summary,coseqResults-method {coseq} | R Documentation

## Summarize results from coseq clustering

### Description

A function to summarize the clustering results obtained from a Poisson or Gaussian mixture model estimated using `coseq`. In particular, the function provides the number of clusters selected for the ICL model selection approach (or alternatively, for the capushe non-asymptotic approach if K-means clustering is used), number of genes assigned to each cluster, and if desired the per-gene cluster means.

### Usage

    ## S4 method for signature 'coseqResults'
    summary(object, y_profiles, digits = 3, ...)

### Arguments

`object` | An object of class `"coseqResults"`
`y_profiles` | Data used for clustering if per-cluster means are desired
`digits` | Integer indicating the number of decimal places to be used for mixture model parameters
`...` | Additional arguments

### Details

Provides the following summary of results:

1) Number of clusters and model selection criterion used, if applicable.

2) Number of observations across all clusters with a maximum conditional probability greater than 90 observations) for the selected model.

3) Number of observations per cluster with a maximum conditional probability greater than 90 cluster) for the selected model.

4) If desired, the μ values and π values for the selected model in the case of a Gaussian mixture model.

### Value

Summary of the `coseqResults` object.

### Author(s)

Andrea Rau

### References

Rau, A. and Maugis-Rabusseau, C. (2017) Transformation and model choice for co-expression analayis of RNA-seq data. Briefings in Bioinformatics, doi: http://dx.doi.org/10.1101/065607.

Godichon-Baggioni, A., Maugis-Rabusseau, C. and Rau, A. (2017) Clustering transformed compositional data using K-means, with applications in gene expression and bicycle sharing system data. arXiv:1704.06150.

### See Also

`coseq`

### 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 1.16.0 Index]