GSEAPY

GSEAPY: Gene Set Enrichment Analysis in Python.

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For examples of using GSEApy please click here: Example

Release notes : https://github.com/zqfang/GSEApy/releases

FAQ: wiki

GSEAPY is a python wrapper for GSEA and 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 six sub-commands available: gsea, prerank, ssgsea, 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.
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.

If you use gseapy in your research, you should cite the original ``GSEA`` and ``Enrichr`` paper.

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 `Prerank` module output

Using the same data from GSEA, GSEAPY reproduce the example above.

Using Prerank or replot module will reproduce the same figure for GSEA Java desktop outputs

Generated by GSEAPY

GSEAPY figures are supported by all matplotlib figure formats.

You can modify GSEA plots easily in .pdf files. Please Enjoy.

Installation

Install gseapy package from bioconda or pypi.

# if you have conda
$ conda install -c conda-forge -c bioconda gseapy

# or use pip to install the latest release
$ pip install gseapy

You may instead want to use the development version from Github, by running

$ pip install git+git://github.com/zqfang/gseapy.git#egg=gseapy

Dependency

Python 3.5+

Mandatory

Numpy >= 1.13.0
Scipy
Pandas
Matplotlib
Requests (for Enrichr API)
Bioservices (for BioMart API)

# 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 use enrichr api
# see details of -g below, -d  is optional
$ gseapy enrichr -i gene_list.txt -g KEGG_2016 -d pathway_enrichment -o test

Run gseapy inside python console:

Prepare expression.txt, gene_sets.gmt and test.cls required by GSEA, you could do this

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')


# An example to reproduce figures using replot module.
gseapy.replot(indir='./Gsea.reports', outdir='test')

If you prefer to use Dataframe, dict, list in interactive python console, you could do this.

see detail here: Example

# 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')

# using prerank tool
gene_ranked_dataframe = pd.DataFrame()
gseapy.prerank(rnk=gene_ranked_dataframe, gene_sets='KEGG_2016', outdir='test')

# using ssGSEA
gseapy.ssgsea(data=ssGSEA_dataframe, gene_sets='KEGG_2016', outdir='test')

For enrichr , you could assign a list, pd.Series, pd.DataFrame object, or a txt file (should be one gene name per row.)

# 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, description='pathway', gene_sets='KEGG_2016', outdir='test')

# or a txt file path.
gseapy.enrichr(gene_list='gene_list.txt', description='pathway', 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.

 #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',]