scGHOST: Identifying single-cell 3D genome subcompartments

Xiong, Kyle; Zhang, Ruochi; Ma, Jian

doi:10.1101/2023.05.24.542032

Cited by 3 publications

(2 citation statements)

References 36 publications

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“…These applications include various tasks such as TAD boundary recognition [22,23], chromatin loop detection [24,25], chromatin interaction data enhancement [26,27], interaction matrix generation [28][29][30] and single cell Hi-C imputation [31,32]. While several methods explore contact map generation and enhancement, they lack cell type specificity.…”

Section: Introductionmentioning

confidence: 99%

COCOA: A Framework for Fine-scale Mapping Cell-type-specific Chromatin Compartmentalization Using Epigenomic Information

Li,

Zhang,

et al. 2024

Preprint

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Chromatin compartmentalization and epigenomic modification are crucial in cell differentiation and diseases development. However, precise mapping of chromatin compartmental patterns requires Hi-C or Micro-C data at high sequencing depth. Exploring the systematic relationship between epigenomic modifications and compartmental patterns remains challenging. To address these issues, we present COCOA, a deep neural network framework using COnvolution and attention mechanisms to infer fine-scale chromatin COmpartment pAtterns from six histone modification signals. COCOA extracts 1-D track features through bi-directional feature reconstruction after resolution-specific binning epigenomic signals. These track features are then cross-fused with contact features using an attention mechanism and transformed into chromatin compartment patterns through residual feature reduction. COCOA demonstrates accurate inference of chromatin compartmentalization at a fine-scale resolution and exhibits stable performance on test sets. Additionally, we explored the impact of histone modifications on chromatin compartmentalization prediction through in silico epigenomic perturbation experiments. Unlike obscure compartments observed with 1kb resolution high-depth experimental data, COCOA generates clear and detailed compartmental patterns, highlighting its superior performance. Finally, we demonstrated that COCOA enables cell-type-specific prediction of unrevealed chromatin compartment patterns in various biological processes, making it an effective tool for gaining chromatin compartmentalization insights from epigenomics in diverse biological scenarios.

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Section: Introductionmentioning

confidence: 99%

COCOA: A Framework for Fine-scale Mapping Cell-type-specific Chromatin Compartmentalization Using Epigenomic Information

Li,

Zhang,

et al. 2024

Preprint

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“…Topic modeling 12 , random-walk methods 13 and recent deep learning approaches 14,15 effectively cluster cells into subpopulations. Recent methods also offer rich annotations in single cells, including A/B compartments 14,16 , subcompartments 17 , topologically associating domains (TADs) 14,16 , and chromatin loops 18 . Rajpurkar et al first applied a convolutional neural network to directly predict nascent transcription from chromatin folding 19 and Zhan et al 20 propose an effective deep-learning-based dimensionality reduction method to cluster conformations.…”

Section: Introductionmentioning

confidence: 99%

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

Gunsalus,

Keiser,

Pollard

2023

Preprint

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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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