Tutorials

A typical workflow follows two main steps. The first is to attribute a score to each reaction of the model, in accordance with the omics data imputed. The second is to use the scores and apply an integration method to select a subset of reactions to build the final model.

Integration scoring methods implemented in Troppo are:

• continuous: ContinuousScoreIntegrationStrategy

• threshold: ThresholdSelectionIntegrationStrategy

• default_core: DefaultCoreIntegrationStrategy

• adjusted_score: AdjustedScoreIntegrationStrategy

• custom: CustomSelectionIntegrationStrategy

Omics integration methods implemented in Troppo are:

• gimme: GIMME

• tinit: tINIT

• fastcore: GIMME

• imat: IMAT

• swiftcore: SWIFTCORE

• corda: CORDA

Note that the appropriate integration scoring method can differ between integration algorithms. For instance, for GIMME a continuous scoring method can be used, while for fastcore a threshold scoring method is more adequate.

Moreover, gene-level thresholding can be applied to the omics data before integration. This can be done using the GeneLevelThresholding class.

• GIMME tutorial
  • Imports and Setup
  • Initial Setup
  • Create the OmicsContainer object
  • Create a model wrapper
  • Map gene IDs in the data to model IDs
  • Integrate Scores
  • Run GIMME
• FastCORE tutorial
  • Imports and Setup
  • Initial Setup
  • Create the OmicsContainer object
  • Create a model wrapper
  • Map gene IDs in the data to model IDs
  • Integrate Scores
  • Run FASTcore
• tINIT tutorial
  • Imports and Setup
  • Initial Setup
  • Create the OmicsContainer object
  • Create a model wrapper
  • Map gene IDs in the data to model IDs
  • Integrate Scores
  • Run tINIT
• iMAT tutorial
  • Imports and Setup
  • Initial Setup
  • Create the OmicsContainer object
  • Create a model wrapper
  • Map gene IDs in the data to model IDs
  • Integrate Scores
  • Run iMAT
• Batch integration of Omics Data
  • Initial setup
  • Define the function to be parallelized
  • Run the batch integration
• Gene-level thresholding tutorial
  • Imports and Setup
  • Read Omics Data
  • Apply Gene-level Thresholding
• Task performance evaluation
  • Setup the model and the tasks
  • Evaluate a set of tasks
  • Save the results