sspa_kpca ( reactome_pathways, min_entity = 2 ). Generate pathway scores using kPCA method kpca_scores = sspa. load_example_data ( omicstype = 'metabolomics', processed = True ) Load some example metabolomics data in the form of a pandas DataFrame: covid_data_processed = sspa. process_reactome ( organism = 'Homo sapiens' ) Load Reactome pathways reactome_pathways = sspa.

Alternatively, you can download the notebook from the Colab tutorial as an '.ipynb' file and run it locally using Jupyter Notebook or Jupyter Lab.ĭocumentation is available on our Read the Docs page. For new users we highly recommend following our full walkthrough notebook tutorial available on Google Colab which provides a step-by-step guide to using the package.Ĭlick the link above and save a copy of the Colab notebook to your Google Drive. This README provides a quickstart guide to the package and its functions. Pathway database download (KEGG, Reactome, and MetExplore metabolic networks)Īlthough 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.Metabolite set enrichment analysis (based on GSEA).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. Sspa provides a Python interface for metabolomics pathway analysis.

Pathway downloading