cytof_cluster {cytofkit} | R Documentation

## Subset detection by clustering

### Description

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).

### Usage

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

### Arguments

`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.

### Value

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

### Examples

    d<-system.file('extdata', package='cytofkit')
    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")

[Package cytofkit version 1.12.0 Index]