BISCUIT is an R tool to normalize and cluster single-cell RNA-seq data. The BISCUIT algorithm uses a hierarchical Bayesian mixture model that scales related to each cell, facilitating iterative normalization and clustering of cells, and reduces experimental variation. The algorithm also applies a scalable Gibbs inference algorithm to enhance the deduction of cluster building.
Gene expression; RNA-seq
Prabhakaran S, Rey M, Zagordi O, Beerenwinkel N, Roth V. "HIV Haplotype Inference Using a Propagating Dirichlet Process Mixture Model." IEEE/ACM Trans Comput Biol Bioinform. 2014 Jan-Feb;11(1):182-91. https://doi.org/10.1109/TCBB.2013.145
PMID: 26355517
Sandhya Prabhakaran, Elham Azizi, Ambrose Carr, Dana Pe’er "Dirichlet Process Mixture Model for Correcting Technical Variation in
Single-Cell Gene Expression Data" Proceedings of the 33 rd International Conference on Machine Learning, New York, NY, USA, 2016. JMLR: W&CP volume 48.
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