Framework for Explainable Predictive Oncology and Target Prioritization • powerup

Framework for Explainable Predictive Oncology and Target Prioritization

POWERUP is a framework for building explainable predictive models of therapeutic vulnerabilities from molecular profiles. It is available through the open-source R package documented here and the web-based POWERUP Portal.

Overview

The POWERUP R package provides a standardized and accessible workflow for training machine learning models, evaluating model performance, and generating predictions in new samples. It includes tools for interpreting the molecular features contributing to predictions and integrating new experimental measurements using Bayesian updating. POWERUP also generates analysis-ready summaries and visualizations to support target prioritization and downstream experimental validation.

Installation

Install POWERUP directly from GitHub:

install.packages("remotes")
remotes::install_github("hts-lab/powerup", ref = "v1.0.117")

See Installing POWERUP for requirements and further setup notes.

Main workflow

Step	Purpose	Main functions
Prepare	Assemble reference and prediction data	`prepare_powerup_data()`
Train	Train and evaluate predictive models	`fit_powerup_models()` `summarize_models()`
Predict	Generate sample-level predictions	`add_powerup_predictions()` `summarize_predictions()`
Explain	Examine important features contributing to sample-level predictions and potential diversity in explanations across samples	Local features `summarize_contributions()` `plot_contributions_to_sample()` Explanation diversity `calculate_explanation_paths()` `plot_explanation_paths()`
Observe	Process experimental observations	`prepare_powerup_observations()`
Update	Combine predictions with observations	`calculate_powerup_posteriors()` `plot_posterior()`

Documentation

Start with Get Started with POWERUP. The focused guides cover:

Individual functions are documented in the reference index. Data provenance and acknowledgement information for the bundled examples are documented in Example data and provenance.

Citation

If you use POWERUP in your research, please cite the associated manuscript:

Al-Jazrawe, M.*, Dede, M.*, Nanda, N., Trepicchio, C.J., Johnson, G.A., Cebula, K., Abeyta, E., Ostrovsky, N., Rutherford, K.A., Vo, H.P., Neiswender, J.V., Brenan, L., Krill-Burger, J.M., Keskula, P., Tseng, Y.-Y., Van Hare, B., Haddox, C.L., Soragni, A., Vazquez, F., and Boehm, J.S. Single tumor transcriptional dependency inference prioritizes rare cancer targets. Publication details: TBD.

The citation is also available from R:
```
citation("powerup")
```
For a published analysis, record the POWERUP version:
```
packageVersion("powerup")
```
If you use the POWERUP Portal for an analysis, in addition to citing the relevant POWERUP paper, please consider also including a reference to the POWERUP Portal website in your manuscript: https://powerup.htslab.org.

The POWERUP Portal provides access to pre-run public analyses and an interactive interface for launching analyses on new samples without installing the R package.

How to get help and source code

POWERUP is maintained by the HTS Lab. Feel free to reach out to hts-lab@mit.edu or open an issue if you need help implementing POWERUP in your research.

The source code of the R package is available on GitHub and is released under the MIT License.

POWERUP Portal

The HTS POWERUP Portal provides an interactive interface for running analyses without directly using the R package. The portal additionally orchestrates large analyses (e.g. training predictive models for thousands of perturbations, or inferring vulnerabilities for hundreds of new samples) that would otherwise take too long or require too much memory to complete using this R package alone.

Public analyses are available without an account. To request access to launch analyses on new samples, contact hts-lab@mit.edu.