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cn.mops (version 1.18.0)

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

Description

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++.

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Version

Version

1.18.0

License

LGPL (>= 2.0)

Last Published

February 15th, 2017

Functions in cn.mops (1.18.0)

calcIntegerCopyNumbers,CNVDetectionResult-method

Calculation of integer copy numbers for the CNVs and CNV regions.
calcIntegerCopyNumbers

Calculation of integer copy numbers for the CNVs and CNV regions.
getReadCountsFromBAM

Calculation of read counts from BAM files.
iniCall,CNVDetectionResult-method

This generic function returns the informative/non-informative call of a CNV detection method stored in an instance of CNVDetectionResult-class. The I/NI call is a measure for a genomic segment across all samples, whether this segment is a CNV region (informative) or a normal genomic region (non-informative).
exomeCounts

Read counts from exome sequencing for CNV detection
sampleNames

iniCall

This generic function returns the informative/non-informative call of a CNV detection method stored in an instance of CNVDetectionResult-class. The I/NI call is a measure for a genomic segment across all samples, whether this segment is a CNV region (informative) or a normal genomic region (non-informative).
segment

Fast segmentation of CNV calls.
cnvr

cnvr,CNVDetectionResult-method

getSegmentReadCountsFromBAM

Calculation of read counts from BAM files for predefined segments.
gr,CNVDetectionResult-method

integerCopyNumber,CNVDetectionResult-method

integerCopyNumber

referencecn.mops

Copy number detection in NGS data with in a control versus cases setting.
sampleNames,CNVDetectionResult-method

X

A simulated data set for CNV detection from NGS data.
XRanges

A simulated data set for CNV detection from NGS data.
cnvs,CNVDetectionResult-method

CNVRanges

Genomic locations and indices of the simulated CNVs.
individualCall,CNVDetectionResult-method

individualCall

normalizeGenome

Normalization of NGS data
params,CNVDetectionResult-method

segplot,CNVDetectionResult-method

Visualization of a CNV detection result.
segplot

Visualization of a CNV detection result.
calcFractionalCopyNumbers

Calculation of fractional copy numbers for the CNVs and CNV regions.
cnvs

calcFractionalCopyNumbers,CNVDetectionResult-method

Calculation of fractional copy numbers for the CNVs and CNV regions.
exomecn.mops

Copy number detection in exome sequencing data.
makeRobustCNVR

Calculates robust CNV regions.
normalizeChromosomes

Normalization of NGS data.
params

plot

Plots a CNVDetectionResult
segmentation,CNVDetectionResult-method

segmentation

cn.mops

Copy number detection in NGS data.
normalizedData

show

Displays the result object of a copy number detection method.
normalizedData,CNVDetectionResult-method

singlecn.mops

Copy number detection in NGS data with in a setting in which only one sample is available
CNVDetectionResult-class

Class "CNVDetectionResult"
gr

haplocn.mops

Copy number detection in NGS data of haploid samples.
localAssessments

localAssessments,CNVDetectionResult-method

posteriorProbs,CNVDetectionResult-method

This generic function returns the posterior probabilities of a CNV detection method stored in an instance of CNVDetectionResult-class. The posterior probabilities are represented as a three dimensional array, where the three dimensions are segment, copy number and individual.
posteriorProbs

This generic function returns the posterior probabilities of a CNV detection method stored in an instance of CNVDetectionResult-class. The posterior probabilities are represented as a three dimensional array, where the three dimensions are segment, copy number and individual.