plotFreqStat: frequency plots and significance analysis
In aCGH: Classes and functions for Array Comparative Genomic Hybridization data

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

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)

`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.

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)