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Single sample pathway analysis toolkit

sspa provides a Python interface for metabolomics pathway analysis. In addition to conventional methods over-representation analysis (ORA) and gene/metabolite set enrichment analysis (GSEA), it also provides a wide range of single-sample pathway analysis (ssPA) methods.

Features

  • Over-representation analysis
  • Metabolite set enrichment analysis (based on GSEA)
  • Single-sample pathway analysis
  • Compound identifier conversion
  • Pathway database download (KEGG, Reactome, and MetExplore metabolic networks)
graph LR 
  A[Download pathways ] --> C;
  B[Input data] --> G[Identifier conversion];
  G -->C[Pathway analysis];
  C --> D[ORA];
  C --> E[GSEA];
  C --> F[ssPA];

Note

Although this package is designed to provide a user-friendly interface for metabolomics pathway analysis, the methods are also applicable to other datatypes such as normalised RNA-seq data. Gene/protein pathway collections can be input as .GMT files (see tutorials).

Installation

pip install sspa

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 M D Ebbels},
   doi = {10.1186/s12859-022-05005-1},
   issn = {1471-2105},
   issue = {1},
   journal = {BMC Bioinformatics},
   pages = {481},
   title = {Single sample pathway analysis in metabolomics: performance evaluation and application},
   volume = {23},
   url = {https://doi.org/10.1186/s12859-022-05005-1},
   year = {2022},
}