| Name | rnanorm JSON |
| Version |
2.1.0
JSON |
| download |
| home_page | |
| Summary | Common RNA-seq normalization methods |
| upload_time | 2023-11-06 07:48:31 |
| maintainer | |
| docs_url | None |
| author | Genialis, Inc. |
| requires_python | <3.13,>=3.8 |
| license | Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. 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For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. 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Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. 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| keywords |
bio
bioinformatics
data science
machine learning
artificial intelligence
python
genialis
rnaseq
normalization
|
| VCS |
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|
====================================
RNA-seq data normalization in Python
====================================
|build| |black| |docs| |pypi_version| |pypi_pyversions| |pypi_downloads|
.. |build| image:: https://github.com/genialis/RNAnorm/actions/workflows/build.yml/badge.svg?branch=main
:target: https://github.com/genialis/RNAnorm/actions?query=branch%3Amain
:alt: Build Status
.. |black| image:: https://img.shields.io/badge/code%20style-black-000000.svg
:target: https://github.com/psf/black
:alt: Code Style Black
.. |docs| image:: https://readthedocs.org/projects/rnanorm/badge/?version=latest
:target: http://rnanorm.readthedocs.io/
:alt: Documentation Status
.. |pypi_version| image:: https://img.shields.io/pypi/v/rnanorm.svg
:target: https://pypi.org/project/rnanorm
:alt: Version on PyPI
.. |pypi_pyversions| image:: https://img.shields.io/pypi/pyversions/rnanorm.svg
:target: https://pypi.org/project/rnanorm
:alt: Supported Python versions
.. |pypi_downloads| image:: https://pepy.tech/badge/rnanorm
:target: https://pepy.tech/project/rnanorm
:alt: Number of downloads from PyPI
Python implementation of common RNA-seq normalization methods:
- CPM (Counts per million)
- FPKM_ (Fragments per kilobase million)
- TPM_ (Transcripts per million)
- UQ_ (Upper quartile)
- CUF_ (Counts adjusted with UQ factors)
- TMM_ (Trimmed mean of M-values)
- CTF_ (Counts adjusted with TMM factors)
For in-depth description of methods see documentation_.
.. _FPKM: https://www.nature.com/articles/nmeth.1226
.. _TPM: https://link.springer.com/article/10.1007/s12064-012-0162-3
.. _UQ: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-94
.. _CUF: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02568-9/
.. _TMM: https://genomebiology.biomedcentral.com/articles/10.1186/gb-2010-11-3-r25
.. _CTF: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02568-9/
.. _documentation: https://rnanorm.readthedocs.io/
Features
========
- Pure Python implementation (no need for R, etc.)
- Compatible with Scikit-learn_
- Command line interface
- Verbose documentation_
- Validated method implementation
.. _Scikit-learn: https://scikit-learn.org/
.. _documentation: https://rnanorm.readthedocs.io/
Install
=======
We recommend installing RNAnorm with pip::
pip install rnanorm
Quick start
===========
The implemented methods can be executed from Python or from the command line.
Normalize from Python
---------------------
The most common use case is to run normalization from Python::
>>> from rnanorm.datasets import load_toy_data
>>> from rnanorm import FPKM
>>> dataset = load_toy_data()
>>> # Expressions need to have genes in columns and samples in rows
>>> dataset.exp
Gene_1 Gene_2 Gene_3 Gene_4 Gene_5
Sample_1 200 300 500 2000 7000
Sample_2 400 600 1000 4000 14000
Sample_3 200 300 500 2000 17000
Sample_4 200 300 500 2000 2000
>>> fpkm = FPKM(dataset.gtf_path).set_output(transform="pandas")
>>> fpkm.fit_transform(dataset.exp)
Gene_1 Gene_2 Gene_3 Gene_4 Gene_5
Sample_1 100000.0 100000.0 100000.0 200000.0 700000.0
Sample_2 100000.0 100000.0 100000.0 200000.0 700000.0
Sample_3 50000.0 50000.0 50000.0 100000.0 850000.0
Sample_4 200000.0 200000.0 200000.0 400000.0 400000.0
Normalize from command line
---------------------------
Normalization from the command line is also supported. To list available
methods and general help::
rnanorm --help
Get info about a particular method, e.g., CPM::
rnanorm cpm --help
To normalize with CPM::
rnanorm cpm exp.csv --out exp_cpm.csv
File ``exp.csv`` needs to be comma separated file with genes in columns and
samples in rows. Values should be raw counts. The output is saved to
``exp_cpm.csv``. Example of input file::
cat exp.csv
,Gene_1,Gene_2,Gene_3,Gene_4,Gene_5
Sample_1,200,300,500,2000,7000
Sample_2,400,600,1000,4000,14000
Sample_3,200,300,500,2000,17000
Sample_4,200,300,500,2000,2000
One can also provide input through standard input::
cat exp.csv | rnanorm cpm --out exp_cpm.csv
If file specified with ``--out`` already exists the command will fail. If you
are sure that you wish to overwrite, use ``--force`` flag::
cat exp.csv | rnanorm cpm --force --out exp_cpm.csv
If no file is specified with ``--out`` parameter, output is printed to standard
output::
cat exp.csv | rnanorm cpm > exp_cpm.csv
Methods TPM and FPKM require gene lengths. These can be provided either with GTF_
file or with "gene lengths" file. The later is a two columns file. The first
column should include the genes in the header of ``exp.csv`` and the second
column should contain gene lengths computed by union exon model::
# Use GTF file
rnanorm tpm exp.csv --gtf annotations.gtf > exp_out.csv
# Use gene lengths file
rnanorm tpm exp.csv --gene-lengths lenghts.csv > exp_out.csv
# Example of gene lengths file
cat lenghts.csv
gene_id,gene_length
Gene_1,200
Gene_2,300
Gene_3,500
Gene_4,1000
Gene_5,1000
.. _GTF: https://www.ensembl.org/info/website/upload/gff.html
Contribute
==========
To learn about contributing to the code base, read the Contributing_ section.
.. _Contributing: docs/contributing.rst
Citing
======
If you are using RNAnorm in your research, please cite as suggested by "Cite this repository" section in the side panel of this page.
Raw data
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"description": "====================================\nRNA-seq data normalization in Python\n====================================\n\n|build| |black| |docs| |pypi_version| |pypi_pyversions| |pypi_downloads|\n\n.. |build| image:: https://github.com/genialis/RNAnorm/actions/workflows/build.yml/badge.svg?branch=main\n :target: https://github.com/genialis/RNAnorm/actions?query=branch%3Amain\n :alt: Build Status\n\n.. |black| image:: https://img.shields.io/badge/code%20style-black-000000.svg\n :target: https://github.com/psf/black\n :alt: Code Style Black\n\n.. |docs| image:: https://readthedocs.org/projects/rnanorm/badge/?version=latest\n :target: http://rnanorm.readthedocs.io/\n :alt: Documentation Status\n\n.. |pypi_version| image:: https://img.shields.io/pypi/v/rnanorm.svg\n :target: https://pypi.org/project/rnanorm\n :alt: Version on PyPI\n\n.. |pypi_pyversions| image:: https://img.shields.io/pypi/pyversions/rnanorm.svg\n :target: https://pypi.org/project/rnanorm\n :alt: Supported Python versions\n\n.. |pypi_downloads| image:: https://pepy.tech/badge/rnanorm\n :target: https://pepy.tech/project/rnanorm\n :alt: Number of downloads from PyPI\n\n\nPython implementation of common RNA-seq normalization methods:\n\n- CPM (Counts per million)\n- FPKM_ (Fragments per kilobase million)\n- TPM_ (Transcripts per million)\n- UQ_ (Upper quartile)\n- CUF_ (Counts adjusted with UQ factors)\n- TMM_ (Trimmed mean of M-values)\n- CTF_ (Counts adjusted with TMM factors)\n\nFor in-depth description of methods see documentation_.\n\n.. _FPKM: https://www.nature.com/articles/nmeth.1226\n.. _TPM: https://link.springer.com/article/10.1007/s12064-012-0162-3\n.. _UQ: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-94\n.. _CUF: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02568-9/\n.. _TMM: https://genomebiology.biomedcentral.com/articles/10.1186/gb-2010-11-3-r25\n.. _CTF: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02568-9/\n.. _documentation: https://rnanorm.readthedocs.io/\n\n\nFeatures\n========\n\n- Pure Python implementation (no need for R, etc.)\n- Compatible with Scikit-learn_\n- Command line interface\n- Verbose documentation_\n- Validated method implementation\n\n\n.. _Scikit-learn: https://scikit-learn.org/\n.. _documentation: https://rnanorm.readthedocs.io/\n\n\nInstall\n=======\n\nWe recommend installing RNAnorm with pip::\n\n pip install rnanorm\n\n\nQuick start\n===========\n\nThe implemented methods can be executed from Python or from the command line.\n\nNormalize from Python\n---------------------\n\nThe most common use case is to run normalization from Python::\n\n >>> from rnanorm.datasets import load_toy_data\n >>> from rnanorm import FPKM\n >>> dataset = load_toy_data()\n >>> # Expressions need to have genes in columns and samples in rows\n >>> dataset.exp\n Gene_1 Gene_2 Gene_3 Gene_4 Gene_5\n Sample_1 200 300 500 2000 7000\n Sample_2 400 600 1000 4000 14000\n Sample_3 200 300 500 2000 17000\n Sample_4 200 300 500 2000 2000\n >>> fpkm = FPKM(dataset.gtf_path).set_output(transform=\"pandas\")\n >>> fpkm.fit_transform(dataset.exp)\n Gene_1 Gene_2 Gene_3 Gene_4 Gene_5\n Sample_1 100000.0 100000.0 100000.0 200000.0 700000.0\n Sample_2 100000.0 100000.0 100000.0 200000.0 700000.0\n Sample_3 50000.0 50000.0 50000.0 100000.0 850000.0\n Sample_4 200000.0 200000.0 200000.0 400000.0 400000.0\n\n\nNormalize from command line\n---------------------------\n\nNormalization from the command line is also supported. To list available\nmethods and general help::\n\n rnanorm --help\n\nGet info about a particular method, e.g., CPM::\n\n rnanorm cpm --help\n\nTo normalize with CPM::\n\n rnanorm cpm exp.csv --out exp_cpm.csv\n\nFile ``exp.csv`` needs to be comma separated file with genes in columns and\nsamples in rows. Values should be raw counts. The output is saved to\n``exp_cpm.csv``. Example of input file::\n\n cat exp.csv\n ,Gene_1,Gene_2,Gene_3,Gene_4,Gene_5\n Sample_1,200,300,500,2000,7000\n Sample_2,400,600,1000,4000,14000\n Sample_3,200,300,500,2000,17000\n Sample_4,200,300,500,2000,2000\n\nOne can also provide input through standard input::\n\n cat exp.csv | rnanorm cpm --out exp_cpm.csv\n\nIf file specified with ``--out`` already exists the command will fail. If you\nare sure that you wish to overwrite, use ``--force`` flag::\n\n cat exp.csv | rnanorm cpm --force --out exp_cpm.csv\n\nIf no file is specified with ``--out`` parameter, output is printed to standard\noutput::\n\n cat exp.csv | rnanorm cpm > exp_cpm.csv\n\nMethods TPM and FPKM require gene lengths. These can be provided either with GTF_\nfile or with \"gene lengths\" file. The later is a two columns file. The first\ncolumn should include the genes in the header of ``exp.csv`` and the second\ncolumn should contain gene lengths computed by union exon model::\n\n # Use GTF file\n rnanorm tpm exp.csv --gtf annotations.gtf > exp_out.csv\n # Use gene lengths file\n rnanorm tpm exp.csv --gene-lengths lenghts.csv > exp_out.csv\n # Example of gene lengths file\n cat lenghts.csv\n gene_id,gene_length\n Gene_1,200\n Gene_2,300\n Gene_3,500\n Gene_4,1000\n Gene_5,1000\n\n\n.. _GTF: https://www.ensembl.org/info/website/upload/gff.html\n\n\nContribute\n==========\n\nTo learn about contributing to the code base, read the Contributing_ section.\n\n.. _Contributing: docs/contributing.rst\n\nCiting\n======\n\nIf you are using RNAnorm in your research, please cite as suggested by \"Cite this repository\" section in the side panel of this page.\n",
"bugtrack_url": null,
"license": " Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\" \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a \"NOTICE\" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. 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