Advanced Capabilities and Features

Parameter tuning

Parameter tuning is the process of determining which parameter combinations should be explored for each algorithm for a given dataset. Parameter tuning focuses on defining and refining the parameter search space.

Each dataset has unique characteristics so there are no preset parameters combinations to use. Instead, we recommend tuning parameters individually for each new dataset. SPRAS provides a flexible framework for getting parameter grids for any algorithms for a given dataset.

Grid Search

A grid search systematically checks different combinations of parameter values to see how each affects network reconstruction results.

In SPRAS, users can define parameter grids for each algorithm directly in the configuration file. When executed, SPRAS automatically runs each algorithm across all parameter combinations and collects the resulting subnetworks.

# TODO maybe add in information about how parameter tuning seems to be done now # add in more details about two stage parameter tuning

SPRAS will also support parameter refinement using graph topological heuristics. These topological metrics help identify parameter regions that produce biologically plausible outputs networks. Based on these heuristics, SPRAS will generate new configuration files with refined parameter grids for each algorithm per dataset.

Users can further refine these grids by rerunning the updated configuration and adjusting the parameter ranges around the newly identified regions to find and fine-tune the most promising algorithm specific outputs for a given dataset.

Note

Grid search features are still under development and will be added in future SPRAS releases.

HTCondor integration

Running SPRAS locally can become slow and resource intensive, especially when running many algorithms, parameter combinations, or datasets simultaneously.

To address this, SPRAS supports an integration with HTCondor (a high throughput computing system), allowing Snakemake jobs to be distributed in parallel and executed across available compute.

See Running with HTCondor for more information on SPRAS’s integrations with HTConder.

Ability to run with different container frameworks

CHTC uses Apptainer to run containerized software in secure, high-performance environments.

SPRAS accommodates this by allowing users to specify which container framework to use globally within their workflow configuration.

The global workflow control section in the configuration file allows a user to set which SPRAS supported container framework to use:

containers:
    framework: docker

The frameworks include Docker, Apptainer/Singularity, or dsub

Benchmarking Datasets

# add this part in # Should link to the benchmarking repo # We are working on the vision of the live benchmarking website