BREM-SC

"BREM-SC" (Bayesian Random Effects Mixture model for Single Cell data) is a computational tool to address the challenges of analyzing multi-modal CITE-Seq data, which integrates transcriptomic and proteomic information at the single-cell level. This novel Bayesian Random Effects Mixture model is specifically developed to jointly cluster paired single-cell transcriptomic and proteomic data, thereby enhancing the accuracy of cell cluster identification by leveraging the complementary strengths of both data types.

Key Features and Functionalities:

- Joint Clustering of Transcriptomic and Proteomic Data: BREM-SC's core functionality is its ability to perform joint clustering of transcriptomic and proteomic data obtained from single cells. This approach allows for a more comprehensive understanding of cell states and identities by integrating two major aspects of cellular function.

- Quantification of Clustering Uncertainty: As a probabilistic model-based approach, BREM-SC can quantify the uncertainty associated with the clustering of each single cell. This feature is particularly valuable for assessing cluster assignments' reliability and identifying cells that may not clearly belong to any single cluster.