posteriorProbs,CNVDetectionResult-method

object


This generic function returns the posterior probabilities of
 a CNV detection method stored in an instance of 
<code><a rd-options="" href="/link/CNVDetectionResult-class?package=cn.mops&version=1.18.0" data-mini-rdoc="cn.mops::CNVDetectionResult-class">CNVDetectionResult-class</a></code>. 
The posterior probabilities are represented
as a three dimensional array, where the three dimensions are segment,
copy number and individual.


cn.mops (Copy Number estimation by a Mixture Of PoissonS)
is a data processing pipeline for copy number variations and
aberrations (CNVs and CNAs) from next generation sequencing
(NGS) data. The package supplies functions to convert BAM files
into read count matrices or genomic ranges objects, which are
the input objects for cn.mops. cn.mops models the depths of
coverage across samples at each genomic position. Therefore, it
does not suffer from read count biases along chromosomes. Using
a Bayesian approach, cn.mops decomposes read variations across
samples into integer copy numbers and noise by its mixture
components and Poisson distributions, respectively. cn.mops
guarantees a low FDR because wrong detections are indicated by
high noise and filtered out. cn.mops is very fast and written
in C++.

Guenter Klambauer

cn.mops

cn.mops - Mixture of Poissons for CNV detection in NGS data

posteriorProbs,CNVDetectionResult-method function


This generic function returns the posterior probabilities of
 a CNV detection method stored in an instance of 
<code><a rd-options='' href='CNVDetectionResult-class'>CNVDetectionResult-class</a></code>. 
The posterior probabilities are represented
as a three dimensional array, where the three dimensions are segment,
copy number and individual.


This generic function returns the posterior probabilities of
 a CNV detection method stored in an instance of 
<code><a rd-options='' href='CNVDetectionResult-class'>CNVDetectionResult-class</a></code>. 
The posterior probabilities are represented
as a three dimensional array, where the three dimensions are segment,
copy number and individual.

This generic function returns the posterior probabilities of
 a CNV detection method stored in an instance of 
<code><a rd-options="" href="/link/CNVDetectionResult-class?package=cn.mops&version=1.18.0" data-mini-rdoc="cn.mops::CNVDetectionResult-class">CNVDetectionResult-class</a></code>. 
The posterior probabilities are represented
as a three dimensional array, where the three dimensions are segment,
copy number and individual.

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