aCGH {aCGH} | R Documentation |
Objects of this class represent batch of arrays of Comparative Genomic Hybridization data. In addition to that, there are slots for representing phenotype and various genomic events associated with aCGH experiments, such as transitions, amplifications, aberrations, and whole chromosomal gains and losses. Currently objects of class aCGH are represented as S3 classes which are named list of lists with functions for accessing elements of that list. In the future, it's anticipated that aCGH objects will be implemented using S4 classes and methods.
One way of creating objects of class aCGH is to provide the two
mandatory arguments to create.aCGH
function: log2.ratios
and clones.info
. Alternatively aCGH object can be created using
aCGH.read.Sprocs
that reads Sproc data files and
creates object of type aCGH.
log2.ratios |
Data frame containing the log2 ratios of copy number changes; rows correspond to the clones and the columns to the samples (Mandatory). |
clones.info |
Data frame containing information about the clones used for
comparative genomic hybridization. The number of rows of
clones.info has to match the number of rows in
log2.ratios (Mandatory).
|
phenotype |
Data frame containing phenotypic information about samples used in
the experiment generating the data. The number of rows of
phenotype has to match the number of columns in
log2.ratios (Optional).
|
log2.ratios.imputed |
Data frame containing the imputed log2 ratios. Calculate this using
impute.lowess function; look at the examples below (Optional).
|
hmm |
The structure of the hmm element is described in
hmm . Calculate this using
find.hmm.states function; look at the examples below
(Optional).
|
hmm |
Similar to the structure of the hmm element. Calculate this using
mergeHmmStates function; look at the examples below
(Optional).
|
sd.samples |
The structure of the sd.samples element is described in
computeSD.Samples . Calculate this using
computeSD.Samples function; look at the examples
below (Optional). It is prerequisite that the hmm states are
estimated first.
|
genomic.events |
The structure of the genomic.events element is described in
find.genomic.events . Calculate this using
find.genomic.events function; look also at the
examples below. It is prerequisite that the hmm states and
sd.samples are computed first. The genomic.events is used
widely in variety of plotting functions such as
plotHmmStates , plotFreqStat , and
plotSummaryProfile .
|
dim.aCGH |
returns the dimensions of the aCGH object: number of clones by number of samples. |
num.clones |
number of clones/number of rows of the log2.ratios data.frame. |
nrow.aCGH |
same as num.clones .
|
is.aCGH |
tests if its argument is an object of class aCGH .
|
num.samples |
number of samples/number of columns of the log2.ratios data.frame. |
nrow.aCGH |
same as num.samples .
|
num.chromosomes |
number of chromosomes processed and stored in the aCGH object. |
clone.names |
returns the names of the clones stored in the clones.info slot of the aCGH object. |
row.names.aCGH |
same as clone.names .
|
sample.names |
returns the names of the samples used to create the aCGH
object. If the object is created using
aCGH.read.Sprocs , these are the file names of the
individual arrays.
|
col.names.aCGH |
same as sample.names .
|
[.aCGH |
subsetting function. Works the same way as [.data.frame .
|
Most of the functions/slots listed above have assignment operators '<-' associated with them.
clones.info
slot has to contain a list with at least
4 columns: Clone (clone name), Target (unique ID, e.g. Well ID), Chrom
(chromosome number, X chromosome = 23 in human and 20 in mouse, Y
chromosome = 24 in human and 21 in mouse) and kb (kb position on the
chromosome).
Peter Dimitrov
aCGH.read.Sprocs
,
find.hmm.states
,
computeSD.Samples
,
find.genomic.events
,
plotGenome
,
plotHmmStates
,
plotFreqStat
,
plotSummaryProfile
## Creating aCGH object from log2.ratios and clone info files ## For alternative way look at aCGH.read.Sprocs help datadir <- system.file(package = "aCGH") datadir <- paste(datadir, "/examples", sep="") clones.info <- read.table(file = file.path(datadir, "clones.info.ex.txt"), header = TRUE, sep = "\t", quote="", comment.char="") log2.ratios <- read.table(file = file.path(datadir, "log2.ratios.ex.txt"), header = TRUE, sep = "\t", quote="", comment.char="") pheno.type <- read.table(file = file.path(datadir, "pheno.type.ex.txt"), header = TRUE, sep = "\t", quote="", comment.char="") ex.acgh <- create.aCGH(log2.ratios, clones.info, pheno.type) ## Printing, summary and basic plotting for objects of class aCGH data(colorectal) colorectal summary(colorectal) sample.names(colorectal) phenotype(colorectal) plot(colorectal) ## Subsetting aCGH object colorectal[1:1000, 1:30] ## Imputing the log2 ratios log2.ratios.imputed(ex.acgh) <- impute.lowess(ex.acgh) ## Determining hmm states of the clones ## WARNING: Calculating the states takes some time ##in the interests of time, hmm-finding function is commented out ##instead the states previosuly save are assigned ##hmm(ex.acgh) <- find.hmm.states(ex.acgh) hmm(ex.acgh) <- ex.acgh.hmm hmm.merged(ex.acgh) <- mergeHmmStates(ex.acgh, model.use = 1, minDiff = .25) ## Calculating the standard deviations for each array sd.samples(ex.acgh) <- computeSD.Samples(ex.acgh) ## Finding the genomic events associated with each sample genomic.events(ex.acgh) <- find.genomic.events(ex.acgh) ## Plotting and printing the hmm states plotHmmStates(ex.acgh, 1) pdf("hmm.states.temp.pdf") plotHmmStates(ex.acgh, 1) dev.off() ## Plotting summary of the sample profiles plotSummaryProfile(colorectal)