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

In coseq: Co-Expression Analysis of Sequencing Data

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 'RangedSummarizedExperiment'
ICL(object)

## S4 method for signature 'MixmodCluster'
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

coseq documentation built on Nov. 8, 2020, 5:18 p.m.