coseq-package {coseq} | R Documentation

## Co-expression and co-abundance analysis of high-throughput sequencing data

### Description

Co-expression analysis for expression profiles arising from high-throughput sequencing data. Feature (e.g., gene) profiles are clustered using adapted transformations and mixture models or a K-means algorithm, and model selection criteria (to choose an appropriate number of clusters) are provided.

### Details

Package: | coseq
Type: | Package
Version: | 1.15.4
Date: | 2020-12-03
License: | GPL-3
LazyLoad: | yes

### Author(s)

Andrea Rau, Cathy Maugis-Rabusseau, Antoine Godichon-Baggioni

Maintainer: Andrea Rau <andrea.rau@inrae.fr>

### References

Godichon-Baggioni, A., Maugis-Rabusseau, C. and Rau, A. (2018) Clustering transformed compositional data using K-means, with applications in gene expression and bicycle sharing system data. Journal of Applied Statistics, doi:10.1080/02664763.2018.1454894.

Rau, A. and Maugis-Rabusseau, C. (2018) Transformation and model choice for co-expression analayis of RNA-seq data. Briefings in Bioinformatics, 19(3)-425-436.

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(object=countmat, K=2:4, iter=5, transformation="arcsin",
                        model="Normal", seed=12345)
    run_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(object=countmat, K=2:4, iter=5, transformation="logit",
                       model="Normal")
    compareICL(list(run_arcsin, run_logit))

    ## Use accessor functions to explore results
    clusters(run_arcsin)
    likelihood(run_arcsin)
    nbCluster(run_arcsin)
    ICL(run_arcsin)

    ## Examine transformed counts and profiles used for graphing
    tcounts(run_arcsin)
    profiles(run_arcsin)

    ## Run the K-means algorithm for logclr profiles for K = 2,..., 20
    run_kmeans <- coseq(object=countmat, K=2:20, transformation="logclr",
                        model="kmeans")
    run_kmeans

[Package coseq version 1.18.0 Index]