Metadata-Version: 2.1
Name: hicreppy
Version: 0.0.6
Summary: Reimplementation of the hicrep with added support for sparse matrix and multiple chromosomes.
Home-page: https://github.com/cmdoret/hicreppy
Author: cmdoret
Author-email: cyril.matthey-doret@pasteur.fr
License: GPLv2
Description: # hicreppy
        **cmdoret**
        
        
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        This is a python reimplementation of hicrep's algorithm with added support for sparse matrices (in .cool format). 
        
        hicrep measures similarity between Hi-C samples by computing a stratum-adjusted correlation coefficient (SCC). In this implementation, the SCC is computed separately for each chromosome and the chromosome length-weighted average of SCCs is computed.
        
        hicrep is published at:
        > HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient. Tao Yang, Feipeng Zhang, Galip Gurkan Yardimci, Ross C Hardison, William Stafford Noble, Feng Yue, Qunhua Li, 2017, Genome Research, doi: 10.1101/gr.220640.117
        
        The original implementation, in R can be found at https://github.com/MonkeyLB/hicrep
        
        ### Installation
        
        You can install the package using pip:
        
        ```bash
        pip install --user hicreppy
        ```
        
        ### Usage
        
        To find the optimal value for smoothing parameter h, you can use the htrain subcommand:
        
        ```
        
        Usage: hicreppy htrain [OPTIONS] COOL1 COOL2
        
          Find the optimal value for smoothing parameter h. The optimal h-value is
          printed to stdout. Run informations are printed to stderr.
        
        Options:
          -r, --h-max INTEGER     Maximum value of the smoothing parameter h to
                                  explore. All consecutive integer values from 0 to
                                  this value will be tested.  [default: 10]
          -m, --max-dist INTEGER  Maximum distance at which to compute the SCC, in
                                  basepairs.  [default: 100000]
          -b, --blacklist TEXT    Exclude those chromosomes in the analysis. List of
                                  comma-separated chromosome names.
          -w, --whitelist TEXT    Only include those chromosomes in the analysis. List
                                  of comma-separated chromosome names.
          --help                  Show this message and exit.
        
        ```
        
        To compute the SCC between two matrices, use the scc subcommand. The optimal h value obtained with htrain should be provided to the flag `-v`:
        
        ```
        
        Usage: hicreppy scc [OPTIONS] COOL1 COOL2
        
          Compute the stratum-adjusted correlation coefficient for input matrices
        
        Options:
          -v, --h-value INTEGER    Value of the smoothing parameter h to use. Should
                                   be an integer value >= 0.  [default: 10]
          -m, --max-dist INTEGER   Maximum distance at which to compute the SCC, in
                                   basepairs.  [default: 100000]
          -s, --subsample INTEGER  Subsample contacts from both matrices to target
                                   value. Leave to 0 to disable subsampling.
                                   [default: 0]
          -b, --blacklist TEXT     Exclude those chromosomes in the analysis. List of
                                   comma-separated chromosome names.
          -w, --whitelist TEXT     Only include those chromosomes in the analysis.
                                   List of comma-separated chromosome names.
          --help                   Show this message and exit.
        ```
        
        When running multiple pairwise comparisons, compute the optimal h value once between two highly similar samples and reuse the h value for all `scc` commands
        
        ### Contributing
        
        All contributions are welcome. We use the [numpy standard](https://numpydoc.readthedocs.io/en/latest/format.html) for docstrings when documenting functions.
        
        The code formatting standard we use is [black](https://github.com/psf/black), with --line-length=79 to follow PEP8 recommendations. We use `pytest` with the `pytest-doctest` and `pytest-pylint` plugins as our testing framework. Ideally, new functions should have associated unit tests, placed in the `tests` folder.
        
        To test the code, you can run:
        
        ```bash
        pytest --doctest-modules --pylint --pylint-error-types=EF --pylint-rcfile=.pylintrc hicreppy tests
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Requires-Python: >=3.6
Description-Content-Type: text/markdown
