ArticleBiostatistics (Oxford, England)2022
A sparse negative binomial mixture model for clustering RNA-seq count data.
Article in Biostatistics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Poisson Beta Regression for Count Data With an Application to Hospital Length of Stay Data.Statistics in medicine · 2025Article
- OUTCOME-GUIDED DISEASE SUBTYPING BY GENERATIVE MODEL AND WEIGHTED JOINT LIKELIHOOD IN TRANSCRIPTOMIC APPLICATIONS.The annals of applied statistics · 2024Article
- Sample Size Requirements to Test Subgroup-Specific Treatment Effects in Cluster-Randomized Trials.Prevention science : the official journal of the Society for Prevention Research · 2024Article
- Transcriptomic pan-cancer analysis using rank-based Bayesian inference.Molecular oncology · 2023Article
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5 authors.
Funding
Abstract
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 a Gaussian assumption. In this article, 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 $p$). A modified 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 the superior performance of the proposed count data model in clustering accuracy, feature selection, and biological interpretation in pathways.
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