rocco 0.6.5.1

Robust ATAC-seq Peak Calling for Many Samples via Convex Optimization

[R]obust [O]pen [C]hromatin Detection via [C]onvex [O]ptimization

Underlying ROCCO is a constrained optimization problem that can be solved efficiently to predict consensus regions of open chromatin across multiple samples.

Features

1. Explicitly accounts for both enrichment and spatial characteristics of open chromatin signals to capture the full extent of peaks;
2. No arbitrary thresholds on the minimum number of supporting samples/replicates;
3. Is efficient for large numbers of samples with an asymptotic time complexity independent of sample count;
4. Does not require training data or initial candidate peak regions which are hard to define given the lack of a priori sets of open chromatin regions;
5. Employs a mathematically tractable model permitting guarantees of performance and efficiency.

Quick Start Demo

The quick start demo is an interactive Jupyter Notebook showcasing ROCCO's functionality.

Documentation

API Reference: https://nolan-h-hamilton.github.io/ROCCO/index.html

Citation

ROCCO: A Robust Method for Detection of Open Chromatin via Convex Optimization
Nolan H. Hamilton, Terrence S. Furey
bioRxiv 2023.05.24.542132; doi: https://doi.org/10.1101/2023.05.24.542132

Additional dependencies for optional features:

• 'mosek': Commercial grade solver. Users can instantly obtain a free academic license or generous trial commericial license at https://www.mosek.com/products/academic-licenses/.
• 'ortools': includes the first-order solver, PDLP.
• 'pytest': allows local execution of the Tests workflow.