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DGEclust 17.10.16

Hierarchical non-parametric Bayesian clustering of digital expression data

DGEclust is a program for clustering and differential expression analysis of digital expression data generated by next-generation sequencing assays, such as RNA-seq, CAGE and others. It takes as input a table of count data and it estimates the number and parameters of the clusters supported by the data. At a later stage, these can be used for identifying differentially expressed genes and for gene- and sample-wise clustering of the original data matrix. Internally, DGEclust uses a Hierarchical Dirichlet Process Mixture Model for modeling over-dispersed count data, combined with a blocked Gibbs sampler for efficient Bayesian learning.

This program is part of the software collection of the [Computational Genomics Group]( at the University of Bristol and it is under continuous development. You can find more technical details on the statistical methodologies used in this software in the following papers:

  1. (Vavoulis et al., Genome Biology 16:39, 2015)
  2. (Vavoulis & Gough, J Comput Sci Syst Biol 7:001-009, 2013)

For more information, including bug reports, send an email to <> or <>


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DGEclust-17.10.16.tar.gz (md5) Source 2017-10-15 10KB