Reimplementation of the hicrep with added support for sparse matrix and multiple chromosomes.
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
You can install the package using pip:
pip install --user hicreppy
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
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
All contributions are welcome. We use the numpy standard for docstrings when documenting functions.
The code formatting standard we use is black, with --line-length=79 to follow PEP8 recommendations. We use
pytest with the
pytest-pylint plugins as our testing framework. Ideally, new functions should have associated unit tests, placed in the
To test the code, you can run:
pytest --doctest-modules --pylint --pylint-error-types=EF --pylint-rcfile=.pylintrc hicreppy tests
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