View source: R/cKmeansWrapperSubsample.R
cKmeansWrapperSubsample | R Documentation |
This fuction is a wrapper for the constrained Kmeans algorithm using 'lcvqe' from the 'conclust' package. This function will subset each cohort down to that with the smallest number of observations.This function is not meant to be run individually, but as a 'clustFunc' argument for running 'K2preproc()', 'runK2Taxonomer()', and 'K2tax()'.
cKmeansWrapperSubsample(dataMatrix, clustList)
dataMatrix |
An P x C numeric matrix of data. Where C is the number of cohort labels. |
clustList |
List of objects to use for clustering procedure.
|
A character string of concatenated 1's and 2's pertaining to the cluster assignment of each column in dataMatrix.
reed_2020K2Taxonomer \insertRefcKmK2Taxonomer
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)
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