Model1: Correct for global correlations and biases

In Rolexa: Statistical analysis of Solexa sequencing data

Description

Functions to correct for global correlations between color channels or between successive sequencing cycles

Usage

## S4 method for signature 'SolexaIntensity'
DeCorrelateChannels(int,cycles=seq(1,dim(int)[3],by=1),theta=matrix(rep(c(0.8806742,1.3727418,0.883865,1.545728),length(cycles)),ncol=4,byrow=TRUE))
## S4 method for signature 'array'
DeCorrelateChannels(int,cycles=seq(1,dim(int)[3],by=1),theta=matrix(rep(c(0.8806742,1.3727418,0.883865,1.545728),length(cycles)),ncol=4,byrow=TRUE))
DeCorrelateChannels(int,...)
## S4 method for signature 'SolexaIntensity'
OptimizeAngle(int,cycles=seq(1,dim(int)[3],by=1),...)
OptimizeAngle(int,...)
## S4 method for signature 'SolexaIntensity'
DeCorrelateCycles(int,ncycles=dim(int)[3],rate=1.8e-2)
## S4 method for signature 'array'
DeCorrelateCycles(int,ncycles=dim(int)[3],rate=1.8e-2)
DeCorrelateCycles(int,...)
## S4 method for signature 'SolexaIntensity'
OptimizeRate(int,ncycles=dim(int)[3],...)
OptimizeRate(int,...)
## S4 method for signature 'RolexaRun'
TileNormalize(run=Rolexa.env,int,cycles=seq(1,dim(int)[3],by=1))
TileNormalize(run,...)

Arguments

run	a RolexaRun object defining the run parameters
int	a SolexaIntensity object or an array
cycles, ncycles	the cycles or the number of cycles (starting from 1) to apply the correction to
theta	a length(cycles)*4 matrix with four angles per cycle defining the coordinate changes
rate	the rate of nucleotide mis-incorporation at each cycle
...	additional arguments passed to optim

Details

DeCorrelateChannels applies to coordinate transforms: one transforming the axes 1,2 to the axes with angles theta[,1:2] relative to axis 1, and similarly with axes 3,4 and angles theta[,3:4]. These angles can be calculated with OptimizeAngle which minimizes the correlations between channel 1 and 2, and between channel 3 and 4, for each cycle. DeCorrelateCycles assumes that at each cycles, a fraction rate of sequences fail to incorporate any nucleotides and therefore the sequence lengths at each colony display a binomial distribution which is corrected for by taking into account the intensity measured at previous cycles. OptimizeRate calculates a rate that minimizes correlations between consecutives cycles.

TileNormalize estimates the local trend by loess fitting of the model int ~ x+y and substracts it from the intensity matrix.

Value

TileNormalize, DeCorrelateChannels and DeCorrelateCycles return an object of the same type as int corrected for spurious correlations. OptimizeAngle returns an length(cycles)*4 matrix and OptimizeRate returns a single positive real number.

Author(s)

Jacques Rougemont, Arnaud Amzallag, Christian Iseli, Laurent Farinelli, Ioannis Xenarios, Felix Naef

References

Probabilistic base calling of Solexa sequencing data, BMC Bioinformatics 2008, 9:431

See Also

TileNormalize

Examples

path = SolexaPath(system.file("extdata", package="ShortRead"))
rolenv = SetModel(idsep="_")
int = readIntensities(path,pattern="s_1_0001",withVariability=FALSE)

int1 = DeCorrelateChannels(int=int,cycles=1:5,theta=OptimizeAngle(int=int,cycles=1:5))
int2 = DeCorrelateCycles(int=int1,ncycles=5,rate=OptimizeRate(int=int1))
int3 = TileNormalize(run=rolenv,int=int,cycles=1)
seq = CombineReads(run=rolenv,path=path,pattern="s_1_0001_seq*")
PlotCycles(run=rolenv,int=int3,seq=seq,cycles=1:4)

Rolexa documentation built on Oct. 5, 2016, 4:38 a.m.