determineOffset {Repitools} | R Documentation

## Function to determine the normalising offset f that accounts for the relative sequencing depth.

### Description

The composition of a library influences the resulting read densities. To adjust the modelled mean (in the Poisson model) for these composition effects, we estimate a normalising factor f that accounts simultaneously for overall sequencing depth and composition. The derivation of this offset is motivated by the M (log ratio) versus A (average-log-count) plot.

### Usage

    determineOffset(x, quantile = 0.998, controlPlot = list(show = FALSE,
        nsamp = 50000, mfrow=c(1,1), xlim=NULL, ylim=NULL, main=NULL, ask=FALSE))

### Arguments

`x` | `BayMethList` object.
`quantile` | quantile q to restrict values of A = log2(sampleInterest*control)/2
`controlPlot` | list defining whether a MA plot should be shown. - `show` logical. If 'TRUE' the corresponding MA plot is shown. (default FALSE) - `nsamp` number of genomic regions included in the plot. (These are sampled without replacement). - `mfrow` vector of the form "c(nr, nc)" to determine how several plots should be ordered. - `xlim, ylim` numeric vectors of length 2, giving the x and y coordinates ranges. - `main` If NULL the names of the sample of interest are used as title in the MA plot. Alternatively, a vector with length equal to the number of samples of interest can be provided. - `ask` logical. If 'TRUE' (and the R session is interactive) the user is asked for input, before a new figure is drawn. (default FALSE).

### Value

A `BayMethList` object given as input, where the slot `fOffset` is filled accordingly.

### Author(s)

Andrea Riebler

### See Also

maPlot, plotSmear

### Examples

       if(require(BSgenome.Hsapiens.UCSC.hg18)){
            windows <- genomeBlocks(Hsapiens, chrs="chr21", width=100, spacing=100)
            cpgdens <- cpgDensityCalc(windows, organism=Hsapiens,
                w.function="linear", window=700)
            co <- matrix(rnbinom(length(windows), mu=10, size=2), ncol=1)
            sI <- matrix(rnbinom(2*length(windows), mu=5, size=2), ncol=2)
            bm <- BayMethList(windows=windows, control=co, sampleInterest=sI,
                cpgDens=cpgdens)

            bm <- determineOffset(bm, controlPlot=list(show=TRUE, mfrow=c(1,2)))
       }

[Package Repitools version 1.36.0 Index]