cytofkit CD4+ T cell analysis

require(cytofkit)

## Loading required package: cytofkit

## Loading required package: ggplot2

## Loading required package: plyr

Merge expresssion data

## down sample 10000 cells from each file using method ceil
files <- list.files(path="./CD4posTcell_dataset/", 
                    pattern = ".fcs$", full.names = TRUE)
markers <- as.character(read.table("./CD4posTcell_dataset/parameter.txt", 
                                   header = TRUE)[,1])
xdata <- cytof_exprsMerge(fcsFiles = files, markers = markers,
                          transformMethod = "autoLgcl", 
                          mergeMethod = "ceil", fixedNum = 10000)

Run t-SNE

ydata <- cytof_dimReduction(data = xdata, method = "tsne")

Clustering Analysis

ClusterX

cx_cluster <- cytof_cluster(ydata = ydata, xdata = xdata, method = "ClusterX")

DensVM

dv_cluster <- cytof_cluster(ydata = ydata, xdata = xdata, method = "DensVM")

PhenoGraph

pg_cluster <- cytof_cluster(ydata = ydata, xdata = xdata, method = "Rphenograph")

Clustering Plot

aData <- cbind(xdata, ydata)
aData$DensVM <- factor(dv_cluster)
aData$ClusterX <- factor(cx_cluster)
aData$PhenoGraph <- factor(pg_cluster)

ClusterX

cytof_clusterPlot(data = aData, xlab = "tsne_1", ylab = "tsne_2", 
                  cluster = "ClusterX", sampleLabel = FALSE)

DensVM

cytof_clusterPlot(data = aData, xlab = "tsne_1", ylab = "tsne_2", 
                  cluster = "DensVM", sampleLabel = FALSE)

PhenoGraph

cytof_clusterPlot(data = aData, xlab = "tsne_1", ylab = "tsne_2", 
                  cluster = "PhenoGraph", sampleLabel = FALSE)