coseqFullResults {coseq} | R Documentation

## Accessors for the assigned cluster labels of a coseqResults object.

### Description

The counts slot holds the count data as a matrix of non-negative integer count values, one row for each observational unit (gene or the like), and one column for each sample.

### Usage

    coseqFullResults(object, ...)

    clusters(object, ...)

    likelihood(object, ...)

    nbCluster(object, ...)

    proba(object, ...)

    ICL(object, ...)

    profiles(object, ...)

    tcounts(object, ...)

    transformationType(object, ...)

    model(object, ...)

    DDSEextract(object, ...)

    Djumpextract(object, ...)

    ## S4 method for signature 'coseqResults'
    clusters(object, K)

    ## S4 method for signature 'RangedSummarizedExperiment'
    clusters(object, ...)

    ## S4 method for signature 'matrix'
    clusters(object, ...)

    ## S4 method for signature 'data.frame'
    clusters(object, ...)

    ## S4 method for signature 'MixmodCluster'
    likelihood(object)

    ## S4 method for signature 'RangedSummarizedExperiment'
    likelihood(object)

    ## S4 method for signature 'coseqResults'
    likelihood(object)

    ## S4 method for signature ''NULL''
    likelihood(object)

    ## S4 method for signature 'MixmodCluster'
    nbCluster(object)

    ## S4 method for signature 'RangedSummarizedExperiment'
    nbCluster(object)

    ## S4 method for signature 'coseqResults'
    nbCluster(object)

    ## S4 method for signature ''NULL''
    nbCluster(object)

    ## S4 method for signature 'MixmodCluster'
    ICL(object)

    ## S4 method for signature 'RangedSummarizedExperiment'
    ICL(object)

    ## S4 method for signature 'coseqResults'
    ICL(object)

    ## S4 method for signature ''NULL''
    ICL(object)

    ## S4 method for signature 'coseqResults'
    profiles(object)

    ## S4 method for signature 'coseqResults'
    tcounts(object)

    ## S4 method for signature 'coseqResults'
    transformationType(object)

    ## S4 method for signature 'coseqResults'
    model(object)

    ## S4 method for signature 'coseqResults'
    coseqFullResults(object)

    ## S4 method for signature 'coseqResults'
    show(object)

    ## S4 method for signature 'MixmodCluster'
    proba(object)

    ## S4 method for signature 'Capushe'
    DDSEextract(object)

    ## S4 method for signature 'Capushe'
    Djumpextract(object)

### Arguments

`object` | a `coseqResults`, `RangedSummarizedExperiment`, or `MixmodCluster` object.
`...` | Additional optional parameters
`K` | numeric indicating the model to be used (if NULL of missing, the model chosen by ICL is used by default)

### Value

Output varies depending on the method. `clusters` returns a vector of cluster labels for each gene for the desired model.

### Author(s)

Andrea Rau

### 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]