cnv.plot: Plots posterior probabilty distributions In CNVtools: A package to test genetic association with CNV data

Description

Makes formatted density plots from the posterior data frame(s) returned by CNVtest.binary

Usage

 `1` ```cnv.plot(posterior, hist.or.dens='histogram', batch = NULL, freq = NULL, ...) ```

Arguments

 `posterior` The posterior distribution obtained from the CNVtools fitting algorithm, for example using CNVtest.binary `hist.or.dens` Either 'histogram' or 'density' to plot the data as an histogram or using a kernel density estimator `batch` character vector (usually of length 1, but not always), designing the batches one wants to plot. `freq` This argument is only relevant when hist.or.dens='histogram' (the default). It matches the argument freq of the hist function. With freq = FALSE frequencies, and not raw counts, are shown in the histogram. `...` Usual arguments passed to the hist function, including main or breaks for example.

Author(s)

Vincent Plagnol [email protected] and Chris Barnes [email protected]

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ``` #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 and trait information batches <- factor(A112\$cohort) sample <- factor(A112\$subject) trait <- ifelse( A112\$cohort == '58C', 0, 1) #Fit the CNV with a three component model fit.pca <- CNVtest.binary(signal = pca.signal, sample = sample, batch = batches, disease.status = trait, ncomp = 3, n.H0=3, n.H1=3, model.disease = "~ cn") cnv.plot(fit.pca[['posterior.H0']], batch = '58C', breaks = 30) ```

CNVtools documentation built on Nov. 1, 2018, 2:24 a.m.