cKmeansWrapper: Wrapper for constrained K-means

In montilab/K2Taxonomer: Creating annotated submodules of high-throughput data through recursive partitioning

Wrapper for constrained K-means

Description

This fuction is a wrapper for the constrained Kmeans algorithm using 'lcvqe' from the 'conclust' package. This function is not meant to be run individually, but as a 'clustFunc' argument for running 'K2preproc()', 'runK2Taxonomer()', and 'K2tax()'.

Usage

cKmeansWrapper(dataMatrix, clustList)

Arguments

dataMatrix	An P x N numeric matrix of data
clustList	List of objects to use for clustering procedure.

Value

A character string of concatenated 1's and 2's pertaining to the cluster assignment of each column in dataMatrix.

References

\insertRef

reed_2020K2Taxonomer \insertRefcKmK2Taxonomer

Examples

dat <- scRNAseq::ReprocessedAllenData(assays='rsem_tpm')[seq_len(50),]

eSet <- ExpressionSet(assayData=assay(dat))
pData(eSet) <- as.data.frame(colData(dat))
exprs(eSet) <- log2(exprs(eSet) + 1)

## Subset for fewer cluster labels for this example
eSet <- eSet[, !is.na(eSet$Primary.Type) &
            eSet$Primary.Type %in% c('L4 Arf5',
                'L4 Ctxn3', 'L4 Scnn1a', 'L5 Ucma', 'L5a Batf3')]

## Create cell type variable with spaces
eSet$celltype <- gsub(' ', '_', eSet$Primary.Type)

## Create clustList
cL <- list(
    eMat=exprs(eSet),
    labs=eSet$celltype,
    maxIter=10)

## Run K2preproc to generate generate data matrix
## with a column for each celltype.
K2res <- K2preproc(eSet,
                cohorts='celltype',
                featMetric='F',
                logCounts=TRUE)
dm <- K2data(K2res)

## Generate K=2 split with constrained K-means
cKmeansWrapperSubsample(dm, cL)

montilab/K2Taxonomer documentation built on Jan. 25, 2024, 4:29 p.m.