ChromaFactor: deconvolution of single-molecule chromatin organization with non-negative matrix factorization

Area of Science:

• Genomics
• Molecular Biology
• Computational Biology

Background:

• Single-cell chromatin organization analysis is crucial for understanding genome structure-function relationships.
• Inherent cellular heterogeneity complicates the analysis of single-molecule chromatin data.
• Identifying individual chromatin fiber contributions to bulk trends remains a challenge.

Approach:

• We introduce ChromaFactor, a novel computational method utilizing non-negative matrix factorization.
• ChromaFactor deconvolves single-molecule chromatin organization datasets into salient primary components.
• The approach identifies trends explaining maximum variance and individual molecule contributions.

Key Points:

• Primary components derived from ChromaFactor correlate significantly with functional phenotypes.
• These phenotypes include active transcription, enhancer-promoter distances, and genomic compartments.
• The method was validated on two distinct single-molecule imaging datasets.

Conclusions:

• ChromaFactor provides a robust tool for dissecting chromatin structure-function interplay at the single-molecule level.
• It pinpoints subpopulations driving functional changes, offering insights into cellular heterogeneity.
• This facilitates a deeper understanding of bulk genomic phenomena driven by individual DNA molecules.