Robust ATAC-seq Peak Calling for Many Samples via Convex Optimization
Project description
[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
- Explicitly accounts for both enrichment and spatial characteristics of open chromatin signals to capture the full extent of peaks;
- No arbitrary thresholds on the minimum number of supporting samples/replicates;
- Is efficient for large numbers of samples with an asymptotic time complexity independent of sample count;
- 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;
- 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
If using ROCCO in your research, please cite the corresponding paper in Bioinformatics.
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.
Project details
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