Single sample pathway analysis tools for omics data
Project description
sspa
Single sample pathway analysis tools for omics data
Full walkthrough notebook available on Google Colab:
Documentation is available on our Read the Docs page
Quickstart
pip install sspa
Load Reactome pathways
reactome_pathways = sspa.process_reactome(organism="Homo sapiens")
Load some example metabolomics data in the form of a pandas DataFrame:
covid_data_processed = sspa.load_example_data(omicstype="metabolomics", processed=True)
Generate pathway scores using kPCA method
kpca_scores = sspa.sspa_kpca(covid_data_processed, reactome_pathways)
Loading pathways
# Pre-loaded pathways
# Reactome v78
reactome_pathways = sspa.process_reactome(organism="Homo sapiens")
# KEGG v98
kegg_human_pathways = sspa.process_kegg(organism="hsa")
Load a custom GMT file (extension .gmt or .csv)
custom_pathways = sspa.process_gmt("wikipathways-20220310-gmt-Homo_sapiens.gmt")
Download latest version of pathways
# download KEGG latest
kegg_mouse_latest = sspa.process_kegg("mmu", download_latest=True, filepath=".")
# download Reactome latest
reactome_mouse_latest = sspa.process_reactome("Mus musculus", download_latest=True, filepath=".")
Identifier harmonization
# download the conversion table
compound_names = processed_data.columns.tolist()
conversion_table = sspa.identifier_conversion(input_type="name", compound_list=compound_names)
# map the identifiers to your dataset
processed_data_mapped = sspa.map_identifiers(conversion_table, output_id_type="ChEBI", matrix=processed_data)
Conventional pathway analysis
ORA
ora = sspa.sspa_ora(processed_data_mapped, covid_data["Group"], reactome_pathways, 0.05, custom_background=None)
# perform ORA
ora_res = ora.over_representation_analysis()
# get t-test results
ora.ttest_res
# obtain list of differential molecules input to ORA
ora.DA_molecules
GSEA
sspa.sspa_fgsea(processed_data_mapped, covid_data['Group'], reactome_pathways)
Single sample pathway analysis methods
# ssclustPA
ssclustpa_proj_res = sspa.sspa_cluster(processed_data_mapped, reactome_pathways)
# kPCA
kpca_scores = sspa.sspa_kpca(processed_data_mapped, reactome_pathways)
# z-score
zscore_res = sspa.sspa_zscore(processed_data_mapped, reactome_pathways)
# SVD (PLAGE)
svd_res = sspa.sspa_svd(processed_data_mapped, reactome_pathways)
# GSVA
gsva_res = sspa.sspa_gsva(processed_data_mapped, reactome_pathways)
License
GNU GPL 3.0
Citing us
If you found this package useful, please consider citing us:
ssPA package
@article{Wieder22a,
author = {Cecilia Wieder and Nathalie Poupin and Clément Frainay and Florence Vinson and Juliette Cooke and Rachel PJ Lai and Jacob G Bundy and Fabien Jourdan and Timothy MD Ebbels},
doi = {10.5281/ZENODO.6959120},
month = {8},
title = {cwieder/py-ssPA: v1.0.4},
url = {https://zenodo.org/record/6959120},
year = {2022},
}
Single-sample pathway analysis in metabolomics
@article{Wieder2022,
author = {Cecilia Wieder and Rachel P J Lai and Timothy Ebbels},
doi = {10.1101/2022.04.11.487976},
journal = {bioRxiv},
month = {4},
pages = {2022.04.11.487976},
publisher = {Cold Spring Harbor Laboratory},
title = {Single sample pathway analysis in metabolomics : performance evaluation and application},
url = {https://www.biorxiv.org/content/10.1101/2022.04.11.487976v1 https://www.biorxiv.org/content/10.1101/2022.04.11.487976v1.abstract},
year = {2022},
}
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
sspa-0.1.4.tar.gz
(8.0 MB
view hashes)
Built Distribution
sspa-0.1.4-py3-none-any.whl
(8.0 MB
view hashes)