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

AI Article Synopsis

  • The study focuses on analyzing chromatin organization in single cells to understand how genomic structure relates to its functions, which has been difficult due to variability in single-molecule data.
  • The researchers introduce a computational method called ChromaFactor that uses non-negative matrix factorization to break down complex chromatin data into key components, allowing better analysis and interpretation.
  • When applied to various datasets, ChromaFactor reveals significant connections between chromatin structure and important biological processes like transcription and enhancing-promoter interactions, helping to clarify the relationship between individual molecular behavior and overall genomic activity.

Article 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. 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10690235PMC
http://dx.doi.org/10.1101/2023.11.22.568268DOI Listing

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