Hi-C Matrix Denoising for Chromatin Aberration Detection
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Solution Overview
Problem
Conventional High-Throughput Chromosome Conformation Capture (Hi-C) methods face challenges in reliably comparing chromatin structures across different biological samples due to systematic biases, making it difficult to identify global chromatin structural changes, especially in cancerous cells, which are crucial for diagnosis and treatment.
Innovation Solution
The method involves denoising, balancing, and ranking Hi-C matrices to generate an enhanced Hi-C matrix using algorithms like Diffusion State Distance and Laplacian eigenmaps, allowing for the identification of structural chromatin aberrations by comparing 3D chromatin structures between normal and aberrant cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional Hi-C methods are used to compare chromatin structures across different biological samples, then the analysis can be performed, but systematic biases make it difficult to reliably identify global chromatin structural changes
Solution Approach 1:
The patent extracts and removes systematic biases from the Hi-C matrix through denoising operations. Specifically, it performs denoising on the raw Hi-C matrix to obtain a balanced distance matrix, and further denoising to obtain a denoised distance matrix. This extraction of bias information allows for reliable comparison of chromatin structures across different biological samples while maintaining measurement precision for detecting structural aberrations.
2Ease of manufacture
If raw Hi-C matrices are used directly for analysis, then the workflow is simple, but systematic biases affect the reliability of downstream interpretations
Solution Approach 1:
The patent applies preliminary denoising actions to the Hi-C matrix before downstream analysis. The process includes initial denoising to obtain a balanced distance matrix, followed by further denoising to obtain a denoised distance matrix. These preliminary actions remove systematic biases early in the workflow, ensuring that subsequent downstream analyses are reliable while maintaining a relatively simple overall process.
3Productivity
If conventional normalization methods are applied, then the Hi-C data can be processed, but the methods are difficult to analyze effectively and lack reliability
Solution Approach 1:
The patent changes the parameters and approach of normalization by using denoising operations instead of conventional normalization methods. It performs denoising on the Hi-C matrix to obtain a balanced distance matrix, and further denoising to obtain a denoised distance matrix. This parameter change in the processing approach maintains productivity while significantly improving the reliability of the normalized data for downstream analysis.
Data Source
AI summary
A method for generating an enhanced Hi-C matrix, a non-transitory computer readable medium storing a program for generating an enhanced Hi-C matrix, a method for identifying a structural chromatin aberration in an enhanced Hi-C matrix, and methods for diagnosing and treating a medical condition or disease. The method for generating an enhanced Hi-C matrix includes denoising an input Hi-C matrix to obtain a balanced distance matrix, denoising the balanced distance matrix to obtain a denoised distance matrix, sorting and ranking the denoised distance matrix to obtain a ranked distance matrix, calculating an adjacency matrix based on the ranked matrix, and calculating Laplacian eigenmaps of the adjacency matrix to obtain an enhanced Hi-C matrix.