Welcome to the Phenonaut documentation!

Phenonaut is a framework for applying workflows to multi-omics data. Originally targeting high-content imaging and the exploration of phenotypic space, with different visualisations and metrics, Phenonaut allows now operates in a data agnostic manner, allowing users to describe their data (potentially multi-view/multi-omics) and apply a series of generic or specialised data-centric transforms and measures.

Phenonaut operates in 2 modes:

• As a Python package, importable and callable within custom scripts.

• Operating on a workflow defined in either YAML, or JSON, allowing integration of complex chains of Phenonaut instructions to be integrated into existing workflows and pipelines. When built as a package and installed, workflows can be executed with:

`python -m phenonaut workflow.yml`.

User guide

Alongside the API documentation a crash-course userguide is available here: User guide.

A breakdown and guide to workflow mode and commands can be found here: Workflow mode.

Contents:

• API documentation
  • Subpackages
    • phenonaut.data package
      • Submodules
      • phenonaut.data.dataset module
      • phenonaut.data.platemap_querier module
      • phenonaut.data.recipes module
      • Module contents
    • phenonaut.metrics package
      • Submodules
      • phenonaut.metrics.distances module
      • phenonaut.metrics.measures module
      • phenonaut.metrics.non_ds_phenotypic_metrics module
      • phenonaut.metrics.performance module
      • phenonaut.metrics.utils module
      • Module contents
    • phenonaut.output package
      • Submodules
      • phenonaut.output.boxplot module
      • phenonaut.output.heatmap module
      • phenonaut.output.pair_plot module
      • phenonaut.output.pptx module
      • phenonaut.output.scatter module
      • phenonaut.output.spreadsheet module
      • phenonaut.output.visualisation_base module
      • Module contents
    • phenonaut.packaged_datasets package
      • Submodules
      • phenonaut.packaged_datasets.base module
      • phenonaut.packaged_datasets.breast_cancer module
      • phenonaut.packaged_datasets.cmap module
      • phenonaut.packaged_datasets.iris module
      • phenonaut.packaged_datasets.lincs module
      • phenonaut.packaged_datasets.metadata_moa module
      • phenonaut.packaged_datasets.tcga module
      • Module contents
    • phenonaut.predict package
      • Subpackages
      • Submodules
      • phenonaut.predict.optuna_functions module
      • phenonaut.predict.predict_utils module
      • phenonaut.predict.predictor_dataclasses module
      • phenonaut.predict.profile module
      • Module contents
    • phenonaut.transforms package
      • Submodules
      • phenonaut.transforms.dimensionality_reduction module
      • phenonaut.transforms.generic_transformations module
      • phenonaut.transforms.imputers module
      • phenonaut.transforms.preparative module
      • phenonaut.transforms.supervised_transformer module
      • phenonaut.transforms.transformer module
      • Module contents
  • Submodules
  • phenonaut.errors module
    • NotEnoughRowsError
  • phenonaut.phenonaut module
    • Phenonaut
      • Phenonaut.add_well_id()
      • Phenonaut.aggregate_dataset()
      • Phenonaut.clone_dataset()
      • Phenonaut.combine_datasets()
      • Phenonaut.data
      • Phenonaut.df
      • Phenonaut.ds
      • Phenonaut.get_dataset_combinations()
      • Phenonaut.get_dataset_index_from_name()
      • Phenonaut.get_dataset_names()
      • Phenonaut.get_df_features_perturbation_column()
      • Phenonaut.get_hash_dictionary()
      • Phenonaut.groupby_datasets()
      • Phenonaut.keys()
      • Phenonaut.load()
      • Phenonaut.load_dataset()
      • Phenonaut.merge_datasets()
      • Phenonaut.new_dataset_from_query()
      • Phenonaut.revert()
      • Phenonaut.save()
      • Phenonaut.subtract_median_perturbation()
    • random()
  • phenonaut.utils module
    • check_path()
    • load_dict()
  • phenonaut.workflow module
    • Workflow
      • Workflow.VIF_filter_features()
      • Workflow.add_well_id()
      • Workflow.cityblock_distance()
      • Workflow.copy_column()
      • Workflow.euclidean_distance()
      • Workflow.filter_columns()
      • Workflow.filter_correlated_and_VIF_features()
      • Workflow.filter_correlated_features()
      • Workflow.filter_rows()
      • Workflow.if_blank_also_blank()
      • Workflow.load()
      • Workflow.mahalanobis_distance()
      • Workflow.manhattan_distance()
      • Workflow.pca()
      • Workflow.rename_column()
      • Workflow.rename_columns()
      • Workflow.run_workflow()
      • Workflow.scalar_projection()
      • Workflow.scatter()
      • Workflow.set_perturbation_column()
      • Workflow.tsne()
      • Workflow.umap()
      • Workflow.write_csv()
      • Workflow.write_multiple_csvs()
    • predict()
  • Module contents
• User guide
  • Phenonaut
    • Phenonaut class
    • Datasets class
    • Datasets containing one feature per-row
    • Packaged datasets
      • Removing highly correlated features
      • Applying UMAP
      • Plotting the results
• Publication examples
  • TCGA Example
  • CMAP Example
• Workflow mode
  • I/O
    • load
    • write_csv
    • write_multiple_csvs
  • Transforms
    • add_well_id
    • copy_column
    • rename_column and rename_columns
  • Filtering
    • filter_columns
    • filter_rows
    • filter_correlated_features
    • VIF_filter_features
    • filter_correlated_and_VIF_features
    • if_blank_also_blank
  • Dimensionality reduction
    • pca
    • tnse
    • umap
  • Measurements
    • scalar projection
    • euclidean_distance
    • manhattan_distance
    • cityblock_distance
    • mahalanobis_distance
  • Plotting
    • set_perturbation_column
    • scatter
  • Prediction
    • predict

Indices and tables

• Index

• Module Index

• Search Page