## Introduction CytoDx is a method that predicts clinical outcomes using single cell data without the need of cell gating. It first predicts the association between each cell and the outcome using a linear statistical model. The cell level predictions are then averaged within each sample to represent the sample level predictor. A second sample level model is used to make prediction at the sample level. ## Installation To install the MetaCyto package, please run the following code: ``` library("devtools") install_github("hzc363/CytoDx") ``` ## Example Here we provide a simple simulated example. Please see the vignette for an example that applies tssm to diagnos accute myeloid lymphoma using flow cytometry data. ``` library(CytoDx) # simulate 10 samples of class A A = data.frame("marker1"=c(rnorm(1000),rnorm(1000,mean=10)), "marker2"=c(rnorm(1000),rnorm(1000,mean=10)), "sample"=sample(1:10,2000,replace = T), "y"="A") # simulate 10 samples of class B B = data.frame("marker1"=c(rnorm(1500),rnorm(500,mean=10)), "marker2"=c(rnorm(1500),rnorm(500,mean=10)), "sample"=sample(11:20,2000,replace = T), "y"="B") # build CytoDx model dat = rbind(A,B) fit = CytoDx.fit(x = as.matrix(dat[,1:2]), y = (dat$y=="A"), xSample = dat$sample, family = "binomial", reg = F) # predict pred = CytoDx.pred(fit = fit, xNew = as.matrix(dat[,1:2]), xSampleNew = dat$sample) # plot probability result = data.frame("Truth"=rep(c("A","B"),each=10), "Prob"=pred$xNew.Pred.sample$y.Pred.s0) stripchart(result$Prob~result$Truth, jitter = 0.1, vertical = TRUE, method = "jitter", pch = 20, xlab="Truth",ylab="Predicted Prob of A") ```