%global _empty_manifest_terminate_build 0
Name: python-chromosight
Version: 1.6.3
Release: 1
Summary: Detect loops (and other patterns) in Hi-C contact maps.
License: MIT
URL: https://github.com/koszullab/chromosight
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/e5/a6/302010f5ec174023ae984e5cb76413a7cb7129a19f8b7e503699da14c52a/chromosight-1.6.3.tar.gz
BuildArch: noarch
Requires: python3-cooler
Requires: python3-docopt
Requires: python3-jsonschema
Requires: python3-matplotlib
Requires: python3-numpy
Requires: python3-scikit-learn
Requires: python3-scipy
%description
# Chromosight
[](https://badge.fury.io/py/chromosight) [](http://bioconda.github.io/recipes/chromosight/README.html) [](https://github.com/koszullab/chromosight/actions/workflows/build.yml) [](https://quay.io/repository/biocontainers/chromosight) [](https://codecov.io/gh/koszullab/chromosight) [](https://chromosight.readthedocs.io) [](https://opensource.org/licenses/GPL-3.0) [](https://lgtm.com/projects/g/koszullab/chromosight/context:python)
Python package to detect chromatin loops (and other patterns) in Hi-C contact maps.
* Associated publication: https://www.nature.com/articles/s41467-020-19562-7
* Documentation and analyses examples: https://chromosight.readthedocs.io
* scripts used for the analysis presented in the article https://github.com/koszullab/chromosight_analyses_scripts
## Installation
Stable version with pip:
```sh
pip3 install --user chromosight
```
Stable version with conda:
```sh
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
The two main subcommands of `chromosight` are `detect` and `quantify`. For more advanced use, there are two additional subcomands: `generate-config` and `list-kernels`. To get the list and description of those subcommands, you can always run:
```bash
chromosight --help
```
Pattern detection is done using the `detect` subcommand. The `quantify` subcommand is used to compute pattern matching scores for a list of 2D coordinates on a Hi-C matrix. 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 `list-kernels` command is used to view informations about the available patterns.
### 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. You can then have a look at the output files generated.
### Important options
When running `chromosight detect`, there are a handful parameters which are especially important:
* `--min-dist`: Minimum genomic distance from which to detect patterns. For loops, this means the smallest loop size accepted (i.e. distance between the two anchors).
* `--max-dist`: Maximum genomic distance from which to detect patterns. Increasing also increases runtime and memory use.
* `--pearson`: Detection threshold. Decrease to allow a greater number of pattern detected (with potentially more false positives). Setting a very low value may actually reduce the number of detected patterns. This is due to the algorithm which might merge neighbouring patterns.
* `--perc-zero`: Proportion of zero pixels allowed in a window for detection. If you have low coverage, increasing this value may improve results.
### Example
To detect all chromosome loops with sizes between 2kb and 200kb using 8 parallel threads:
```bash
chromosight detect --threads 8 --min-dist 20000 --max-dist 200000 hic_data.cool output_prefix
```
## 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}] [--norm={auto,raw,force}]
[--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]
chromosight generate-config [--preset loops] [--click contact_map]
[--norm={auto,raw,norm}] [--win-size=auto] [--n-mads=5]
[--threads=1]
chromosight quantify [--inter] [--pattern=loops] [--subsample=no]
[--win-fmt=json] [--kernel-config=FILE] [--norm={auto,raw,norm}]
[--threads=1] [--n-mads=5] [--win-size=auto]
[--perc-undetected=auto] [--perc-zero=auto]
[--no-plotting] [--tsvd]
chromosight list-kernels [--long] [--mat] [--name=kernel_name]
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.
list-kernels:
Prints information about available kernels.
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://open2c.github.io/cooler/
Most other Hi-C data formats (hic, homer, hic-pro), can be converted to cool using [hicexplorer's hicConvertFormat](https://hicexplorer.readthedocs.io/en/latest/content/tools/hicConvertFormat.html) or [hic2cool](https://github.com/4dn-dcic/hic2cool). Bedgraph2 format can be converted directly using cooler with the command `cooler load -f bg2 : in.bg2.gz out.cool`. For more informations, see the [cooler documentation](https://cooler.readthedocs.io/en/latest/cli.html#cooler-load)
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](https://chromosight.readthedocs.io/en/stable/TUTORIAL.html#quantification).
## Output
Three files are generated by chromosight's `detect` and `quantify` commands. Their filenames are determined by the value of the `` argument:
* `prefix.tsv`: List of genomic coordinates, bin ids and correlation scores for the pattern identified
* `prefix.json`: JSON file containing the windows (of the same size as the kernel used) around the patterns from pattern.txt
* `prefix.pdf`: Plot showing the pileup (average) window of all detected patterns. Plot generation can be disabled using the `--no-plotting` option.
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.
> Note: the p-values and q-values provided in prefix.tsv should not be used as a criterion for filtering and are only useful for ranking calls. Their values are obtained from a Pearson correlation test and could be biased due to the dependence between contact values in the window.
### 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 `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:
```bash
nose2 -s tests/
```
### FAQ
Questions from previous users are available in the [github issues](https://github.com/koszullab/chromosight/issues?q=label%3Aquestion). You can open a new issue for your question if it is not already covered.
### Citation
When using Chromosight in you research, please cite the pubication: https://www.nature.com/articles/s41467-020-19562-7
%package -n python3-chromosight
Summary: Detect loops (and other patterns) in Hi-C contact maps.
Provides: python-chromosight
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-chromosight
# Chromosight
[](https://badge.fury.io/py/chromosight) [](http://bioconda.github.io/recipes/chromosight/README.html) [](https://github.com/koszullab/chromosight/actions/workflows/build.yml) [](https://quay.io/repository/biocontainers/chromosight) [](https://codecov.io/gh/koszullab/chromosight) [](https://chromosight.readthedocs.io) [](https://opensource.org/licenses/GPL-3.0) [](https://lgtm.com/projects/g/koszullab/chromosight/context:python)
Python package to detect chromatin loops (and other patterns) in Hi-C contact maps.
* Associated publication: https://www.nature.com/articles/s41467-020-19562-7
* Documentation and analyses examples: https://chromosight.readthedocs.io
* scripts used for the analysis presented in the article https://github.com/koszullab/chromosight_analyses_scripts
## Installation
Stable version with pip:
```sh
pip3 install --user chromosight
```
Stable version with conda:
```sh
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
The two main subcommands of `chromosight` are `detect` and `quantify`. For more advanced use, there are two additional subcomands: `generate-config` and `list-kernels`. To get the list and description of those subcommands, you can always run:
```bash
chromosight --help
```
Pattern detection is done using the `detect` subcommand. The `quantify` subcommand is used to compute pattern matching scores for a list of 2D coordinates on a Hi-C matrix. 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 `list-kernels` command is used to view informations about the available patterns.
### 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. You can then have a look at the output files generated.
### Important options
When running `chromosight detect`, there are a handful parameters which are especially important:
* `--min-dist`: Minimum genomic distance from which to detect patterns. For loops, this means the smallest loop size accepted (i.e. distance between the two anchors).
* `--max-dist`: Maximum genomic distance from which to detect patterns. Increasing also increases runtime and memory use.
* `--pearson`: Detection threshold. Decrease to allow a greater number of pattern detected (with potentially more false positives). Setting a very low value may actually reduce the number of detected patterns. This is due to the algorithm which might merge neighbouring patterns.
* `--perc-zero`: Proportion of zero pixels allowed in a window for detection. If you have low coverage, increasing this value may improve results.
### Example
To detect all chromosome loops with sizes between 2kb and 200kb using 8 parallel threads:
```bash
chromosight detect --threads 8 --min-dist 20000 --max-dist 200000 hic_data.cool output_prefix
```
## 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}] [--norm={auto,raw,force}]
[--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]
chromosight generate-config [--preset loops] [--click contact_map]
[--norm={auto,raw,norm}] [--win-size=auto] [--n-mads=5]
[--threads=1]
chromosight quantify [--inter] [--pattern=loops] [--subsample=no]
[--win-fmt=json] [--kernel-config=FILE] [--norm={auto,raw,norm}]
[--threads=1] [--n-mads=5] [--win-size=auto]
[--perc-undetected=auto] [--perc-zero=auto]
[--no-plotting] [--tsvd]
chromosight list-kernels [--long] [--mat] [--name=kernel_name]
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.
list-kernels:
Prints information about available kernels.
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://open2c.github.io/cooler/
Most other Hi-C data formats (hic, homer, hic-pro), can be converted to cool using [hicexplorer's hicConvertFormat](https://hicexplorer.readthedocs.io/en/latest/content/tools/hicConvertFormat.html) or [hic2cool](https://github.com/4dn-dcic/hic2cool). Bedgraph2 format can be converted directly using cooler with the command `cooler load -f bg2 : in.bg2.gz out.cool`. For more informations, see the [cooler documentation](https://cooler.readthedocs.io/en/latest/cli.html#cooler-load)
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](https://chromosight.readthedocs.io/en/stable/TUTORIAL.html#quantification).
## Output
Three files are generated by chromosight's `detect` and `quantify` commands. Their filenames are determined by the value of the `` argument:
* `prefix.tsv`: List of genomic coordinates, bin ids and correlation scores for the pattern identified
* `prefix.json`: JSON file containing the windows (of the same size as the kernel used) around the patterns from pattern.txt
* `prefix.pdf`: Plot showing the pileup (average) window of all detected patterns. Plot generation can be disabled using the `--no-plotting` option.
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.
> Note: the p-values and q-values provided in prefix.tsv should not be used as a criterion for filtering and are only useful for ranking calls. Their values are obtained from a Pearson correlation test and could be biased due to the dependence between contact values in the window.
### 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 `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:
```bash
nose2 -s tests/
```
### FAQ
Questions from previous users are available in the [github issues](https://github.com/koszullab/chromosight/issues?q=label%3Aquestion). You can open a new issue for your question if it is not already covered.
### Citation
When using Chromosight in you research, please cite the pubication: https://www.nature.com/articles/s41467-020-19562-7
%package help
Summary: Development documents and examples for chromosight
Provides: python3-chromosight-doc
%description help
# Chromosight
[](https://badge.fury.io/py/chromosight) [](http://bioconda.github.io/recipes/chromosight/README.html) [](https://github.com/koszullab/chromosight/actions/workflows/build.yml) [](https://quay.io/repository/biocontainers/chromosight) [](https://codecov.io/gh/koszullab/chromosight) [](https://chromosight.readthedocs.io) [](https://opensource.org/licenses/GPL-3.0) [](https://lgtm.com/projects/g/koszullab/chromosight/context:python)
Python package to detect chromatin loops (and other patterns) in Hi-C contact maps.
* Associated publication: https://www.nature.com/articles/s41467-020-19562-7
* Documentation and analyses examples: https://chromosight.readthedocs.io
* scripts used for the analysis presented in the article https://github.com/koszullab/chromosight_analyses_scripts
## Installation
Stable version with pip:
```sh
pip3 install --user chromosight
```
Stable version with conda:
```sh
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
The two main subcommands of `chromosight` are `detect` and `quantify`. For more advanced use, there are two additional subcomands: `generate-config` and `list-kernels`. To get the list and description of those subcommands, you can always run:
```bash
chromosight --help
```
Pattern detection is done using the `detect` subcommand. The `quantify` subcommand is used to compute pattern matching scores for a list of 2D coordinates on a Hi-C matrix. 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 `list-kernels` command is used to view informations about the available patterns.
### 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. You can then have a look at the output files generated.
### Important options
When running `chromosight detect`, there are a handful parameters which are especially important:
* `--min-dist`: Minimum genomic distance from which to detect patterns. For loops, this means the smallest loop size accepted (i.e. distance between the two anchors).
* `--max-dist`: Maximum genomic distance from which to detect patterns. Increasing also increases runtime and memory use.
* `--pearson`: Detection threshold. Decrease to allow a greater number of pattern detected (with potentially more false positives). Setting a very low value may actually reduce the number of detected patterns. This is due to the algorithm which might merge neighbouring patterns.
* `--perc-zero`: Proportion of zero pixels allowed in a window for detection. If you have low coverage, increasing this value may improve results.
### Example
To detect all chromosome loops with sizes between 2kb and 200kb using 8 parallel threads:
```bash
chromosight detect --threads 8 --min-dist 20000 --max-dist 200000 hic_data.cool output_prefix
```
## 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}] [--norm={auto,raw,force}]
[--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]
chromosight generate-config [--preset loops] [--click contact_map]
[--norm={auto,raw,norm}] [--win-size=auto] [--n-mads=5]
[--threads=1]
chromosight quantify [--inter] [--pattern=loops] [--subsample=no]
[--win-fmt=json] [--kernel-config=FILE] [--norm={auto,raw,norm}]
[--threads=1] [--n-mads=5] [--win-size=auto]
[--perc-undetected=auto] [--perc-zero=auto]
[--no-plotting] [--tsvd]
chromosight list-kernels [--long] [--mat] [--name=kernel_name]
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.
list-kernels:
Prints information about available kernels.
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://open2c.github.io/cooler/
Most other Hi-C data formats (hic, homer, hic-pro), can be converted to cool using [hicexplorer's hicConvertFormat](https://hicexplorer.readthedocs.io/en/latest/content/tools/hicConvertFormat.html) or [hic2cool](https://github.com/4dn-dcic/hic2cool). Bedgraph2 format can be converted directly using cooler with the command `cooler load -f bg2 : in.bg2.gz out.cool`. For more informations, see the [cooler documentation](https://cooler.readthedocs.io/en/latest/cli.html#cooler-load)
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](https://chromosight.readthedocs.io/en/stable/TUTORIAL.html#quantification).
## Output
Three files are generated by chromosight's `detect` and `quantify` commands. Their filenames are determined by the value of the `` argument:
* `prefix.tsv`: List of genomic coordinates, bin ids and correlation scores for the pattern identified
* `prefix.json`: JSON file containing the windows (of the same size as the kernel used) around the patterns from pattern.txt
* `prefix.pdf`: Plot showing the pileup (average) window of all detected patterns. Plot generation can be disabled using the `--no-plotting` option.
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.
> Note: the p-values and q-values provided in prefix.tsv should not be used as a criterion for filtering and are only useful for ranking calls. Their values are obtained from a Pearson correlation test and could be biased due to the dependence between contact values in the window.
### 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 `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:
```bash
nose2 -s tests/
```
### FAQ
Questions from previous users are available in the [github issues](https://github.com/koszullab/chromosight/issues?q=label%3Aquestion). You can open a new issue for your question if it is not already covered.
### Citation
When using Chromosight in you research, please cite the pubication: https://www.nature.com/articles/s41467-020-19562-7
%prep
%autosetup -n chromosight-1.6.3
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-chromosight -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon May 15 2023 Python_Bot - 1.6.3-1
- Package Spec generated