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)