MultiSourceCopyNumberNormalization: The MultiSourceCopyNumberNormalization class
Description
Package: aroma.cn
Class MultiSourceCopyNumberNormalization
Object
~~|
~~+--MultiSourceCopyNumberNormalization
Directly known subclasses:
public static class MultiSourceCopyNumberNormalization
extends Object
The multi-source copy-number normalization (MSCN) method [1] is a
normalization method that normalizes copy-number estimates measured
by multiple sites and/or platforms for common samples. It normalizes the
estimates toward a common scale such that for any copy-number level
the mean level of the normalized data are the same.Usage
MultiSourceCopyNumberNormalization(dsList=NULL, fitUgp=NULL, subsetToFit=NULL, targetDimension=1, align=c("byChromosome", "none"), tags="*", ...)Arguments
fitUgp
An AromaUgpFile that specifies the
common set of loci used to normalize the data sets at. subsetToFit
The subset of loci (as mapped by the fitUgp
object) to be used to fit the normalization functions.
If NULL, loci on chromosomes 1-22 are used, but not on ChrX and ChrY. targetDimension
A numeric index specifying the data set in
dsList to which each platform in standardize towards.
If NULL, the arbitrary scale alo align
A character specifying type of alignment applied, if any.
If "none", no alignment is done.
If "byChromosome", the signals are shifted chromosome
by chromosome tags
(Optional) Sets the tags for the output data sets.
Fields and Methods
Methods:
rll{
getAllNames Gets the names of all unique samples across all sources.
getAsteriskTags -
getInputDataSets Gets the list of data sets to be normalized.
getOutputDataSets -
getOutputPaths -
getParameters -
getTags -
nbrOfDataSets -
process Normalizes all samples.
}
Methods inherited from Object:
asThis, getChecksum, $, $<-, [[, [[<-, as.character, attach, attachLocally, clearCache, clearLookupCache, clone, detach, equals, extend, finalize, gc, getEnvironment, getFieldModifier, getFieldModifiers, getFields, getInstantiationTime, getStaticInstance, hasField, hashCode, ll, load, objectSize, print, registerFinalizer, saveDifferent preprocessing methods normalize ChrX & ChrY differently
Some preprocessing methods estimate copy numbers on sex chromosomes
differently from the autosomal chromosomes. The way this is done may
vary from method to method and we cannot assume anything about what
approach is. This is the main reason why the estimation of the
normalization function is by default based on signals from autosomal
chromosomes only; this protects the estimate of the function from
being biased by specially estimated sex-chromosome signals.
Note that the normalization function is still applied to all chromosomes.
This means that if the transformation applied by a particular
preprocessing method is not the same for the sex chromosomes as the
autosomal chromosomes, the normalization applied on the sex
chromosomes is not optimal one. This is why multi-source
normalization sometimes fails to bring sex-chromosome signals
to the same scale across sources. Unfortunately, there is no
automatic way to handle this.
The only way would be to fit a specific normalization function to each
of the sex chromosomes, but that would require that there exist
copy-number abberations on those chromosomes, which could be a too
strong assumption.
A more conservative approach is to normalize the signals such that
afterward the median of the smoothed copy-number levels are the same
across sources for any particular chromosome.
This is done by setting argument align="byChromosome".Details
The multi-source normalization method is by nature a single-sample method,
that is, it normalizes arrays for one sample at the time and independently
of all other samples/arrays.
However, the current implementation is such that it first generates
smoothed data for all samples/arrays. Then, it normalizes the
sample one by one.References
[1] H. Bengtsson, A. Ray, P. Spellman & T.P. Speed,
A single-sample method for normalizing and combining
full-resolution copy numbers from multiple platforms,
labs and analysis methods,
Bioinformatics 2009.