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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>_
::
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/btac757GSEApy 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 genesets 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.
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:
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>_
| Install gseapy package from bioconda or pip.
.. code:: shell
$ conda install -c bioconda gseapy
$ pip install gseapy
| If pip install failed, use
.. code:: shell
curl https://sh.rustup.rs -sSf | sh -s -- -y
export PATH="$PATH:$HOME/.cargo/bin"
$ pip install git+git://github.com/zqfang/gseapy.git#egg=gseapy
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
$ gseapy replot -i ./Gsea.reports -o test
$ gseapy gsea -d exptable.txt -c test.cls -g gene_sets.gmt -o test
$ gseapy prerank -r gsea_data.rnk -g gene_sets.gmt -o test
$ gseapy ssgsea -d expression.txt -g gene_sets.gmt -o test
$ gseapy gsva -d expression.txt -g gene_sets.gmt -o test
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 ReportIf you would like to report any bugs when use gseapy, don't hesitate to create an issue on github here.
See Frequently Asked Questions <https://gseapy.readthedocs.io/en/latest/faq.html>_
Visit the document site at Examples <https://gseapy.readthedocs.io/en/latest/gseapy_example.html>_
The GSEApy discussion channel: Q&A <https://github.com/zqfang/GSEApy/discussions>_