Skip to main content
Join the official 2019 Python Developers SurveyStart the survey!

ChIP-R is a method for assessing the reproducibility of replicated ChIP-seq type experiments. It incorporates the rank product method, a novel thresholding methods, and the use of peak fragmentation return the most reproducible peaks.

Project description

ChIP-R ("chipper")

ChIP-R uses an adaptation of the rank product statistic to assess the reproducibility of ChIP-seq peaks by incorporating information from multiple ChIP-seq replicates and "fragmenting" peak locations to better combine the information present across the replicates.

Install

  • Python3.x with the following packages:
  • Numpy
  • Scipy
  • pyBigWig

To install ChIP-R:

pip install ChIP-R

OR if you want to install from source:

git clone https://github.com/rhysnewell/ChIP-R.git
cd ChIP-R
python3 setup.py install

Usage

In the command line, type in 'chipr -h ' for detailed usage.

$ chipr -h

usage: chipr [-h] -i INPUT [INPUT ...] [-o OUTPUT] [-m MINENTRIES]
         [--rankmethod RANKMETHOD] [--duphandling DUPHANDLING]
         [--seed RANDOM_SEED] [-a ALPHA]

Combine multiple ChIP-seq files and return a union of all peak locations and a
set confident, reproducible peaks as determined by rank product analysis

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT [INPUT ...], --input INPUT [INPUT ...]
                        ChIP-seq input files. These files must be in either
                        narrowPeak, broadPeak, or regionPeak format. Multiple
                        inputs are separeted by a single space
  -o OUTPUT, --output OUTPUT
                        ChIP-seq output filename prefix
  -B, --bigbed          Specify if input files are in BigBed format
  -m MINENTRIES, --minentries MINENTRIES
                        The minimum peaks between replicates required to form
                        an intersection of the peaks Default: 1
  --rankmethod RANKMETHOD
                        The ranking method used to rank peaks within
                        replicates. Options: 'signalvalue', 'pvalue',
                        'qvalue'. Default: pvalue
  --duphandling DUPHANDLING
                        Specifies how to handle entries that are ranked
                        equally within a replicate Can either take the
                        'average' ranks or a 'random' rearrangement of the
                        ordinal ranks Options: 'average', 'random' Default:
                        'average'
  --seed RANDOM_SEED    Specify a seed to be used in conjunction with the
                        'random' option for -duphandling Must be between 0 and
                        1 Default: 0.5
  -a ALPHA, --alpha ALPHA
                        Alpha specifies the user cut-off value for set of
                        reproducible peaks The analysis will still produce
                        results including peaks within the threshold
                        calculatedusing the binomial method Default: 0.05

Example

$ chipr -i input_prefix1.bed input_prefix2.bed input_prefix3.bed input_prefix4.bed -m 2 -o output_prefix   

Output

Important result files:

  • prefixname_ALL.bed: All intersected peaks, ordered from most significant to least (10 columns)
  • prefixname_T2.bed: The tier 2 intersected peaks, the peaks that fall within the binomial threshold (10 columns)
  • prefixname_T1.bed: The tier 1 intersected peaks, the peaks that fall within the user defined threshold (10 columns)
  • prefixname_log.txt: A log containing the number of peaks appearing in each tier.

prefixname.bed file has 10 columns. The output follows the standard peak format for bed files, with the addition of a 10th column that specifies the ranks of the peaks that produced this possible peak. See the toy example below.

chr start end name score strand signalValue p-value q-value
chr1 9118 10409 T3_peak_87823 491 . 15.000000 0.113938 0.712353

Citation

Contact

Authors: Rhys Newell, Michael Piper, Mikael Boden, Alexandra Essebier

Contact: rhys.newell(AT)uq.edu.au

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for ChIP-R, version 1.1.6
Filename, size File type Python version Upload date Hashes
Filename, size ChIP_R-1.1.6-py3-none-any.whl (51.4 kB) File type Wheel Python version py3 Upload date Hashes View hashes
Filename, size ChIP-R-1.1.6.tar.gz (36.2 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page