CytoDx.pred: Make prediction using the CytoDx model

In hzc363/CytoDx: Robust prediction of clinical outcomes using cytometry data without cell gating

Description

A function that makes prediction using the CytoDx model.

Usage

CytoDx.pred(fit, xNew, xSampleNew)

Arguments

fit	The two stage statistical model. Must be the object returned by CytoDx.fit.
xNew	The marker profile of cells pooled from all new samples. Each row is a cell, each column is a marker.
xSampleNew	A vector specifying which sample each cell belongs to. Length must equal to nrow(xNew).

Value

Returns a list. xNew.Pred1 contains the predicted y for the new data at the cell level. xNew.Pred2 contains the predicted y for the new data at the sample level.

Examples

# 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$fcsName)
train_data <- fcs2DF(fcsFiles=fn,
                    y=fcs_info$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)

hzc363/CytoDx documentation built on May 8, 2019, 11:56 p.m.