scHiCSC: A Novel Single-Cell Hi-C Clustering Framework by Contact-Weight-Based Smoothing and Feature Fusion

Zhou, Xin; Shi, Zhenqi; Wu, Yingfu; Zhao, Jianmin; Wu, Hao

doi:10.1109/bibm55620.2022.9995134

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…Recent studies have underscored the superiority of leveraging multiple features over individual ones in sequence-based prediction tasks [ 41–44 ]. Consequently, Enhancer-MDLF, proposed in this study, incorporates a dna2vec module and a motif module to comprehensively extract information on enhancer sequences.…”

Section: Resultsmentioning

confidence: 99%

Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers

Zhang,

2024

Briefings in Bioinformatics

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Enhancers, noncoding DNA fragments, play a pivotal role in gene regulation, facilitating gene transcription. Identifying enhancers is crucial for understanding genomic regulatory mechanisms, pinpointing key elements and investigating networks governing gene expression and disease-related mechanisms. Existing enhancer identification methods exhibit limitations, prompting the development of our novel multi-input deep learning framework, termed Enhancer-MDLF. Experimental results illustrate that Enhancer-MDLF outperforms the previous method, Enhancer-IF, across eight distinct human cell lines and exhibits superior performance on generic enhancer datasets and enhancer–promoter datasets, affirming the robustness of Enhancer-MDLF. Additionally, we introduce transfer learning to provide an effective and potential solution to address the prediction challenges posed by enhancer specificity. Furthermore, we utilize model interpretation to identify transcription factor binding site motifs that may be associated with enhancer regions, with important implications for facilitating the study of enhancer regulatory mechanisms. The source code is openly accessible at https://github.com/HaoWuLab-Bioinformatics/Enhancer-MDLF.

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Section: Resultsmentioning

confidence: 99%

Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers

Zhang,

2024

Briefings in Bioinformatics

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“…Most current computational methods for single-cell Hi-C data represent the data as multiple chromatin contact matrices with a given resolution [42][43][44][45][46][47][48][49][50] . However, generating a chromatin contact matrix requires specific information about interacting chromatin fragments in each cell, which is not contained in the raw data files.…”

Section: Data Preparationmentioning

confidence: 99%

scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information

Wu,

Shi,

Zhou

et al. 2023

Preprint

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While scRNA-seq offers gene expression snapshots, it misses the spatial context of chromatin organization crucial for cell cycle regulation. Single-cell Hi-C, capturing chromatin’s three-dimensional (3D) architecture, fills this void, revealing interactions between genomic regions that transcript-only data might overlook. We introduce scHiCyclePred, a model that utilizes single-cell Hi-C’s multi-scale interaction data to predict cell cycle phases by extracting chromatin’s 3D features. This fusion-prediction model integrates three feature sets into a unified vector. Remarkably, scHiCyclePred outperforms methods like NAGANO and CIRCLET and traditional machine learning techniques across various metrics. Our insights into 3D chromatin dynamics during the cell cycle further underscore its utility. By offering a more comprehensive view of cell cycle dynamics through chromatin structure, scHiCyclePred stands to significantly advance our understanding in cellular biology and holds potential to catalyze breakthroughs in disease research. Access scHiCyclePred atgithub.com/HaoWuLab-Bioinformatics/scHiCyclePred.

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scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information

Wu,

Shi,

Zhou

et al. 2024

Commun Biol

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The emergence of single-cell Hi-C (scHi-C) technology has provided unprecedented opportunities for investigating the intricate relationship between cell cycle phases and the three-dimensional (3D) structure of chromatin. However, accurately predicting cell cycle phases based on scHi-C data remains a formidable challenge. Here, we present scHiCyclePred, a prediction model that integrates multiple feature sets to leverage scHi-C data for predicting cell cycle phases. scHiCyclePred extracts 3D chromatin structure features by incorporating multi-scale interaction information. The comparative analysis illustrates that scHiCyclePred surpasses existing methods such as Nagano_method and CIRCLET across various metrics including accuracy (ACC), F1 score, Precision, Recall, and balanced accuracy (BACC). In addition, we evaluate scHiCyclePred against the previously published CIRCLET using the dataset of complex tissues (Liu_dataset). Experimental results reveal significant improvements with scHiCyclePred exhibiting improvements of 0.39, 0.52, 0.52, and 0.39 over the CIRCLET in terms of ACC, F1 score, Precision, and Recall metrics, respectively. Furthermore, we conduct analyses on three-dimensional chromatin dynamics and gene features during the cell cycle, providing a more comprehensive understanding of cell cycle dynamics through chromatin structure. scHiCyclePred not only offers insights into cell biology but also holds promise for catalyzing breakthroughs in disease research. Access scHiCyclePred on GitHub at https:// github.com/HaoWuLab-Bioinformatics/ scHiCyclePred .

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scHiCSC: A Novel Single-Cell Hi-C Clustering Framework by Contact-Weight-Based Smoothing and Feature Fusion

Cited by 5 publications

References 25 publications

Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers

Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers

scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information

scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information

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