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Celda: A Bayesian model to perform co-clustering of genes into modules and cells into subpopulations using single-cell RNA-seq data

By Zhe Wang, Shiyi Yang, Yusuke Koga, Sean E. Corbett, W. Evan Johnson, Masanao Yajima, Joshua D. Campbell

Posted 17 Nov 2020
bioRxiv DOI: 10.1101/2020.11.16.373274

Single-cell RNA-seq (scRNA-seq) has emerged as a powerful technique to quantify gene expression in individual cells and elucidate the molecular and cellular building blocks of complex tissues. We developed a novel Bayesian hierarchical model called Cellular Latent Dirichlet Allocation (Celda) to perform simultaneous co-clustering of genes into transcriptional modules and cells into subpopulations. Celda can quantify the probabilistic contribution of each gene to each module, each module to each cell population, and each cell population to each sample. We used Celda to identify transcriptional modules and cell subpopulations in a publicly available peripheral blood mononuclear cell (PBMC) dataset. Celda identified a population of proliferating T cells and a single plasma cell which were missed by two other clustering methods. Celda identified transcriptional modules that highlighted unique and shared biological programs across cell types. Celda also outperformed a PCA-based approach for gene clustering on simulated data. Overall, Celda presents a novel statistically principled approach towards characterizing transcriptional programs and cellular heterogeneity in single-cell RNA-seq data.

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