coseqHelpers: Accessors for the assigned cluster labels of a coseqResults...

Description Usage Arguments Value Author(s) Examples

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

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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

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## 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

andreamrau/coseq documentation built on July 25, 2021, 10:17 a.m.