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@@ -0,0 +1 @@ +/methylprep-1.7.1.tar.gz diff --git a/python-methylprep.spec b/python-methylprep.spec new file mode 100644 index 0000000..0490d14 --- /dev/null +++ b/python-methylprep.spec @@ -0,0 +1,289 @@ +%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 @@ -0,0 +1 @@ +8eec7745bc2c72c7564abc5f6eeeb694 methylprep-1.7.1.tar.gz |