Clustering High-Throughput Transcriptome Sequencing (HTS) Data

A Poisson mixture model is implemented to cluster genes from high- throughput transcriptome sequencing (RNA-seq) data. Parameter estimation is performed using either the EM or CEM algorithm, and the slope heuristics are used for model selection (i.e., to choose the number of clusters).

HTSCluster: Clustering High-Throughput Transcriptome Sequencing (HTS) Data with Poisson Mixture Models

This is the development version of the HTSCluster R package. The current, stable version of HTSCluster is available on the CRAN. If you make use of HTSCluster in your work, please cite our paper:

Rau, A., Maugis-Rabusseau, C., Martin-Magniette, M.-L., Celeux, G. (2015) Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models. Bioinformatic, 31(9): 1420-1427. [link]