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

## TL;DR

ChromaFactor is a new computational tool that helps analyze single-cell chromatin structures to better understand how genome organization relates to function.

## Contribution

ChromaFactor introduces a non-negative matrix factorization approach to deconvolve single-molecule chromatin data into primary components linked to functional phenotypes.

## Key findings

- Primary components identified by ChromaFactor correlate with active transcription, enhancer-promoter distance, and genomic compartment.
- Some bulk trends are observed in only a small fraction of cells, indicating rare subpopulations may drive genome organization changes.
- ChromaFactor's components are associated with bulk genomic measurements of transcription factor and structural protein binding.

## Abstract

The investigation of chromatin organization in single cells holds great promise for identifying causal relationships between genome structure and function. However, analysis of single-molecule data is hampered by extreme yet inherent heterogeneity, making it challenging to determine the contributions of individual chromatin fibers to bulk trends. To address this challenge, we propose ChromaFactor, a novel computational approach based on non-negative matrix factorization that deconvolves single-molecule chromatin organization datasets into their most salient primary components. ChromaFactor provides the ability to identify trends accounting for the maximum variance in the dataset while simultaneously describing the contribution of individual molecules to each component. Applying our approach to two single-molecule imaging datasets across different genomic scales, we find that these primary components demonstrate significant correlation with key functional phenotypes, including active transcription, enhancer-promoter distance, and genomic compartment. Also, we find that some bulk trends exist at the single-cell level, but only in a small fraction of cells, suggesting that critical changes in genome organization may be driven by specific rare subpopulations rather than occurring uniformly across all cells. ChromaFactor offers a robust tool for understanding the complex interplay between chromatin structure and function on individual DNA molecules, pinpointing which subpopulations drive functional changes and fostering new insights into cellular heterogeneity and its implications for bulk genomic phenomena.

Emerging imaging technologies are generating high-resolution, single-molecule chromatin interaction data for individual loci, providing an opportunity to identify the chromatin structures that contribute to patterns seen in bulk genomics assays or that correlate with variability in gene regulation. However, chromatin conformation varies dramatically between cells, making it challenging to determine which patterns are important for transcriptional activity and which represent inherent heterogeneity. We present ChromaFactor, a new method to address this problem based on non-negative matrix factorization. ChromaFactor identifies the major interaction patterns across chromatin fibers as well as the individual molecules contributing to each pattern. We analyzed human and fly single-molecule imaging data, finding significant correlations between ChromaFactor’s primary components and paired molecular phenotypes measured in the same cells, including active transcription, enhancer-promoter distance, and genomic compartment. Interaction patterns in primary components are also associated with bulk genomic measurements of transcription factor and structural protein binding. These findings demonstrate that ChromaFactor provides a robust framework for understanding the complex interplay between chromatin structure and function at the single-molecule level by treating single-cell chromatin measurements not as independent instances, but as snapshots of continuous, dynamic states.