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|
%global _empty_manifest_terminate_build 0
Name: python-methylprep
Version: 1.7.1
Release: 1
Summary: Python-based Illumina methylation array preprocessing software
License: MIT
URL: https://github.com/FOXOBioScience/methylprep
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/6b/58/677b958474072bccae801805608fb8a700ebdafdda7fc86c2e5a66ce165a/methylprep-1.7.1.tar.gz
BuildArch: noarch
Requires: python3-pyparsing
Requires: python3-numpy
Requires: python3-pandas
Requires: python3-scipy
Requires: python3-statsmodels
Requires: python3-tqdm
Requires: python3-bs4
Requires: python3-lxml
Requires: python3-requests
Requires: python3-methylcheck
Requires: python3-pytest
Requires: python3-pytest-mock
Requires: python3-matplotlib
Requires: python3-scikit-learn
Requires: python3-openpyxl
Requires: python3-coverage
%description
`methylprep` is a python package for processing Illumina methylation array data.
View on [ReadTheDocs.](https://life-epigenetics-methylprep.readthedocs-hosted.com/en/latest/)
[](https://github.com/FoxoTech/methylprep/actions/workflows/ci.yml) [](https://life-epigenetics-methylprep.readthedocs-hosted.com/en/latest/) [](https://opensource.org/licenses/MIT) [](https://circleci.com/gh/FoxoTech/methylprep) [](https://www.codacy.com/gh/FoxoTech/methylprep/dashboard?utm_source=github.com&utm_medium=referral&utm_content=FoxoTech/methylprep&utm_campaign=Badge_Grade) [](https://coveralls.io/github/FoxoTech/methylprep) [](https://pypi.org/project/methylprep/)
## Methylprep is part of the methylsuite
`methylprep` is part of the [methylsuite](https://pypi.org/project/methylsuite/) of python packages that provide functions to process and analyze DNA methylation data from Illumina's Infinium arrays (27k, 450k, and EPIC, as well as mouse arrays). The `methylprep` package contains functions for processing raw data files from arrays and downloading/processing public data sets from GEO (the NIH Gene Expression Omnibus database repository), or from ArrayExpress. It contains both a command line interface (CLI) for processing data from local files, and a set of functions for building a custom pipeline in a jupyter notebook or python scripting environment. The aim is to offer a standard process, with flexibility for those who want it.
`methylprep` data processing has also been tested and benchmarked to match the outputs of two popular R packages: [sesame](https://bioconductor.org/packages/release/bioc/html/sesame.html) (v1.10.4) and [minfi](https://bioconductor.org/packages/release/bioc/html/minfi.html) (v1.38).
## Methylsuite package components
You should install all three components, as they work together. The parts include:
- `methylprep`: (this package) for processing `idat` files or downloading GEO datasets from NIH. Processing steps include
- infer type-I channel switch
- NOOB (normal-exponential convolution on out-of-band probe data)
- poobah (p-value with out-of-band array hybridization, for filtering lose signal-to-noise probes)
- qualityMask (to exclude historically less reliable probes)
- nonlinear dye bias correction (AKA signal quantile normalization between red/green channels across a sample)
- calculate beta-value, m-value, or copy-number matrix
- large batch memory management, by splitting it up into smaller batches during processing
- `methylcheck`: for quality control (QC) and analysis, including
- functions for filtering out unreliable probes, based on the published literature
- Note that `methylprep process` will exclude a set of unreliable probes by default. You can disable that using the --no_quality_mask option from CLI.
- sample outlier detection
- array level QC plots, based on Genome Studio functions
- a python clone of Illumina's Bead Array Controls Reporter software (QC)
- data visualization functions based on `seaborn` and `matplotlib` graphic libraries.
- predict sex of human samples from probes
- interactive method for assigning samples to groups, based on array data, in a Jupyter notebook
- `methylize` provides more analysis and interpretation functions
- differentially methylated probe statistics (between treatment and control samples)
- volcano plots (which probes are the most different?)
- manhattan plots (where in genome are the differences?)
## Installation
`methylprep` maintains configuration files for your Python package manager of choice: [pipenv](https://pipenv.readthedocs.io/en/latest/) or [pip](https://pip.pypa.io/en/stable/). Conda install is coming soon.
```shell
>>> pip install methylprep
```
or if you want to install all three packages at once:
```shell
>>> pip install methylsuite
```
## Tutorials and Guides
If you're new to DNA methylation analysis, we recommend reading through [this introduction](docs/introduction/introduction.md) in order get the background knowledge needed to best utilize `methylprep` effectively. Otherwise, you're ready to use `methylprep` for:
<br>
- processing [your own methylation data](docs/general_walkthrough.md#processing-your-own-data)
- downloading [unprocessed data](docs/general_walkthrough.md#downloading-from-geo) (like IDAT files) from GEO.
- downloading [preprocessed data](docs/special_cases.md#using-beta-bake-for-preprocessed-data) (like beta values) from GEO.
- building a composite dataset [using control samples](docs/special_cases.md#building-a-composite-dataset-using-meta-data) from GEO.
- building a composite dataset from GEO data [with any keyword you choose](docs/special_cases.md#building-a-composite-dataset-with-alert-and-composite) (e.g. combining all GEO datasets that have methylation data from patients with brain cancer).
<!-- Add link to methods paper when available -->
%package -n python3-methylprep
Summary: Python-based Illumina methylation array preprocessing software
Provides: python-methylprep
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-methylprep
`methylprep` is a python package for processing Illumina methylation array data.
View on [ReadTheDocs.](https://life-epigenetics-methylprep.readthedocs-hosted.com/en/latest/)
[](https://github.com/FoxoTech/methylprep/actions/workflows/ci.yml) [](https://life-epigenetics-methylprep.readthedocs-hosted.com/en/latest/) [](https://opensource.org/licenses/MIT) [](https://circleci.com/gh/FoxoTech/methylprep) [](https://www.codacy.com/gh/FoxoTech/methylprep/dashboard?utm_source=github.com&utm_medium=referral&utm_content=FoxoTech/methylprep&utm_campaign=Badge_Grade) [](https://coveralls.io/github/FoxoTech/methylprep) [](https://pypi.org/project/methylprep/)
## Methylprep is part of the methylsuite
`methylprep` is part of the [methylsuite](https://pypi.org/project/methylsuite/) of python packages that provide functions to process and analyze DNA methylation data from Illumina's Infinium arrays (27k, 450k, and EPIC, as well as mouse arrays). The `methylprep` package contains functions for processing raw data files from arrays and downloading/processing public data sets from GEO (the NIH Gene Expression Omnibus database repository), or from ArrayExpress. It contains both a command line interface (CLI) for processing data from local files, and a set of functions for building a custom pipeline in a jupyter notebook or python scripting environment. The aim is to offer a standard process, with flexibility for those who want it.
`methylprep` data processing has also been tested and benchmarked to match the outputs of two popular R packages: [sesame](https://bioconductor.org/packages/release/bioc/html/sesame.html) (v1.10.4) and [minfi](https://bioconductor.org/packages/release/bioc/html/minfi.html) (v1.38).
## Methylsuite package components
You should install all three components, as they work together. The parts include:
- `methylprep`: (this package) for processing `idat` files or downloading GEO datasets from NIH. Processing steps include
- infer type-I channel switch
- NOOB (normal-exponential convolution on out-of-band probe data)
- poobah (p-value with out-of-band array hybridization, for filtering lose signal-to-noise probes)
- qualityMask (to exclude historically less reliable probes)
- nonlinear dye bias correction (AKA signal quantile normalization between red/green channels across a sample)
- calculate beta-value, m-value, or copy-number matrix
- large batch memory management, by splitting it up into smaller batches during processing
- `methylcheck`: for quality control (QC) and analysis, including
- functions for filtering out unreliable probes, based on the published literature
- Note that `methylprep process` will exclude a set of unreliable probes by default. You can disable that using the --no_quality_mask option from CLI.
- sample outlier detection
- array level QC plots, based on Genome Studio functions
- a python clone of Illumina's Bead Array Controls Reporter software (QC)
- data visualization functions based on `seaborn` and `matplotlib` graphic libraries.
- predict sex of human samples from probes
- interactive method for assigning samples to groups, based on array data, in a Jupyter notebook
- `methylize` provides more analysis and interpretation functions
- differentially methylated probe statistics (between treatment and control samples)
- volcano plots (which probes are the most different?)
- manhattan plots (where in genome are the differences?)
## Installation
`methylprep` maintains configuration files for your Python package manager of choice: [pipenv](https://pipenv.readthedocs.io/en/latest/) or [pip](https://pip.pypa.io/en/stable/). Conda install is coming soon.
```shell
>>> pip install methylprep
```
or if you want to install all three packages at once:
```shell
>>> pip install methylsuite
```
## Tutorials and Guides
If you're new to DNA methylation analysis, we recommend reading through [this introduction](docs/introduction/introduction.md) in order get the background knowledge needed to best utilize `methylprep` effectively. Otherwise, you're ready to use `methylprep` for:
<br>
- processing [your own methylation data](docs/general_walkthrough.md#processing-your-own-data)
- downloading [unprocessed data](docs/general_walkthrough.md#downloading-from-geo) (like IDAT files) from GEO.
- downloading [preprocessed data](docs/special_cases.md#using-beta-bake-for-preprocessed-data) (like beta values) from GEO.
- building a composite dataset [using control samples](docs/special_cases.md#building-a-composite-dataset-using-meta-data) from GEO.
- building a composite dataset from GEO data [with any keyword you choose](docs/special_cases.md#building-a-composite-dataset-with-alert-and-composite) (e.g. combining all GEO datasets that have methylation data from patients with brain cancer).
<!-- Add link to methods paper when available -->
%package help
Summary: Development documents and examples for methylprep
Provides: python3-methylprep-doc
%description help
`methylprep` is a python package for processing Illumina methylation array data.
View on [ReadTheDocs.](https://life-epigenetics-methylprep.readthedocs-hosted.com/en/latest/)
[](https://github.com/FoxoTech/methylprep/actions/workflows/ci.yml) [](https://life-epigenetics-methylprep.readthedocs-hosted.com/en/latest/) [](https://opensource.org/licenses/MIT) [](https://circleci.com/gh/FoxoTech/methylprep) [](https://www.codacy.com/gh/FoxoTech/methylprep/dashboard?utm_source=github.com&utm_medium=referral&utm_content=FoxoTech/methylprep&utm_campaign=Badge_Grade) [](https://coveralls.io/github/FoxoTech/methylprep) [](https://pypi.org/project/methylprep/)
## Methylprep is part of the methylsuite
`methylprep` is part of the [methylsuite](https://pypi.org/project/methylsuite/) of python packages that provide functions to process and analyze DNA methylation data from Illumina's Infinium arrays (27k, 450k, and EPIC, as well as mouse arrays). The `methylprep` package contains functions for processing raw data files from arrays and downloading/processing public data sets from GEO (the NIH Gene Expression Omnibus database repository), or from ArrayExpress. It contains both a command line interface (CLI) for processing data from local files, and a set of functions for building a custom pipeline in a jupyter notebook or python scripting environment. The aim is to offer a standard process, with flexibility for those who want it.
`methylprep` data processing has also been tested and benchmarked to match the outputs of two popular R packages: [sesame](https://bioconductor.org/packages/release/bioc/html/sesame.html) (v1.10.4) and [minfi](https://bioconductor.org/packages/release/bioc/html/minfi.html) (v1.38).
## Methylsuite package components
You should install all three components, as they work together. The parts include:
- `methylprep`: (this package) for processing `idat` files or downloading GEO datasets from NIH. Processing steps include
- infer type-I channel switch
- NOOB (normal-exponential convolution on out-of-band probe data)
- poobah (p-value with out-of-band array hybridization, for filtering lose signal-to-noise probes)
- qualityMask (to exclude historically less reliable probes)
- nonlinear dye bias correction (AKA signal quantile normalization between red/green channels across a sample)
- calculate beta-value, m-value, or copy-number matrix
- large batch memory management, by splitting it up into smaller batches during processing
- `methylcheck`: for quality control (QC) and analysis, including
- functions for filtering out unreliable probes, based on the published literature
- Note that `methylprep process` will exclude a set of unreliable probes by default. You can disable that using the --no_quality_mask option from CLI.
- sample outlier detection
- array level QC plots, based on Genome Studio functions
- a python clone of Illumina's Bead Array Controls Reporter software (QC)
- data visualization functions based on `seaborn` and `matplotlib` graphic libraries.
- predict sex of human samples from probes
- interactive method for assigning samples to groups, based on array data, in a Jupyter notebook
- `methylize` provides more analysis and interpretation functions
- differentially methylated probe statistics (between treatment and control samples)
- volcano plots (which probes are the most different?)
- manhattan plots (where in genome are the differences?)
## Installation
`methylprep` maintains configuration files for your Python package manager of choice: [pipenv](https://pipenv.readthedocs.io/en/latest/) or [pip](https://pip.pypa.io/en/stable/). Conda install is coming soon.
```shell
>>> pip install methylprep
```
or if you want to install all three packages at once:
```shell
>>> pip install methylsuite
```
## Tutorials and Guides
If you're new to DNA methylation analysis, we recommend reading through [this introduction](docs/introduction/introduction.md) in order get the background knowledge needed to best utilize `methylprep` effectively. Otherwise, you're ready to use `methylprep` for:
<br>
- processing [your own methylation data](docs/general_walkthrough.md#processing-your-own-data)
- downloading [unprocessed data](docs/general_walkthrough.md#downloading-from-geo) (like IDAT files) from GEO.
- downloading [preprocessed data](docs/special_cases.md#using-beta-bake-for-preprocessed-data) (like beta values) from GEO.
- building a composite dataset [using control samples](docs/special_cases.md#building-a-composite-dataset-using-meta-data) from GEO.
- building a composite dataset from GEO data [with any keyword you choose](docs/special_cases.md#building-a-composite-dataset-with-alert-and-composite) (e.g. combining all GEO datasets that have methylation data from patients with brain cancer).
<!-- Add link to methods paper when available -->
%prep
%autosetup -n methylprep-1.7.1
%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-methylprep -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 1.7.1-1
- Package Spec generated
|
