segmentPlot {ADaCGH}R Documentation

Segment plots for aCGH

Description

A (more or less) uniform interface and uniform output to segment plots for the available aCGH methods in this package. By the default, it produces html files (and associated png) with an image map with links to additional information. You need Python for all this to work.

Usage

segmentPlot(x, geneNames, chrom.numeric = NULL, cghdata = NULL,
            arraynames = NULL, idtype = "ug", organism = "Hs",
            yminmax = NULL, numarrays = NULL, ...)

Arguments

x The fitted object of the appropriate class.
geneNames A vector of gene names.
chrom.numeric A (numeric) vector of chromosome indicators.
cghdata The aCGH data.
arraynames A vector of array names.
idtype The type of id of the gene name; one of ('None', 'cnio', 'affy', 'clone', 'acc', 'ensembl', 'entrez', 'ug') corresponding, respectively to None, CNIO ID, Affymetrix, Clone ID, Accession Number, Ensembl ID, Entrez ID, UniGene.
organism The organism; one of ('None', 'Hs', 'Mm', 'Rn') corresponding, respectively, to None, Homo sapiens, Mus musculus (mouse) and Rattus norvegicus (rat).
yminmax A vector of the form (min, max) for the minimum and maximum used in the figures.
numarrays The number of arrays to plot.
... Other arguments, passed to the underlying plotting functions.

Value

Used only for its side effects of producing plots. Beware that you will get from a few to a lot of HTML and png files writtend to your local file system.

Note

Most of the arguments are not really needed in most cases. See examples below.

You NEED a working Python for this function to work and produce the imagemaps.

Author(s)

Ramon Diaz-Uriarte rdiaz02@gmail.com

References

Diaz-Uriarte, R. and Rueda, O.M. (2006). ADaCGH: an R package and web-based application for the analysis of aCGH data. Tech. report. http://www.ligarto.org/rdiaz/Papers/adacgh.pdf, http://adacgh.bioinfo.cnio.es.

See Also

pSegmentWavelets, pSegmentACE, pSegmentDNAcopy, pSegmentPSW,

Examples


## Not run: 

## This will not work unless you have a working Python installation

setwd("/tmp/o3") ## all slaves need a common dir to read and write.
mpiInit()

data(cghE1)
tmpchr <- sub("chr", "", cghE1$Chromosome)
chrom.numeric <- as.numeric(as.character(tmpchr))
chrom.numeric[tmpchr == "X"] <- 23
chrom.numeric[tmpchr == "Y"] <- 24
rm(tmpchr)
### we need the data ordered
reorder <- order(chrom.numeric,
                 cghE1$UG.Start,
                 cghE1$UG.End,
                 cghE1$Name)
cghE1 <- cghE1[reorder, ]
chrom.numeric <- chrom.numeric[reorder]

## run all methods
hmm.out <- pSegmentHMM(cghE1[, 5:7], chrom.numeric)
glad.out <- pSegmentGLAD(cghE1[, 5:7], chrom.numeric)
cghseg.out <- pSegmentCGHseg(cghE1[, 5:7], chrom.numeric)
ace.out <- pSegmentACE(cghE1[, 5:7], chrom.numeric)
wave.out <- pSegmentWavelets(cghE1[, 5:7], chrom.numeric)
wave.nm.out <- pSegmentWavelets(cghE1[, 5:7], chrom.numeric, merge = FALSE)
cbs.out <- pSegmentDNAcopy(cghE1[, 5:7], chrom.numeric)
cbs.nm.out <- pSegmentDNAcopy(cghE1[, 5:7], chrom.numeric, merge = FALSE)
cbs.nm.ns.out <- pSegmentDNAcopy(cghE1[, 5:7], chrom.numeric, merge = FALSE,
                                 smooth = FALSE)
psw.pos.out <- pSegmentPSW(cghE1[, 5:7], chrom.numeric, sign = 1)
psw.neg.out <- pSegmentPSW(cghE1[, 5:7], chrom.numeric, sign = -1)

## BioHMM is the only one that uses distances
## it is the slowest, so do only two
biohmm.out <- pSegmentBioHMM(cghE1[, 5:6], chrom.numeric, cghE1$UG.Start)

### plot all
segmentPlot(hmm.out, 
            geneNames = cghE1[, 1],
            chrom.numeric = chrom.numeric,
            cghdata = cghE1[, 5:7],
            idtype = "ug",
            organism = "Hs")

segmentPlot(glad.out,
            geneNames = cghE1[, 1],
            chrom.numeric = chrom.numeric,
            cghdata = cghE1[, 5:7],
            idtype = "ug",
            organism = "Hs")

segmentPlot(cghseg.out,
            geneNames = cghE1[, 1],
            chrom.numeric = chrom.numeric,
            cghdata = cghE1[, 5:7],
            idtype = "ug",
            organism = "Hs")

segmentPlot(wave.out, 
            geneNames = cghE1[, 1],
            chrom.numeric = chrom.numeric,
            cghdata = cghE1[, 5:7],
            idtype = "ug",
            organism = "Hs")

segmentPlot(wave.nm.out,
            geneNames = cghE1[, 1],
            chrom.numeric = chrom.numeric,
            cghdata = cghE1[, 5:7],
            idtype = "ug",
            organism = "Hs")

segmentPlot(cbs.out, 
            geneNames = cghE1[, 1],
            chrom.numeric = chrom.numeric,
            cghdata = cghE1[, 5:7],
            idtype = "ug",
            organism = "Hs")

segmentPlot(psw.pos.out,
            geneNames = cghE1[, 1],
            chrom.numeric = chrom.numeric,
            cghdata = cghE1[, 5:7],
            idtype = "ug",
            organism = "Hs")

segmentPlot(psw.neg.out,
            geneNames = cghE1[, 1],
            chrom.numeric = chrom.numeric,
            cghdata = cghE1[, 5:7],
            idtype = "ug",
            organism = "Hs")

segmentPlot(biohmm.out, 
            geneNames = cghE1[, 1],
            chrom.numeric = chrom.numeric,
            cghdata = cghE1[, 5:6],
            idtype = "ug",
            organism = "Hs")

## need to choose fdr
ace.out.sum <- summary(ace.out)
segmentPlot(ace.out.sum, 
            geneNames = cghE1[, 1],
            chrom.numeric = chrom.numeric,
            cghdata = cghE1[, 5:7],
            idtype = "ug",
            organism = "Hs")

## End(Not run)

[Package ADaCGH version 1.3-10 Index]