gseapy 1.3.0

Gene Set Enrichment Analysis in Python

GSEApy: Gene Set Enrichment Analysis in Python.

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

Tutorial for scRNA-seq datasets

Tutorial for general usage

• A faithful Rust port of fgsea’s multilevel algorithm that resolves p-values far below 1/permutation_num.

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. It also supports the fgsea multilevel p-value method (a faithful Rust port of fgsea) via method='multilevel', which resolves p-values far below 1/permutation_num.

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.

See more output here: Example

Installation

Install gseapy package from bioconda or pip.

# if you have conda/mamba
$ conda install -c bioconda gseapy
# or pip
$ pip install gseapy
# or uv
$ uv add gseapy

If pip install failed, install Rust first and build from source:

# install rust toolchain
curl https://sh.rustup.rs -sSf | sh -s -- -y
export PATH="$PATH:$HOME/.cargo/bin"
# then install via pip or uv
$ pip install gseapy
# or
$ uv add gseapy

Dependency

• Python 3.8+

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:

# 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 Prerank with the fgsea multilevel p-value method
$ gseapy prerank -r gsea_data.rnk -g gene_sets.gmt --method multilevel --sample-size 101 --eps 1e-50 -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

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 prerank with the fgsea multilevel p-value method
gseapy.prerank(rnk='gsea_data.rnk', gene_sets='gene_sets.gmt', method='multilevel', 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

# 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.)

# 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.

 #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

# clone and set up dev environment (requires Rust toolchain)
$ git clone https://github.com/zqfang/GSEApy.git
$ cd GSEApy
$ uv sync --extra dev

# run tests
$ uv run pytest

# lint and format
$ uv run ruff format gseapy
$ uv run ruff check gseapy

# test rust extension only
$ cargo test --features=extension-module

# build wheel + sdist locally
$ uv build

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