Description Usage Arguments Author(s) Examples
Makes signal vs trait and formatted density plots from the data frame returned by CNVtest.qt
1 | qt.plot(DataFrame.list, main='', hist.or.dens='histogram')
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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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | #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)
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