HTSCluster

HTSCluster clusters observations (such as genes) in high-throughput sequencing digital gene expression (DGE) datasets using a Poisson mixture model to identify groups of co-expressed genes for gene expression analysis.

Key Features:

• Poisson Mixture Model: Employs a Poisson mixture model to identify groups of co-expressed genes from DGE profiles, modeling count data typical of RNA-sequencing experiments.
• Parameter Estimation Algorithms: Provides Expectation-Maximization (EM) and Classification EM (CEM) algorithms for estimation of mixture model parameters.
• Model Selection via Slope Heuristics: Uses slope heuristics to determine the optimal number of clusters in the mixture model.

Scientific Applications:

• Digital Gene Expression (DGE) Clustering: Clusters RNA-seq DGE profiles to discover co-expressed genes.
• Gene Regulatory Network Inference: Facilitates identification of co-expressed gene groups that inform gene regulatory network analyses.
• Functional Genomics: Supports functional genomics studies by grouping genes with similar expression patterns across conditions or samples.
• Developmental Biology and Disease Research: Applies to RNA-seq studies in developmental biology and disease contexts to reveal coordinated expression programs.
• Evolutionary Studies: Enables comparison of expression-based gene clusters across species or conditions for evolutionary analyses.

Methodology:

Uses a Poisson mixture model with parameter estimation via EM and CEM algorithms, model selection by slope heuristics, and performance evaluation through simulation studies comparing to other clustering approaches.