runKM | R Documentation |
Executes k-means clustering on multiple subsets of data defined by singular value decomposition (SVD) components, and then aggregates the results into a consensus matrix.
runKM(
logX,
v,
maxSets = 8,
k,
consMin = 0.75,
kmNStart,
kmMax = 1000,
BPPARAM = bpparam()
)
logX |
A (sparse or dense) numeric matrix representing the transpose of a log-normalized gene expression matrix. Rows correspond to cells, and columns correspond to genes. |
v |
A matrix of right singular vectors obtained from SVD of a distance matrix derived from 'logX'. |
maxSets |
(Optional) The maximum number of sub-datasets used for
consensus clustering (default: |
k |
(Optional) The number of clusters (cell groups) in the data. If not provided, it is estimated using the Tracy-Widom Bound. |
consMin |
(Optional) The low-pass filter threshold for processing the
consensus matrix (default: |
kmNStart |
nstart parameter passed to |
kmMax |
iter.max parameter passed to |
BPPARAM |
(Optional) A |
A consensus matrix summarizing the clustering results across multiple sub-datasets.
library(scater)
library(BiocParallel)
library(splatter)
sce <- splatSimulate(group.prob = rep(1, 5)/5, sparsify = FALSE,
batchCells=100, nGenes=1000, method = "groups", verbose = FALSE,
dropout.type = "experiment")
sce <- logNormCounts(sce)
cores <- 2
logX <- as.matrix(logcounts(sce))
w <- rowVars_fast(logX, cores)
corMat <- getCorM("spearman", logcounts(sce), w, cores)
v <- doSVD(corMat, nCores=cores)
BPPARAM = MulticoreParam(cores)
consMtx <- runKM(logX, v, BPPARAM=bpparam())
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