cytof_cluster: Subset detection by clustering

In haoeric/cytofkit_devel: cytofkit: an integrated mass cytometry data analysis pipeline

Description

Apply clustering algorithms to detect cell subsets. DensVM and ClusterX clustering is based on the transformend ydata and use xdata to train the model. While Rphenograph directly works on the high dimemnional 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"), FlowSOM_k = 40)

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.
FlowSOM_k	Number of clusters for meta clustering in FlowSOM.

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, sep = "\t", header = TRUE)[, 1])
xdata <- cytof_exprsMerge(fcsFile, markers = markers, mergeMethod = 'fixed', fixedNum = 100)
ydata <- cytof_dimReduction(xdata, method = "tsne")
clusters <- cytof_cluster(ydata, xdata, method = "ClusterX")

haoeric/cytofkit_devel documentation built on May 17, 2019, 2:29 p.m.