qt.plot {CNVtools} | R Documentation |
Makes signal vs trait and formatted density plots from the data frame returned by CNVtest.qt
qt.plot(DataFrame.list, main='', hist.or.dens='histogram')
DataFrame.list |
The output obtained from the CNVtools fitting algorithm CNVtest.qt |
main |
Potential title for the graph |
hist.or.dens |
Either 'histogram' or 'density' to plot the data as an histogram or using a kernel density estimator |
Vincent Plagnol vincent.plagnol@cimr.cam.ac.uk and Chris Barnes christopher.barnes@imperial.ac.uk
#Load data for CNV for two control cohorts data(A112) raw.signal <- as.matrix(A112[, -c(1,2)]) dimnames(raw.signal)[[1]] <- A112$subject #Extract CNV signal using principal components pca.signal <- apply.pca(raw.signal) #Extract batch, sample sample <- factor(A112$subject) batches <- rep("ALL",length(sample)) #Create a fake quantitative trait trait <- rnorm(length(sample),mean=9.0,sd=1.0) #Fit the CNV with a three component model fit.pca <- CNVtest.qt(signal = pca.signal, sample = sample, batch = batches, qt = trait, ncomp = 3, n.H0=3, n.H1=3, model.qt = "~ cn") qt.plot(fit.pca)