when only a fraction of input cells is provided and is robust to changes in hyperparameters or network architecture. The decoupling between the creation of the embedding and the clustering phase allows the flexibility to choose a suitable clustering algorithm (i.e. KMeans when the number of expected clusters is known, Leiden otherwise) or to integrate the embedding with other existing techniques.

Dataset Information

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

Publications

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

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