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 ChIP-Seq experiments, which can serve as a quality control to detect inefficient immuno-precipitation.
Object with ranges indicating the start and end of each read. Currently,
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
signature(x = "list")
A vector with coefficients of variation for each element in
returned, by repeatedly calling the method for
mc.cores to speed up computations with
but be careful as this requires more memory.
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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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