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https://github.com/zqfang/GSEApy

Gene Set Enrichment Analysis in Python
https://github.com/zqfang/GSEApy

enrichment-analysis gsea python3 rust

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Gene Set Enrichment Analysis in Python

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README 

        

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 `_



`Tutorial for general usage `_



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  `_ 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 `_.

:gsva: The ``gsva`` module performs `GSVA `_ method by `Hänzelmann et al `_. 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  `_ 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 `_



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 `_



.. 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 `_



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 `_



2. Visit the document site at `Examples `_



3. The GSEApy discussion channel: `Q&A `_