Probabilistic Clustering of Cells Using Single-Cell RNA-Seq Data

Clustering cells into homogeneous subpopulations from single-cell RNA-seq data is a core step in analyzing cellular heterogeneity, but existing clustering methods perform inconsistently across datasets and are rarely probabilistic, making it hard to quantify uncertainty in the resulting clusters. We propose a generative, probabilistic clustering approach for scRNA-seq data that handles dropout events directly and does not require prior marker-gene information.

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Recommended citation: Joy Saha, Ridwanul Hasan Tanvir, Md. Abul Hassan Samee, Atif Rahman. "Probabilistic Clustering of Cells Using Single-Cell RNA-Seq Data." bioRxiv, 2023.
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