Description Usage Arguments Details Value Methods Examples
Compute variability of the genomic coverage, measured as standardized SD per thousand sequences (see details). For instance, this can measure how pronounced are the peaks in a ChIPSeq experiments, which can serve as a quality control to detect inefficient immunoprecipitation.
1  ssdCoverage(x, mc.cores=1)

x 
Object with ranges indicating the start and end of each read. Currently, 
mc.cores 
Set 
ssdCoverage first computes the coverage for each sample and computes the standard deviation (SD) of the coverage. However, SD is not an appropriate measure of coverage unevenness, as its expected value is proportional to sqrt(n), where n is the number of reads (this can be seen with simple algebra).
ssdCoverage therefore reports 1000*SD/sqrt(n), which can be interpreted as the standardized SD per thousand sequences.
Numeric vector with coefficients of variation.
signature(x = "IRangesList")
A single coefficient of variation is returned, as a weighted average of the coefficients of variation for each chromosome (weighted according to the chromosome length).
signature(x = "RangedData")
The method for IRangesList
is used on ranges(x)
.
signature(x = "list")
A vector with coefficients of variation for each element in x
are
returned, by repeatedly calling the method for RangedData
objects.
Use mc.cores
to speed up computations with mclapply
,
but be careful as this requires more memory.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  set.seed(1)
#Simulate IP data
peak1 < round(rnorm(500,100,10))
peak1 < RangedData(IRanges(peak1,peak1+38),space='chr1')
peak2 < round(rnorm(500,200,10))
peak2 < RangedData(IRanges(peak2,peak2+38),space='chr1')
ip < rbind(peak1,peak2)
#Generate uniform background
bg < runif(1000,1,300)
bg < RangedData(IRanges(bg,bg+38),space='chr1')
rdl < list(ip,bg)
ssdCoverage(rdl)
giniCoverage(rdl)

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