coseq-package: Co-expression and co-abundance analysis of high-throughput...

In coseq: Co-Expression Analysis of Sequencing Data

Description

Mixture models are implemented to cluster genes from high-throughput transcriptome sequencing (RNA-seq) data. Parameter estimation is performed using the EM algorithm, and model selection is performed using either the slope heuristics or the integrated completed likelihood (ICL) criterion.

Details

Package:	coseq
Type:	Package
Version:	0.1.12
Date:	2016-09-23
License:	GPL (>=3)
LazyLoad:	yes

Author(s)

Andrea Rau, Cathy Maugis-Rabusseau

Maintainer: Andrea Rau <[e-mail]>

References

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.

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. Bioinformatics, doi: 10.1093/bioinformatics/btu845.

Rau, A., Celeux, G., Martin-Magniette, M.-L., Maugis-Rabusseau, C. (2011) Clustering high-throughput sequencing data with Poisson mixture models. Inria Research Report 7786. Available at http://hal.inria.fr/inria-00638082.

Examples

## Simulate toy data, n = 300 observations
set.seed(12345)
countmat <- matrix(runif(300*4, min=0, max=500), nrow=300, ncol=4)
countmat <- countmat[which(rowSums(countmat) > 0),]
conds <- rep(c("A","B","C","D"), each=2)

## Run the Normal mixture model for K = 2,3,4
run_arcsin <- coseq(y=countmat, K=2:4, iter=5, transformation="arcsin")

## Plot and summarize results
plot(run_arcsin)
summary(run_arcsin)

## Compare ARI values for all models (no plot generated here)
ARI <- compareARI(run_arcsin, plot=FALSE)

## Compare ICL values for models with arcsin and logit transformations
run_logit <- coseq(y=countmat, K=2:4, iter=5, transformation="logit")
compareICL(list(run_arcsin, run_logit))

coseq documentation built on May 2, 2019, 4:55 p.m.