A sparse negative binomial mixture model for clustering RNA-seq count data
This work addresses a domain-specific problem for bioinformatics researchers by providing a more accurate clustering method for RNA-seq data, though it is incremental as it adapts existing mixture model frameworks to count data.
The authors tackled the problem of clustering high-dimensional RNA-seq count data by developing a negative binomial mixture model with gene regularization, which showed superior clustering accuracy, feature selection, and biological interpretation compared to existing methods in simulations and real applications.
Clustering with variable selection is a challenging yet critical task for modern small-n-large-p data. Existing methods based on sparse Gaussian mixture models or sparse K-means provide solutions to continuous data. With the prevalence of RNA-seq technology and lack of count data modeling for clustering, the current practice is to normalize count expression data into continuous measures and apply existing models with Gaussian assumption. In this paper, we develop a negative binomial mixture model with lasso or fused lasso gene regularization to cluster samples (small n) with high-dimensional gene features (large p). EM algorithm and Bayesian information criterion are used for inference and determining tuning parameters. The method is compared with existing methods using extensive simulations and two real transcriptomic applications in rat brain and breast cancer studies. The result shows superior performance of the proposed count data model in clustering accuracy, feature selection and biological interpretation in pathways.