bayesCGH-package {bayesCGH}R Documentation

bayesCGH - Empirical bayesian analysis of aCGH data

Description

This package is intended to take data analysed by the 'snapCGH' package and, by means of the 'compareEquivalences' function, analyse the data in terms of a set of biological hypotheses established on the data. The output of this analysis are the posterior probabilities of each hypothesis for each region of the genome.

Details

Package: bayesCGH
Type: Package
Version: 1.0
Date: 2008-04-30
License: GPL-3
~~ An overview of how to use the package, including the most important ~~ ~~ functions ~~

Author(s)

Thomas Hardcastle

Maintainer: Thomas Hardcastle (tjh48@cam.ac.uk)

References

Empirical Bayesian methods for model testing on array CGH data. Thomas Hardcastle, Maria J. Garcia, James D. Brenton, Simon Tavare. In preparation.

Examples

#########################
## not run
# library(bayesCGH)
#
## load segInfo data from 'snapCGH' package.
# load("segInfo.DNACopy.merged.Rdata")
# seg <- nudSegmentation(segInfo.DNACopy.merged)
#
## Define sets of non-intersecting clinical sets on samples
# sel.groups <- list()
# sel.groups[[1]] <- which(seg$samples$Treatment == "T0" & seg$samples$CA125.response == "Responder" & seg$samples$Rejected == FALSE)
# sel.groups[[2]] <- which(seg$samples$Treatment == "T0" & seg$samples$CA125.response == "Non-responder" & seg$samples$Rejected == FALSE)
# sel.groups[[3]] <- which(seg$samples$Treatment == "T1" & seg$samples$CA125.response == "Responder" & seg$samples$Rejected == FALSE)
# sel.groups[[4]] <- which(seg$samples$Treatment == "T1" & seg$samples$CA125.response == "Non-responder" & seg$samples$Rejected == FALSE)
#
## Form hypotheses on data
# classes <- list()
# classes[[1]] <- list(c(1,2,3,4))
# classes[[2]] <- list(c(1,2), c(3,4))
# classes[[3]] <- list(c(1,3), c(2,4))
# classes[[4]] <- list(c(1), c(2, 3,4))
# class.names <- c("conserved effects", "treatment effects", "response effects", "selected response effects")
# class.prior <- rep(1/4, length(classes))
#
## Find posterior probabilities
# compeq <- compareEquivalences(seg, sel.groups, c("pre-sensitive",  "pre-resistant",  "post-sensitive", "post-resistant"), probe.classes = list(c("Normal"), c("Down", "Deleted"), c("Up", "Amplified")), class.priors = class.prior, classes = classes, class.names = class.names)
#
## Summarising regions associated with one hypothesis
# conserved <- summarySelection(seg, which(compeq$probabilities[1,] > 0.5), carbo.compeq$probabilities[1,], carbo.sel.groups,
#                 c("pre-sensitive",  "pre-resistant", "post-sensitive", "post-resistant"))
## Plot posterior probabilities for each hypothesis
# for(eq.class in 1:4)
#   plot.up.down(cbind(compeq$probabilities[eq.class,], 0), seg$genes, main = class.names[eq.class], ylim = c(0,1), num.chr = 24)

[Package bayesCGH version 0.6 Index]