treeGate {CytoDx} | R Documentation |
A function that sse decision tree to find a group of cells that are associated with clinical outcome.
treeGate(P, x, ...)
P |
The predicted association of each cell with a clinical outcome. |
x |
The marker profile of each cell. Each row is a cell, each column is a marker. Must have length(P) rows. |
... |
Other parameters to be passed into the rpart function |
Returns a object created by rpart function. Also plots a graph of decision tree.
# Find the table containing fcs file names in CytoDx package path=system.file("extdata",package="CytoDx") # read the table fcs_info <- read.csv(file.path(path,"fcs_info.csv")) # Specify the path to the cytometry files fn <- file.path(path,fcs_info$fcsName) # Read cytometry files using fcs2DF function train_data <- fcs2DF(fcsFiles=fn, y=fcs_info$Label, assay="FCM", b=1/150, excludeTransformParameters= c("FSC-A","FSC-W","FSC-H","Time")) # build the model fit <- CytoDx.fit(x=as.matrix(train_data[,1:7]), y=train_data$y, xSample = train_data$xSample, reg=FALSE, family="binomial") # check accuracy for training data pred <- CytoDx.pred(fit, xNew=as.matrix(train_data[,1:7]), xSampleNew=train_data$xSample) boxplot(pred$xNew.Pred.sample$y.Pred.s0~ fcs_info$Label) # Find the associated population using treeGate TG <- treeGate(P = fit$train.Data.cell$y.Pred.s0, x= train_data[,1:7])