chicViewpointBackgroundModel¶
chicViewpointBackgroundModel computes for all given samples with all reference points a background model. For all relative distances to a reference point a negative binomial distribution is fitted. Moreover, for each relative distance to a reference point the average value for this location is computed. Both background models are used, the first one for p-value and significance computation, the second one to filter out interactions with a less x-fold over mean.
The background distributions are fixed at –fixateRange i.e. all distances lower / higher than this value use the fixed background distribution.
An example usage is:
$ chicViewpointBackgroundModel –matrices matrix1.cool matrix2.cool matrix3.cool –referencePoints referencePointsFile.bed –range 20000 40000 –outFileName background_model.bed
usage: chicViewpointBackgroundModel --matrices MATRICES [MATRICES ...]
--referencePoints REFERENCEPOINTS
[--averageContactBin AVERAGECONTACTBIN]
[--outFileName OUTFILENAME]
[--threads THREADS]
[--fixateRange FIXATERANGE] [--help]
[--version]
Required arguments¶
- --matrices, -m
The input matrices (samples) to build the background model on.
- --referencePoints, -rp
Bed file contains all reference points which should be used to build the background model.
Optional arguments¶
- --averageContactBin
Average the contacts of n bins via a sliding window approach.
Default: 5
- --outFileName, -o
The name of the background model file
Default: “background_model.bed”
- --threads, -t
Number of threads. Using the python multiprocessing module.
Default: 4
- --fixateRange, -fs
Fixate score of backgroundmodel starting at distance x. E.g. all values greater 500kb are set to the value of the 500kb bin.
Default: 500000
- --version
show program’s version number and exit