Skip to main content

Hierarchical non-parametric Bayesian clustering of digital expression data

Project description

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](http://bioinformatics.bris.ac.uk/) 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. http://www.genomebiology.com/2015/16/1/39 (Vavoulis et al., Genome Biology 16:39, 2015)
  2. http://arxiv.org/abs/1301.4144 (Vavoulis & Gough, J Comput Sci Syst Biol 7:001-009, 2013)

For more information, including bug reports, send an email to <Dimitris.Vavoulis@ndcls.ox.ac.uk> or <Julian.Gough@bristol.ac.uk>

Enjoy!

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

DGEclust-17.10.16.tar.gz (10.9 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page