chromosight 1.1.2

Detect loops (and other patterns) in Hi-C contact maps.

Chromosight

Python package to detect chromatin loops (and other patterns) in Hi-C contact maps.

Preprint can be found on https://www.biorxiv.org/content/10.1101/2020.03.08.981910v2

Installation

Stable version with pip:

pip3 install --user chromosight

Stable version with conda:

conda install -c bioconda -c conda-forge chromosight

or, if you want to get the latest development version:

pip3 install --user -e git+https://github.com/koszullab/chromosight.git@master#egg=chromosight

Usage

chromosight has 3 subcommands: detect, quantify and generate-config. To get the list and description of those subcommands, you can always run:

chromosight --help

Pattern detection is done using the detect subcommand. The generate-config subcommand is used to create a new type of pattern that can then be fed to detect using the --custom-kernel option. The quantify subcommand is used to compute pattern matching scores for a list of 2D coordinates on a Hi-C matrix.

Get started

To get a first look at a chromosight run, you can run chromosight test, which will download a test dataset from the github repository and run chromosight detect on it.

Important options

• --min-dist: Minimum distance from which to detect patterns.
• --max-dist: Maximum distance from which to detect patterns. Increasing also increases runtime and memory use.
• --pearson: Decrease to allow a greater number of pattern detected (with potentially more false positives).
• --perc-zero: Proportion of zero pixels allowed in a window for detection.

Example

To detect all chromosome loops with sizes between 2kb and 200kb using 8 parallel threads:

chromosight detect --threads 8 --min-dist 20000 --max-dist 200000 hic_data.cool out_dir

Options

Pattern exploration and detection

Explore and detect patterns (loops, borders, centromeres, etc.) in Hi-C contact
maps with pattern matching.

Usage:
    chromosight detect  [--kernel-config=FILE] [--pattern=loops]
                        [--pearson=auto] [--win-size=auto] [--iterations=auto]
                        [--win-fmt={json,npy}] [--force-norm]
                        [--subsample=no] [--inter] [--tsvd] [--smooth-trend]
                        [--n-mads=5] [--min-dist=0] [--max-dist=auto]
                        [--no-plotting] [--min-separation=auto] [--dump=DIR]
                        [--threads=1] [--perc-zero=auto]
                        [--perc-undetected=auto] <contact_map> [<output>]
    chromosight generate-config [--preset loops] [--click contact_map]
                        [--force-norm] [--win-size=auto] [--n-mads=5]
                        [--threads=1] <prefix>
    chromosight quantify [--inter] [--pattern=loops] [--subsample=no]
                         [--win-fmt=json] [--kernel-config=FILE] [--force-norm]
                         [--threads=1] [--n-mads=5] [--win-size=auto] 
                         [--perc-undetected=auto] [--perc-zero=auto]
                         [--no-plotting] [--tsvd] <bed2d> <contact_map> <output>
    chromosight test

    detect:
        performs pattern detection on a Hi-C contact map via template matching
    generate-config:
        Generate pre-filled config files to use for detect and quantify.
        A config consists of a JSON file describing parameters for the
        analysis and path pointing to kernel matrices files. Those matrices
        files are tsv files with numeric values as kernel to use for
        convolution.
    quantify:
        Given a list of pairs of positions and a contact map, computes the
        correlation coefficients between those positions and the kernel of the
        selected pattern.
    test:
        Download example data and run loop detection on it.

Input

Input Hi-C contact maps should be in cool format. The cool format is an efficient and compact format for Hi-C data based on HDF5. It is maintained by the Mirny lab and documented here: https://mirnylab.github.io/cooler/

Most other Hi-C data formats (hic, homer, hic-pro), can be converted to cool using hicexplorer's hicConvertFormat. Bedgraph2 format can be converted directly using cooler with the command cooler load -f bg2 <chrom.sizes>:<binsize> in.bg2.gz out.cool. For more informations, see the cooler documentation

For chromosight quantify, the bed2d file is a text file with at least 6 tab-separated columns containing pairs of coordinates. The first 6 columns should be chrom start end chrom start end and have no header. Alternatively, the output text file generated by chromosight detect is also accepted. Instructions to generate a bed2d file from a bed file are given in the documentation.

Output

Two files are generated in the output directory (replace pattern by the pattern used, e.g. loops or borders):

• pattern_out.txt: List of genomic coordinates, bin ids and correlation scores for the pattern identified
• pattern_out.json: JSON file containing the windows (of the same size as the kernel used) around the patterns from pattern.txt

Alternatively, one can set the --win-fmt=npy option to dump windows into a npy file instead of JSON. This format can easily be loaded into a 3D array using numpy's np.load function.

Contributing

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 nose2 as our testing framework. Ideally, new functions should have associated unit tests, placed in the tests folder.

To test the code, you can run:

nose2 -s tests/