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 runs different combinations of parameter values on a dataset 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.

SPRAS will also include automatically narrowing down a parameter grid for each algorithm on each dataset using a two-stage grid search. Instead of tuning to a gold standard or a single metric, the search uses graph topological heuristics (rules based on statistics like node count and edge count) to discard subnetworks that are biologically implausible. In the first stage, SPRAS runs each algorithm over a coarse grid: a small set of parameter values spread across a wide range with large gaps between them. Parameter combinations whose output subnetworks pass the heuristics are kept, and the rest are discarded. Because the underlying data differ from dataset to dataset, the set of passing combinations also differs.

In the second stage, SPRAS refines the surviving combinations into a finer grid. For each passing combination, it varies one parameter at a time to sample values near the ones that worked. For example, if b = 5, d = 10, w = 2 passed, SPRAS also tries neighbors such as w = 1 and w = 3 or d = 5 and d = 15. A neighbor is evaluated as long as at least one of its adjacent coarse-grid values passed, so the search can still explore just past the edge of the passing region. The same heuristics filter these neighbors, and the combinations that survive both stages form the final fine-tuned grid for that algorithm and 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