Methods to Assess the Reproducibility and Similarity of Hi-C Data

Yang, Tao; He, Xi; An, Lin; Li, Qunhua

doi:10.1007/978-1-0716-1390-0_2

Cited by 4 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MAE, SSIM and PSNR are defined by the following equations: where Ŷ denotes the predicted correlation matrices and Y represents the real correlation matrices. Furthermore, considering the chromatin compartmentalization information of the correlation matrices, we evaluated their reproducibility using multiple Pearson Correlation Coefficient (PCC) and GenomeDISCO score [52].…”

Section: Methodsmentioning

confidence: 99%

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

Li,

Zhang,

et al. 2024

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

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

Li,

Zhang,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Quantifying the similarity between Hi-C contact matrices is essential for understanding how the 3D genome organization differs among various cell lines or under different biological conditions. Although the scarcity of the data and the intricate nature of artifacts (7, 8) render this task difficult, the recognition of inherent geometric shapes and patterns has been facilitated by the representation of contact matrices as images and networks (9). Similar images will share geometric layouts of local self-similarity (10).…”

Section: Introductionmentioning

confidence: 99%

ENT3C: an entropy-based similarity measure for Hi-C and micro-C derived contact matrices

Lainscsek,

Taher

2024

Preprint

View full text Add to dashboard Cite

Motivation: 3C-sequencing technologies have shed light on the profound importance of 3D genome organization in cellular function. The resulting contact matrices are extremely sparse and susceptible to technical- and sequence-based biases, making their comparison challenging. The development of reliable, robust and efficient methods for quantifying similarity between contact matrix is crucial for investigating variations in the 3D genome organization between different cell types or under different conditions, as well as evaluating the reproducibility of 3C-based experiments. Results: ENT3C is a novel method for quantifying contact matrix similarity. Specifically, ENT3C compares the entropy signals representing the complexity of the patterns along the diagonals of two contact matrices. The entropy signal itself can also be used for biologically relevant analyses.

show abstract

“…Quantifying the similarity between Hi-C and micro-C contact matrices is essential for understanding how the 3D genome organization differs among various cell lines or under different biological conditions. However, the scarcity of the data and presence of intricate artifacts ( 7 , 8 ) render this task difficult. Traditional statistics such as the Pearson correlation coefficient are not well suited for comparing contact matrices, as they often scores biological replicate contact matrices similarly to unrelated samples ( 8 , 9 ).…”

Section: Introductionmentioning

confidence: 99%

ENT3C: an entropy-based similarity measure for Hi-C and micro-C derived contact matrices

Lainscsek,

Taher

2024

NAR Genomics and Bioinformatics

View full text Add to dashboard Cite

Hi-C and micro-C sequencing have shed light on the profound importance of 3D genome organization in cellular function by probing 3D contact frequencies across the linear genome. The resulting contact matrices are extremely sparse and susceptible to technical- and sequence-based biases, making their comparison challenging. The development of reliable, robust and efficient methods for quantifying similarity between contact matrices is crucial for investigating variations in the 3D genome organization in different cell types or under different conditions, as well as evaluating experimental reproducibility. We present a novel method, ENT3C, which measures the change in pattern complexity in the vicinity of contact matrix diagonals to quantify their similarity. ENT3C provides a robust, user-friendly Hi-C or micro-C contact matrix similarity metric and a characteristic entropy signal that can be used to gain detailed biological insights into 3D genome organization.

show abstract

Methods to Assess the Reproducibility and Similarity of Hi-C Data

Cited by 4 publications

References 16 publications

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

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

ENT3C: an entropy-based similarity measure for Hi-C and micro-C derived contact matrices

ENT3C: an entropy-based similarity measure for Hi-C and micro-C derived contact matrices

Contact Info

Product

Resources

About