cytof_cluster: Subset detection by clustering
In raman91/cytofkit2: cytofkit: an integrated mass cytometry data analysis pipeline

Description Usage Arguments Value Examples

Apply clustering algorithms to detect cell subsets. DensVM and ClusterX clustering is based on the transformed ydata and uses xdata to train the model. Rphenograph directly works on high dimensional xdata. FlowSOM is integrated from FlowSOM pacakge (https://bioconductor.org/packages/release/bioc/html/FlowSOM.html).

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cytof_cluster(ydata = NULL, xdata = NULL, method = c("Rphenograph",
  "ClusterX", "DensVM", "FlowSOM", "NULL"), Rphenograph_k = 30,
  FlowSOM_k = 40, flowSeed = NULL)

`ydata`	A matrix of the dimension reduced data.
`xdata`	A matrix of the expression data.
`method`	Cluster method including `DensVM`, `densityClustX`, `Rphenograph` and `FlowSOM`.
`Rphenograph_k`	Integer number of nearest neighbours to pass to Rphenograph.
`FlowSOM_k`	Number of clusters for meta clustering in FlowSOM.
`flowSeed`	Integer to set a seed for FlowSOM for reproducible results.

a vector of the clusters assigned for each row of the ydata

d<-system.file('extdata', package='cytofkit2')
fcsFile <- list.files(d, pattern='.fcs$', full=TRUE)
parameters <- list.files(d, pattern='.txt$', full=TRUE)
markers <- as.character(read.table(parameters, header = FALSE)[, 1])
xdata <- cytof_exprsMerge(fcsFile, mergeMethod = 'fixed', fixedNum = 100)
ydata <- cytof_dimReduction(xdata, markers = markers, method = "tsne")
clusters <- cytof_cluster(ydata, xdata, method = "ClusterX")