aCGH (version 1.50.0)

plotFreqStat: frequency plots and significance analysis

Description

The main application of this function is to plot the frequency of changes.

Usage

plotFreqStat(aCGH.obj, resT = NULL, pheno = rep(1, ncol(aCGH.obj)), chrominfo = human.chrom.info.Jul03, X = TRUE, Y = FALSE, rsp.uniq = unique(pheno), all = length(rsp.uniq) == 1 && is.null(resT), titles = if (all) "All Samples" else rsp.uniq, cutplot = 0, thres = .25, factor = 2.5, ylm = c(-1, 1), p.thres = c(.01, .05, .1), numaut = 22, onepage = TRUE, colored = TRUE)

Arguments

aCGH.obj

Object of class aCGH

resT

Data frame having the same structure as the result of applying mt.maxT or mt.minP functions from Bioconductor's multtest package for multiple testing. The result is a data frame including the following 4 components: 'index', 'teststat', 'rawp' and 'adjp'.

pheno

phenotype to compare.

chrominfo

Chromosomal information. Defaults to human.chrom.info.Jul03

X

Include X chromosome? Defaults to yes.

Y

Include Y chromosome? Defaults to no.

rsp.uniq

rsp.uniq specified the codes for the groups of interest. Default is the unique levels of the phenotype. Not used when all is T.

all

all specifies whether samples should be analyzed by subgroups (T) or together (F).

titles

titles names of the groups to be used. Default is the unique levels of the pheno.

cutplot

only clones with at least cutplot frequency of gain and loss are plotted.

thres

thres is either a vector providing unique threshold for each sample or a vector of the same length as number of samples (columns in data) providing sample-specific threshold. If aCGH.obj has non-null sd.samples, then thres is automatically replaced by factor times madGenome of aCGH object. Clone is considered to be gained if it is above the threshold and lost if it below negative threshold. Used for plotting the gain/loss frequency data as well as for clone screening and for significance analysis when threshold is TRUE.Defaults to 0.25

factor

factor specifies the number by which experimental variability should be multiplied. used only when sd.samples(aCGH.obj) is not NULL or when factor is greater than 0. Defaults to 2.5

ylm

ylm vertical limits for the plot

p.thres

p.thres vector of p-value ciut-off to be plotted. computed conservatively as the threshold corresponding to a given adjusted p-value.

numaut

numaut number of the autosomes

onepage

onepage whether all plots are to be plotted on one page or different pages. When more than 2 groups are compared, we recommend multiple pages.

colored

Is plotting in color or not? Default is TRUE.

Examples

data(colorectal)

## Use mt.maxT function from multtest package to test
## differences in group means for each clone grouped by sex
colnames(phenotype(colorectal))
sex <- phenotype(colorectal)$sex
sex.na <- !is.na(sex)
colorectal.na <- colorectal[ ,sex.na, keep = TRUE ]
dat <- log2.ratios.imputed(colorectal.na)
resT.sex <- mt.maxT(dat, sex[sex.na], test = "t", B = 1000)

## Plot the result along the genome
plotFreqStat(colorectal.na, resT.sex, sex[sex.na],
             titles = c("Male", "Female"))

## Adjust the p.values from previous exercise with "fdr"
## method and plot them
resT.sex.fdr <- resT.sex
resT.sex.fdr$adjp <- p.adjust(resT.sex.fdr$rawp, "fdr")
plotFreqStat(colorectal.na, resT.sex.fdr, sex[sex.na],
             titles = c("Male", "Female"))

## Derive statistics and p-values for testing the linear association of
## age with the log2 ratios of each clone along the samples

age <- phenotype(colorectal)$age
age.na <- which(!is.na(age))
age <- age[age.na]
colorectal.na <- colorectal[, age.na]
stat.age <- aCGH.test(colorectal.na, age, test = "linear.regression", p.adjust.method = "fdr")

#separate into two groups: < 70 and > 70 and plot freqeuncies of gain and loss
#for each clone. Note that statistic plotted corresponds to linear coefficient
#for age variable

plotFreqStat(colorectal.na, stat.age, ifelse(age < 70, 0, 1), titles =
             c("Young", "Old"), X = FALSE, Y = FALSE)