Authors: Andrea Rau and Cathy Maugis-Rabusseau
Gaussian and Poisson mixture models are implemented to cluster gene expression profiles from high-throughput sequencing data. Parameter estimation is performed using the EM algorithm and model selection criteria (to choose the number of clusters and data transformation) are provided.
A typical call to coseq to fit a Gaussian mixture model on arcsin- or logit-transformed normalized RNA-seq profiles takes the following form:
library(coseq)
run_arcsin <- coseq(counts, K=2:10, model="Normal", transformation="arcsin")
run_logit <- coseq(counts, K=2:10, model="Normal", transformation="logit")
where counts represents a (n×q) matrix or data frame of read counts for n genes in q samples
and K=2:10 provides the desired range of numbers of clusters (here, 2 to 10). We note that
this function directly calls the Rmixmod R package to fit Gaussian mixture models.
The output of the coseq function is an
S3 object on which standard plot and summary functions can be directly applied; the former
uses functionalities from the ggplot2 package. The option of parallelization
via the BiocParallel Bioconductor package is also provided.
Rau, A. and Maugis-Rabusseau, C. (2016) Transformation and model choice for co-expression analayis of RNA-seq data. bioRxiv, doi: http://dx.doi.org/10.1101/065607.
The coseq package is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License, version 3, as published by the Free Software Foundation.
This program is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. See the GNU General Public License for more details.
A copy of the GNU General Public License, version 3, is available at http://www.r-project.org/Licenses/GPL-3.