A framework for decomposing DNase I Hypersensitive Sites (DHSs) measured across 100s of biosamples using Non-Negative Matrix Factorization (NMF), with the goal of developing a regulatory "Vocabulary" for the accessible human genome.
Object-oriented (OO) implementation, requiring Python 3 with scikit-learn, numpy, scipy, matplotlib, pandas installations.
The primary OO-developed code are distributed in the root directory.
Various analysis and visualization efforts are demonstrated in the notebooks directory using Jupyter notebooks.
Requisite data are stored in the data directory, when possible.
Several larger files that are incompatible with Github are available through Zenodo.
Supporting scripts to obtain peak or signal data for new unseen biosamples are available in scripts.
Authors: Alexander (Sasha) Muratov & Wouter Meuleman
- Basic procedure using random binary data. Shows how to decompose a random binary matrix and measure reconstruction quality.
- Unsupervised metrics for choosing number of NMF components. Shows how we chose k=16 NMF components using unsupervised metrics.
- Demonstrating decomposition results of ENCODE DNase-Seq biosamples. Demonstrates how we decomposed a binary 733x3.6M matrix into a Vocabulary of regulatory components
- Visualizing decomposition results of ENCODE DNase-Seq biosamples. Visualizes the results of the decomposition in the previous notebook. Includes UMAP projection.
- Demonstrating the embedding of new biosamples into the coordinate space. Demonstrates how one can embed new (unseen) DNase-seq data into an existing NMF decomposition.
- Visualizing the embedding of new biosamples into the coordinate space. Visualizes the newly added biosamples alongside the existing 733. Includes UMAP projections.