# chicViewpointBackgroundModel¶

chicViewpointBackgroundModel computes a background model for all given samples with all reference points. For all relative distances to a reference point a negative binomial distribution is fitted. In addition, 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 smaller x-fold over the mean.

The background distributions are fixed at –fixateRange, i.e. all distances lower or 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]
[--truncateZeros]
[--outFileName OUTFILENAME]
[--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).

Default: 5

--truncateZeros, -tz

Truncates the zeros before the distributions are fitted. Use it in case you observe an over dispersion.

Default: False

--outFileName, -o

The name of the background model file (Default: “background_model.txt”).

Default: “background_model.txt”

Number of threads (uses the python multiprocessing module) (Default: 4).

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).

Default: 500000

--version

show program’s version number and exit