plotFreqStat {aCGH}R Documentation

frequency plots and significance analysis

Description

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

Usage

plotFreqStat(aCGH.obj, resT, pheno,
             chrominfo = human.chrom.info.Jul03, X = TRUE, Y = FALSE,
             threshold = TRUE, minChanged = 0, all = FALSE,
             rsp.uniq = unique(pheno), nlim = 1, cutplot = 0,
             titles = rsp.uniq, thres = 0.2, ylm = c(-1, 1),
             ngrid = 2, p.thres = c(0.01, 0.05, 0.1),
             mincolors = 0.1, quant.col = 0.11, numaut = 22,
             onepage = TRUE, colored = TRUE, summarize.clones = 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.
titles titles names of the groups to be used. Default is the unique levels of the pheno.
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. 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 freqeuncy data as well as for clone screening and for significance analysis when threshold is T.
cutplot only clones with at least cutplot frequency of gain and loss are plotted.
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.
ngrid ngrid in how many grids red and green colors need to be divided.
nlim nlim maximum value for colors.
mincolors mincolors minimum value for colors.
quant.col quant.col what quantile to use for color display.
numaut numaut number of the autosomes
onepage onepage whether all plots are to be plotted on one page or different pages.
threshold threshold specifies whether significance analysis should be performed using original values (F) or thresholded (trimerized) values (T).
minChanged Only clones that change in at least minChanged proportion of samples are plotted; is 0 by default.
all all specifies whether samples should be analyzed by subgroups (T) or together (F).
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.
colored Is plotting in color or not? Default is T.
summarize.clones Return frequency summaries for individual clones.

See Also

aCGH

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)$age
age.na <- !is.na(age)
colorectal.na <- colorectal[ ,age.na, keep = TRUE ]
dat <- log2.ratios.imputed(colorectal.na)
stat.age <- sapply(1:nrow(dat),
                   function(i) {
                      if (i %% 100 == 0)
                         cat(i, "\n")
                      lm.fit <-
                          summary(lm(dat[i,] ~ age[age.na]))
                      c(lm.fit$fstatistic[1],
                        1 - pf(lm.fit$fstatistic[1],
                               lm.fit$fstatistic[2],
                               lm.fit$fstatistic[3])
                       )
                   }
                   )
## Make resT
resT.age <- data.frame(index = 1:ncol(stat.age),
                       teststat = stat.age[ 1, ],
                       rawp = stat.age[ 2, ],
                       adjp = p.adjust(stat.age[ 2, ], "fdr"))
#write out the table of results:
tbl.age <- data.frame(clones.info(colorectal), resT.age)
write.table(tbl.age, "table.age.txt", sep="\t", col.names = TRUE,
            row.names = FALSE, quote = FALSE)


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