sspa 0.1.4

Single sample pathway analysis tools for omics data

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},
}