Normalize sequence read count data.
1 2 
x 
Object of class 
fun 
Function used to average over replicates (default:

offset 
Integer defining the number of bases add to the ends of
each segment with 
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 
... 
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 wellestablished 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 loglikelihood 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:
signature(x="TssData")
normalizeCounts(x, ...)
Maintainer: Julian Gehring <julian.gehring@fdm.unifreiburg.de>
Classes:
TssData
, TssNorm
,
TssResult
Methods:
segmentizeCounts
, normalizeCounts
,
identifyStartSites
, getmethods
,
plotmethods
, asRangedDatamethods
Functions:
subtractfunctions
Data set:
physcoCounts
Package:
TSSipackage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  ## 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)

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
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