Cooler is a support library for a sparse, compressed, binary persistent storage format for Hi-C contact matrices, called cool, which is based on HDF5.
Cooler aims to provide the following functionality:
- Generate contact matrices from contact lists at arbitrary resolutions.
- Store contact matrices efficiently in
coolformat based on the widely used HDF5 container format. - Perform out-of-core genome wide contact matrix normalization (a.k.a. balancing)
- Perform fast range queries on a contact matrix.
- Convert contact matrices between formats.
- Provide a clean and well-documented Python API to work with Hi-C data.
To get started:
- Documentation is available here.
- Walkthrough with a Jupyter notebook.
coolfiles from published Hi-C data sets are available atftp://cooler.csail.mit.edu/coolers.
Requirements:
- Python 2.7/3.4+
- libhdf5 and Python packages
numpy,scipy,pandas,h5py. We highly recommend using thecondapackage manager to install scientific packages like these. To get it, you can either install the full Anaconda Python distribution or just the standalone conda package manager.
Install from PyPI using pip.
$ pip install coolerSee the docs for more information.
The cooler library includes utilities for creating and querying cool files and for performing contact matrix balancing on a cool file of any resolution.
$ cooler makebins $CHROMSIZES_FILE $BINSIZE > bins.10kb.bed
$ cooler cload bins.10kb.bed $CONTACTS_FILE out.cool
$ cooler balance -p 10 out.cool
$ cooler dump -b -t pixels --header --join -r chr3:10,000,000-12,000,000 -r2 chr17 out.cool | headchrom1 start1 end1 chrom2 start2 end2 count balanced
chr3 10000000 10010000 chr17 0 10000 1 0.810766
chr3 10000000 10010000 chr17 520000 530000 1 1.2055
chr3 10000000 10010000 chr17 640000 650000 1 0.587372
chr3 10000000 10010000 chr17 900000 910000 1 1.02558
chr3 10000000 10010000 chr17 1030000 1040000 1 0.718195
chr3 10000000 10010000 chr17 1320000 1330000 1 0.803212
chr3 10000000 10010000 chr17 1500000 1510000 1 0.925146
chr3 10000000 10010000 chr17 1750000 1760000 1 0.950326
chr3 10000000 10010000 chr17 1800000 1810000 1 0.745982
See also:
- CLI Reference.
- Jupyter Notebook walkthrough.
The cooler library provides a thin wrapper over the excellent h5py Python interface to HDF5. It supports creation of cooler files and the following types of range queries on the data:
- Tabular selections are retrieved as Pandas DataFrames and Series.
- Matrix selections are retrieved as NumPy arrays or SciPy sparse matrices.
- Metadata is retrieved as a json-serializable Python dictionary.
- Range queries can be supplied using either integer bin indexes or genomic coordinate intervals.
>>> import cooler
>>> import matplotlib.pyplot as plt
>>> c = cooler.Cooler('bigDataset.cool')
>>> resolution = c.info['bin-size']
>>> mat = c.matrix(balance=True).fetch('chr5:10,000,000-15,000,000')
>>> plt.matshow(np.log10(mat), cmap='YlOrRd')>>> import multiprocessing as mp
>>> import h5py
>>> pool = mp.Pool(8)
>>> f = h5py.File('bigDataset.cool', 'r')
>>> weights, stats = cooler.ice.iterative_correction(f, map=pool.map, ignore_diags=3, min_nnz=10)See also:
- API Reference.
- Jupyter Notebook walkthrough.
The cool format implements a simple schema that stores a contact matrix in a sparse representation, crucial for developing robust tools for use on increasingly high resolution Hi-C data sets, including streaming and out-of-core algorithms.
The data tables in a cool file are stored in a columnar representation as HDF5 groups of 1D array datasets of equal length. The contact matrix itself is stored as a single table containing only the nonzero upper triangle pixels.
Pull requests are welcome. The current requirements for testing are nose and mock.
For development, clone and install in "editable" (i.e. development) mode with the -e option. This way you can also pull changes on the fly.
$ git clone https://github.com/mirnylab/cooler.git
$ cd cooler
$ pip install -e .BSD (New)