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Novel AI-powered computational method using tensor decomposition for identification of common optimal bin sizes when

Y-H Taguchi1, Turki Turki2

  • 1Department of Physics, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan. tag@granular.com.

Scientific Reports
|March 3, 2025
PubMed
Summary

Choosing the right bin size for multiple Hi-C datasets is tricky. This study introduces tensor decomposition for automatically finding the optimal resolution, enabling better integration of genomic interaction data.

Keywords:
AIAdvances in unsupervised learningFeature extractionGenomeHi-CTensor decomposition

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Integrating multiple Hi-C datasets requires a common bin size, which is challenging as dataset quality varies with resolution.
  • Determining optimal bin sizes is crucial for accurately identifying common structural features in Hi-C data.
  • Existing quality assessments have limitations in selecting a single optimal bin size applicable across diverse Hi-C datasets.

Purpose of the Study:

  • To propose a novel method for selecting optimal bin sizes for integrating multiple Hi-C datasets.
  • To enable automatic empirical estimation of the highest resolution for multi-dataset Hi-C analysis.
  • To overcome the limitations of manual threshold setting and dataset-specific quality assessments.

Main Methods:

  • Application of tensor decomposition (TD) for unsupervised feature extraction (FE).
  • Utilizing phase transition-like phenomena observed in TD-based FE to identify optimal bin sizes.
  • Analysis of Hi-C datasets retrieved from GEO (GEO IDs: GSE260760, GSE255264).

Main Results:

  • The proposed TD-based unsupervised FE method automatically estimates the smallest possible bin size (highest resolution).
  • This approach empirically determines the optimal resolution without requiring tunable parameters.
  • Demonstrated ability to optimize bin sizes across multiple Hi-C profiles.

Conclusions:

  • Tensor decomposition provides a robust, parameter-free method for optimizing bin sizes in multi-dataset Hi-C integration.
  • This technique facilitates more accurate identification of common genomic structures across datasets.
  • Represents a novel advancement in analyzing and integrating Hi-C data.