normalize-methods: Normalize methods
In TSSi: Transcription Start Site Identification

Description Usage Arguments Details Value Methods Author(s) See Also Examples

Normalize sequence read count data.

1 2	normalizeCounts(x, fun=mean, offset=10L, basal=1e-4, lambda=c(0.1, 0.1), fit=FALSE, multicore=TRUE, optimizer="all", ...)

`x`	Object of class `TssData` with raw data to normalize.
`fun`	Function used to average over replicates (default: `mean`).
`offset`	Integer defining the number of bases add to the ends of each segment with `basal` rate.
`basal`	Numeric specifying the basal rate.
`lambda`	Numeric vector of length two specifying the regulation parameter for each side of the segment.
`fit`	Logical whether the fitting should be performed in addition to the estimation based on the Poisson ratios obtained from all reads.
`multicore`	Logical whether to use the parallel package to speed up the fitting. Has only an effect if the package is available and loaded. For details, see the ‘details’ section.
`optimizer`	Character string choosing the optimizer for the fit (default: “all”). Possible choices are “optim” for the `optim` function from the stats package, “bobyqa” for the `bobyqa` function from the minqu package,or “all” for taking the best fit out of both.
`...`	Additional arguments passed for the parallel package if used. For details, see the ‘details’ section.

The normalization reduces the noise by shrinking the counts towards zero. This step is intended to eliminate false positive counts as well as making further analyzes more robust by reducing the impact of large counts. Such a shrinkage or regularization procedure constitutes a well-established strategy in statistics to make predictions conservative, i.e. to reduce the number of false positive predictions.

An objective function is minimized to estimate the transcription level in a regularized manner. The log-likelihood is given by the product of the probabilities of the counts which is assumed as a Poisson distribution by default.

For \sQuote{lambda[1] > 0}, counts unequal to zero are penalized to obtain conservative estimates of the transcription levels with a preferably small number components, i.e. genomic positions, unequal to zero. The larger \sQuote{lambda[1]}, the more conservative is the identification procedure.

To enhance the shrinkage of isolated counts in comparison to counts in regions of strong transcriptional activity, the information of consecutive genomic positions in the measurements is regarded by evaluating differences between adjacent count estimates.

In order to distribute the identification step over multiple processor cores, the mclapply function of the parallel package can be used. For this, the parallel package has to be loaded manually before starting the computation, additional parameters are passed via the ... argument, e.g.as normalizeCounts(x, mc.cores=2). The multicore argument can further be used to temporarily disable the parallel estimation by setting it to FALSE.

An object of class TssNorm.

Normalize read data:

normalizeCounts:: signature(x="TssData")

normalizeCounts(x, ...)

Maintainer: Julian Gehring <julian.gehring@fdm.uni-freiburg.de>

Classes: TssData, TssNorm, TssResult

Methods: segmentizeCounts, normalizeCounts, identifyStartSites, get-methods, plot-methods, asRangedData-methods

Functions: subtract-functions

Data set: physcoCounts

Package: TSSi-package

## preceding steps
example(segmentizeCounts)

## normalize data, w/o and w/ fitting
yRatio <- normalizeCounts(x)
yFit <- normalizeCounts(x, fit=TRUE)

yFit

## Not run: 
## parallel computation
library(parallel)
yFit <- normalizeCounts(x, fit=TRUE, mc.ncores=2)

## End(Not run)