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

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 approac

SUBMITTER: Gunsalus LM 

PROVIDER: S-EPMC10690235 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature