GSEApy
========
GSEApy: Gene Set Enrichment Analysis in Python.
------------------------------------------------
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**Release notes** : https://github.com/zqfang/GSEApy/releases
`Tutorial for scRNA-seq datasets <https://gseapy.readthedocs.io/en/latest/singlecell_example.html#>`_
`Tutorial for general usage <https://gseapy.readthedocs.io/en/latest/gseapy_example.html>`_
Citation
------------------------------------
::
Zhuoqing Fang, Xinyuan Liu, Gary Peltz, GSEApy: a comprehensive package for performing gene set enrichment analysis in Python,
Bioinformatics, 2022;, btac757, https://doi.org/10.1093/bioinformatics/btac757
GSEApy is a Python/Rust implementation for **GSEA** and wrapper for **Enrichr**.
--------------------------------------------------------------------------------------------
GSEApy can be used for **RNA-seq, ChIP-seq, Microarray** data. It can be used for convenient GO enrichment and to produce **publication quality figures** in python.
GSEApy has 7 sub-commands available: ``gsea``, ``prerank``, ``ssgsea``, ``gsva``, ``replot`` ``enrichr``, ``biomart``.
:gsea: The ``gsea`` module produces `GSEA <http://www.broadinstitute.org/cancer/software/gsea/wiki/index.php/Main_Page>`_ results. The input requries a txt file(FPKM, Expected Counts, TPM, et.al), a cls file, and gene_sets file in gmt format.
:prerank: The ``prerank`` module produces **Prerank tool** results. The input expects a pre-ranked gene list dataset with correlation values, provided in .rnk format, and gene_sets file in gmt format. ``prerank`` module is an API to `GSEA` pre-rank tools.
:ssgsea: The ``ssgsea`` module performs **single sample GSEA(ssGSEA)** analysis. The input expects a pd.Series (indexed by gene name), or a pd.DataFrame (include ``GCT`` file) with expression values and a ``GMT`` file. For multiple sample input, ssGSEA reconigzes gct format, too. ssGSEA enrichment score for the gene set is described by `D. Barbie et al 2009 <http://www.nature.com/nature/journal/v462/n7269/abs/nature08460.html>`_.
:gsva: The ``gsva`` module performs `GSVA <https://github.com/rcastelo/GSVA>`_ method by `Hänzelmann et al <https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-7>`_. The input is same to ssgsea.
:replot: The ``replot`` module reproduce GSEA desktop version results. The only input for GSEApy is the location to ``GSEA`` Desktop output results.
:enrichr: The ``enrichr`` module enable you perform gene set enrichment analysis using ``Enrichr`` API. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr . It runs very fast.
:biomart: The ``biomart`` module helps you convert gene ids using BioMart API.
Please use 'gseapy COMMAND -h' to see the detail description for each option of each module.
The full ``GSEA`` is far too extensive to describe here; see
`GSEA <http://www.broadinstitute.org/cancer/software/gsea/wiki/index.php/Main_Page>`_ documentation for more information. All files' formats for GSEApy are identical to ``GSEA`` desktop version.
Why GSEApy
-----------------------------------------------------
I would like to use Pandas to explore my data, but I did not find a convenient tool to
do gene set enrichment analysis in python. So, here are my reasons:
* **Ability to run inside python interactive console without having to switch to R!!!**
* User friendly for both wet and dry lab users.
* Produce or reproduce publishable figures.
* Perform batch jobs easy.
* Easy to use in bash shell or your data analysis workflow, e.g. snakemake.
GSEApy vs GSEA(Broad) output
-----------------------------------------------
Using the same data for ``GSEAPreranked``, and ``GSEApy`` reproduce similar results.
.. image:: docs/Preank.py.vs.broad.jpg
:width: 400
See more output here: `Example <http://gseapy.readthedocs.io/en/master/gseapy_example.html>`_
Installation
------------
| Install gseapy package from bioconda or pip.
.. code:: shell
# if you have conda (MacOS_x86-64 and Linux only)
$ conda install -c bioconda gseapy
# Windows and MacOS_ARM64(M1/2-Chip)
$ pip install gseapy
| If pip install failed, use
.. code:: shell
# you need to install rust first to compile the code
curl https://sh.rustup.rs -sSf | sh -s -- -y
# export rust compiler
export PATH="$PATH:$HOME/.cargo/bin"
# install
$ pip install git+git://github.com/zqfang/gseapy.git#egg=gseapy
Dependency
--------------
* Python 3.7+
Mandatory
~~~~~~~~~
* build
* Rust: For gseapy > 0.11.0, Rust compiler is needed
* setuptools-rust
* run
* Numpy >= 1.13.0
* Scipy
* Pandas
* Matplotlib
* Requests
Run GSEApy
-----------------
For command line usage:
~~~~~~~~~~~~~~~~~~~~~~~
.. code:: bash
# An example to reproduce figures using replot module.
$ gseapy replot -i ./Gsea.reports -o test
# An example to run GSEA using gseapy gsea module
$ gseapy gsea -d exptable.txt -c test.cls -g gene_sets.gmt -o test
# An example to run Prerank using gseapy prerank module
$ gseapy prerank -r gsea_data.rnk -g gene_sets.gmt -o test
# An example to run ssGSEA using gseapy ssgsea module
$ gseapy ssgsea -d expression.txt -g gene_sets.gmt -o test
# An example to run GSVA using gseapy ssgsea module
$ gseapy gsva -d expression.txt -g gene_sets.gmt -o test
# An example to use enrichr api
# see details for -g input -> ``get_library_name``
$ gseapy enrichr -i gene_list.txt -g KEGG_2016 -o test
Run gseapy inside python console:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
1. Prepare expression.txt, gene_sets.gmt and test.cls required by GSEA, you could do this
.. code:: python
import gseapy
# run GSEA.
gseapy.gsea(data='expression.txt', gene_sets='gene_sets.gmt', cls='test.cls', outdir='test')
# run prerank
gseapy.prerank(rnk='gsea_data.rnk', gene_sets='gene_sets.gmt', outdir='test')
# run ssGSEA
gseapy.ssgsea(data="expression.txt", gene_sets= "gene_sets.gmt", outdir='test')
# run GSVA
gseapy.gsva(data="expression.txt", gene_sets= "gene_sets.gmt", outdir='test')
# An example to reproduce figures using replot module.
gseapy.replot(indir='./Gsea.reports', outdir='test')
2. If you prefer to use Dataframe, dict, list in interactive python console, you could do this.
see detail here: `Example <http://gseapy.readthedocs.io/en/master/gseapy_example.html>`_
.. code:: python
# assign dataframe, and use enrichr library data set 'KEGG_2016'
expression_dataframe = pd.DataFrame()
sample_name = ['A','A','A','B','B','B'] # always only two group,any names you like
# assign gene_sets parameter with enrichr library name or gmt file on your local computer.
gseapy.gsea(data=expression_dataframe, gene_sets='KEGG_2016', cls= sample_names, outdir='test')
# prerank tool
gene_ranked_dataframe = pd.DataFrame()
gseapy.prerank(rnk=gene_ranked_dataframe, gene_sets='KEGG_2016', outdir='test')
# ssGSEA
gseapy.ssgsea(data=expression_dataframe, gene_sets='KEGG_2016', outdir='test')
# gsva
gseapy.gsva(data=expression_dataframe, gene_sets='KEGG_2016', outdir='test')
3. For ``enrichr`` , you could assign a list, pd.Series, pd.DataFrame object, or a txt file (should be one gene name per row.)
.. code:: python
# assign a list object to enrichr
gl = ['SCARA3', 'LOC100044683', 'CMBL', 'CLIC6', 'IL13RA1', 'TACSTD2', 'DKKL1', 'CSF1',
'SYNPO2L', 'TINAGL1', 'PTX3', 'BGN', 'HERC1', 'EFNA1', 'CIB2', 'PMP22', 'TMEM173']
gseapy.enrichr(gene_list=gl, gene_sets='KEGG_2016', outdir='test')
# or a txt file path.
gseapy.enrichr(gene_list='gene_list.txt', gene_sets='KEGG_2016',
outdir='test', cutoff=0.05, format='png' )
GSEApy supported gene set libaries :
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
To see the full list of gseapy supported gene set libraries, please click here: `Library <http://amp.pharm.mssm.edu/Enrichr/#stats>`_
Or use ``get_library_name`` function inside python console.
.. code:: python
#see full list of latest enrichr library names, which will pass to -g parameter:
names = gseapy.get_library_name()
# show top 20 entries.
print(names[:20])
['Genome_Browser_PWMs',
'TRANSFAC_and_JASPAR_PWMs',
'ChEA_2013',
'Drug_Perturbations_from_GEO_2014',
'ENCODE_TF_ChIP-seq_2014',
'BioCarta_2013',
'Reactome_2013',
'WikiPathways_2013',
'Disease_Signatures_from_GEO_up_2014',
'KEGG_2016',
'TF-LOF_Expression_from_GEO',
'TargetScan_microRNA',
'PPI_Hub_Proteins',
'GO_Molecular_Function_2015',
'GeneSigDB',
'Chromosome_Location',
'Human_Gene_Atlas',
'Mouse_Gene_Atlas',
'GO_Cellular_Component_2015',
'GO_Biological_Process_2015',
'Human_Phenotype_Ontology',]
Dev
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. code:: shell
# test rust extension only
cargo test --features=extension-module
# test whole package
python setup.py test
Bug Report
~~~~~~~~~~~~~~~~~~~~~~~~~~~
If you would like to report any bugs when use gseapy, don't hesitate to create an issue on github here.
To get help of GSEApy
------------------------------------
1. See `Frequently Asked Questions <https://gseapy.readthedocs.io/en/latest/faq.html>`_
2. Visit the document site at `Examples <https://gseapy.readthedocs.io/en/latest/gseapy_example.html>`_
3. The GSEApy discussion channel: `Q&A <https://github.com/zqfang/GSEApy/discussions>`_
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"description": "\nGSEApy\n========\n\nGSEApy: Gene Set Enrichment Analysis in Python.\n------------------------------------------------\n\n.. image:: https://badge.fury.io/py/gseapy.svg\n :target: https://badge.fury.io/py/gseapy\n\n.. image:: https://img.shields.io/conda/vn/bioconda/GSEApy.svg?style=plastic\n :target: http://bioconda.github.io\n\n.. image:: https://anaconda.org/bioconda/gseapy/badges/downloads.svg \n :target: https://anaconda.org/bioconda/gseapy\n\n.. image:: https://github.com/zqfang/GSEApy/workflows/GSEApy/badge.svg?branch=master\n :target: https://github.com/zqfang/GSEApy/actions\n :alt: Action Status\n\n.. image:: http://readthedocs.org/projects/gseapy/badge/?version=master\n :target: http://gseapy.readthedocs.io/en/master/?badge=master\n :alt: Documentation Status\n\n.. image:: https://img.shields.io/badge/license-MIT-blue.svg\n :target: https://img.shields.io/badge/license-MIT-blue.svg\n\n.. image:: https://img.shields.io/pypi/pyversions/gseapy.svg\n :alt: PyPI - Python Version\n\n\n**Release notes** : https://github.com/zqfang/GSEApy/releases\n\n`Tutorial for scRNA-seq datasets <https://gseapy.readthedocs.io/en/latest/singlecell_example.html#>`_\n\n`Tutorial for general usage <https://gseapy.readthedocs.io/en/latest/gseapy_example.html>`_\n\n\nCitation\n------------------------------------\n::\n\n Zhuoqing Fang, Xinyuan Liu, Gary Peltz, GSEApy: a comprehensive package for performing gene set enrichment analysis in Python, \n Bioinformatics, 2022;, btac757, https://doi.org/10.1093/bioinformatics/btac757\n\n\n\nGSEApy is a Python/Rust implementation for **GSEA** and wrapper for **Enrichr**.\n--------------------------------------------------------------------------------------------\n\nGSEApy can be used for **RNA-seq, ChIP-seq, Microarray** data. It can be used for convenient GO enrichment and to produce **publication quality figures** in python.\n\n\nGSEApy has 7 sub-commands available: ``gsea``, ``prerank``, ``ssgsea``, ``gsva``, ``replot`` ``enrichr``, ``biomart``.\n\n\n:gsea: The ``gsea`` module produces `GSEA <http://www.broadinstitute.org/cancer/software/gsea/wiki/index.php/Main_Page>`_ results. The input requries a txt file(FPKM, Expected Counts, TPM, et.al), a cls file, and gene_sets file in gmt format.\n:prerank: The ``prerank`` module produces **Prerank tool** results. The input expects a pre-ranked gene list dataset with correlation values, provided in .rnk format, and gene_sets file in gmt format. ``prerank`` module is an API to `GSEA` pre-rank tools.\n:ssgsea: The ``ssgsea`` module performs **single sample GSEA(ssGSEA)** analysis. The input expects a pd.Series (indexed by gene name), or a pd.DataFrame (include ``GCT`` file) with expression values and a ``GMT`` file. For multiple sample input, ssGSEA reconigzes gct format, too. ssGSEA enrichment score for the gene set is described by `D. Barbie et al 2009 <http://www.nature.com/nature/journal/v462/n7269/abs/nature08460.html>`_.\n:gsva: The ``gsva`` module performs `GSVA <https://github.com/rcastelo/GSVA>`_ method by `H\u00e4nzelmann et al <https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-7>`_. The input is same to ssgsea.\n:replot: The ``replot`` module reproduce GSEA desktop version results. The only input for GSEApy is the location to ``GSEA`` Desktop output results.\n:enrichr: The ``enrichr`` module enable you perform gene set enrichment analysis using ``Enrichr`` API. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr . It runs very fast.\n:biomart: The ``biomart`` module helps you convert gene ids using BioMart API.\n\n\nPlease use 'gseapy COMMAND -h' to see the detail description for each option of each module.\n\n\nThe full ``GSEA`` is far too extensive to describe here; see\n`GSEA <http://www.broadinstitute.org/cancer/software/gsea/wiki/index.php/Main_Page>`_ documentation for more information. All files' formats for GSEApy are identical to ``GSEA`` desktop version.\n\n\n\nWhy GSEApy\n-----------------------------------------------------\n\nI would like to use Pandas to explore my data, but I did not find a convenient tool to\ndo gene set enrichment analysis in python. So, here are my reasons:\n\n* **Ability to run inside python interactive console without having to switch to R!!!**\n* User friendly for both wet and dry lab users.\n* Produce or reproduce publishable figures.\n* Perform batch jobs easy.\n* Easy to use in bash shell or your data analysis workflow, e.g. snakemake.\n\n\nGSEApy vs GSEA(Broad) output\n-----------------------------------------------\nUsing the same data for ``GSEAPreranked``, and ``GSEApy`` reproduce similar results.\n\n\n.. image:: docs/Preank.py.vs.broad.jpg\n :width: 400\n\n\nSee more output here: `Example <http://gseapy.readthedocs.io/en/master/gseapy_example.html>`_\n\n\nInstallation\n------------\n\n| Install gseapy package from bioconda or pip.\n\n\n.. code:: shell\n\n # if you have conda (MacOS_x86-64 and Linux only)\n $ conda install -c bioconda gseapy\n # Windows and MacOS_ARM64(M1/2-Chip)\n $ pip install gseapy\n\n\n| If pip install failed, use\n\n.. code:: shell\n\n # you need to install rust first to compile the code\n curl https://sh.rustup.rs -sSf | sh -s -- -y\n # export rust compiler \n export PATH=\"$PATH:$HOME/.cargo/bin\"\n # install\n $ pip install git+git://github.com/zqfang/gseapy.git#egg=gseapy\n\n\nDependency\n--------------\n* Python 3.7+\n\nMandatory\n~~~~~~~~~\n\n* build\n * Rust: For gseapy > 0.11.0, Rust compiler is needed\n * setuptools-rust\n* run\n * Numpy >= 1.13.0\n * Scipy\n * Pandas\n * Matplotlib\n * Requests\n\nRun GSEApy\n-----------------\n\n\nFor command line usage:\n~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: bash\n\n\n # An example to reproduce figures using replot module.\n $ gseapy replot -i ./Gsea.reports -o test\n\n\n # An example to run GSEA using gseapy gsea module\n $ gseapy gsea -d exptable.txt -c test.cls -g gene_sets.gmt -o test\n\n # An example to run Prerank using gseapy prerank module\n $ gseapy prerank -r gsea_data.rnk -g gene_sets.gmt -o test\n\n # An example to run ssGSEA using gseapy ssgsea module\n $ gseapy ssgsea -d expression.txt -g gene_sets.gmt -o test\n\n # An example to run GSVA using gseapy ssgsea module\n $ gseapy gsva -d expression.txt -g gene_sets.gmt -o test\n\n # An example to use enrichr api\n # see details for -g input -> ``get_library_name`` \n $ gseapy enrichr -i gene_list.txt -g KEGG_2016 -o test\n\n\n\nRun gseapy inside python console:\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n1. Prepare expression.txt, gene_sets.gmt and test.cls required by GSEA, you could do this\n\n.. code:: python\n\n import gseapy\n\n # run GSEA.\n gseapy.gsea(data='expression.txt', gene_sets='gene_sets.gmt', cls='test.cls', outdir='test')\n\n # run prerank\n gseapy.prerank(rnk='gsea_data.rnk', gene_sets='gene_sets.gmt', outdir='test')\n\n # run ssGSEA\n gseapy.ssgsea(data=\"expression.txt\", gene_sets= \"gene_sets.gmt\", outdir='test')\n\n # run GSVA\n gseapy.gsva(data=\"expression.txt\", gene_sets= \"gene_sets.gmt\", outdir='test')\n\n # An example to reproduce figures using replot module.\n gseapy.replot(indir='./Gsea.reports', outdir='test')\n\n\n2. If you prefer to use Dataframe, dict, list in interactive python console, you could do this.\n\nsee detail here: `Example <http://gseapy.readthedocs.io/en/master/gseapy_example.html>`_\n\n.. code:: python\n\n\n # assign dataframe, and use enrichr library data set 'KEGG_2016'\n expression_dataframe = pd.DataFrame()\n\n sample_name = ['A','A','A','B','B','B'] # always only two group,any names you like\n\n # assign gene_sets parameter with enrichr library name or gmt file on your local computer.\n gseapy.gsea(data=expression_dataframe, gene_sets='KEGG_2016', cls= sample_names, outdir='test')\n\n # prerank tool\n gene_ranked_dataframe = pd.DataFrame()\n gseapy.prerank(rnk=gene_ranked_dataframe, gene_sets='KEGG_2016', outdir='test')\n\n # ssGSEA\n gseapy.ssgsea(data=expression_dataframe, gene_sets='KEGG_2016', outdir='test')\n\n # gsva\n gseapy.gsva(data=expression_dataframe, gene_sets='KEGG_2016', outdir='test')\n\n\n3. For ``enrichr`` , you could assign a list, pd.Series, pd.DataFrame object, or a txt file (should be one gene name per row.)\n\n.. code:: python\n\n # assign a list object to enrichr\n gl = ['SCARA3', 'LOC100044683', 'CMBL', 'CLIC6', 'IL13RA1', 'TACSTD2', 'DKKL1', 'CSF1',\n 'SYNPO2L', 'TINAGL1', 'PTX3', 'BGN', 'HERC1', 'EFNA1', 'CIB2', 'PMP22', 'TMEM173']\n\n gseapy.enrichr(gene_list=gl, gene_sets='KEGG_2016', outdir='test')\n\n # or a txt file path.\n gseapy.enrichr(gene_list='gene_list.txt', gene_sets='KEGG_2016',\n outdir='test', cutoff=0.05, format='png' )\n\n\nGSEApy supported gene set libaries :\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nTo see the full list of gseapy supported gene set libraries, please click here: `Library <http://amp.pharm.mssm.edu/Enrichr/#stats>`_\n\nOr use ``get_library_name`` function inside python console.\n\n.. code:: python\n\n #see full list of latest enrichr library names, which will pass to -g parameter:\n names = gseapy.get_library_name()\n\n # show top 20 entries.\n print(names[:20])\n\n\n ['Genome_Browser_PWMs',\n 'TRANSFAC_and_JASPAR_PWMs',\n 'ChEA_2013',\n 'Drug_Perturbations_from_GEO_2014',\n 'ENCODE_TF_ChIP-seq_2014',\n 'BioCarta_2013',\n 'Reactome_2013',\n 'WikiPathways_2013',\n 'Disease_Signatures_from_GEO_up_2014',\n 'KEGG_2016',\n 'TF-LOF_Expression_from_GEO',\n 'TargetScan_microRNA',\n 'PPI_Hub_Proteins',\n 'GO_Molecular_Function_2015',\n 'GeneSigDB',\n 'Chromosome_Location',\n 'Human_Gene_Atlas',\n 'Mouse_Gene_Atlas',\n 'GO_Cellular_Component_2015',\n 'GO_Biological_Process_2015',\n 'Human_Phenotype_Ontology',]\n\n\n\nDev \n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: shell\n\n\n # test rust extension only \n cargo test --features=extension-module\n # test whole package\n python setup.py test\n\n\n\nBug Report\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nIf you would like to report any bugs when use gseapy, don't hesitate to create an issue on github here.\n\n\nTo get help of GSEApy\n------------------------------------\n\n1. See `Frequently Asked Questions <https://gseapy.readthedocs.io/en/latest/faq.html>`_\n\n2. Visit the document site at `Examples <https://gseapy.readthedocs.io/en/latest/gseapy_example.html>`_\n\n3. The GSEApy discussion channel: `Q&A <https://github.com/zqfang/GSEApy/discussions>`_ \n\n",
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