superpc.rainbowplot function

Make rainbow plot of superpc and compeiting predictors

Makes a heatmap display of outcome predictions from superpc, along with expected survival time, and values of competing predictors.

superpc.rainbowplot(data, 
                        pred, 
                        sample.labels,
                        competing.predictors,
                        call.win.metafile=FALSE)

Arguments

• data: List of (test) data, of form described in superpc.train documentation
• pred: Superpc score from superpc.predict or superpc.predict.red
• sample.labels: Vector of sample labels of test data
• competing.predictors: List of competing predictors to be plotted
• call.win.metafile: Used only by Excel interface call to function

Details

Any censored survival times are estimated by E(T|T > C), where $C$ is the observed censoring time and the Kaplan-Meier estimate from the training set is used to estimate the expectation.

References

• E. Bair and R. Tibshirani (2004). "Semi-supervised methods to predict patient survival from gene expression data." PLoS Biol, 2(4):e108.
• E. Bair, T. Hastie, D. Paul, and R. Tibshirani (2006). "Prediction by supervised principal components." J. Am. Stat. Assoc., 101(473):119-137.

Author(s)

• "Eric Bair, Ph.D."
• "Jean-Eudes Dazard, Ph.D."
• "Rob Tibshirani, Ph.D."

Maintainer: "Jean-Eudes Dazard, Ph.D."

Examples

set.seed(332)

#generate some data
x <- matrix(rnorm(50*30), ncol=30)
y <- 10 + svd(x[1:50,])$v[,1] + .1*rnorm(30)
ytest <- 10 + svd(x[1:50,])$v[,1] + .1*rnorm(30)
censoring.status <- sample(c(rep(1,20), rep(0,10)))
censoring.status.test <- sample(c(rep(1,20), rep(0,10)))

featurenames <- paste("feature", as.character(1:50), sep="")
competing.predictors.test <- list(pred1=rnorm(30), 
                                  pred2=as.factor(sample(c(1,2),
                                                  replace=TRUE,
                                                  size=30)))
                                                  
data <- list(x=x, 
             y=y, 
             censoring.status=censoring.status, 
             featurenames=featurenames)
data.test <- list(x=x, 
                  y=ytest, 
                  censoring.status=censoring.status.test, 
                  featurenames=featurenames)
sample.labels <- paste("te", as.character(1:20), sep="")

a <- superpc.train(data, type="survival")
pred <- superpc.predict(a, 
                        data, 
                        data.test, 
                        threshold=.25, 
                        n.components=1)$v.pred
superpc.rainbowplot(data, 
                    pred, 
                    sample.labels, 
                    competing.predictors=competing.predictors.test)