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PeakError: compute the label error rate of peak calling models

After constructing a database of labels that indicate specific samples and genomic regions with and without peaks in a ChIP-seq experiment, you can compute the label error rate of a peak calling model using this package. This is useful for

  • training peak calling model parameters: choose the parameter values that minimize the number of incorrect labels.
  • testing different peak calling models: compute the label error rate of two or more different peak models. The one with the lowest label error rate is the most accurate in your data.

For more details, please read our paper “Optimizing ChIP-seq peak detectors using visual labels and supervised machine learning” by Hocking et al, Bioinformatics 2017.

R installation


R usage


Command line usage

The exampleData directory contains command line scripts for computing the PeakError for peaks and labels defined in bed files:

cd $(Rscript -e 'cat(system.file("exampleData", package="PeakError"))')
Rscript PeakError_compute.R peaks.bed labels.bed > errors.tsv
Rscript PeakError_summarize.R errors.tsv

On my system I got the following output:

thocking@silene:~/R$ cd $(Rscript -e 'cat(system.file("exampleData", package="PeakError"))')
thocking@silene:~/lib/R/library/PeakError/exampleData$ Rscript PeakError_compute.R peaks.bed labels.bed > errors.tsv
read 28 peaks
read 20 labels
Loading required package: PeakError
computed errors for 20 labels
thocking@silene:~/lib/R/library/PeakError/exampleData$ Rscript PeakError_summarize.R errors.tsv

  noPeaks   peakEnd     peaks peakStart 
        5         5         5         5 
11 / 20 incorrect labels = 55.000000% error rate
7 false positives / 15 possible = 46.666667% false positive rate
4 false negatives / 15 possible = 26.666667% false negative rate
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