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Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.
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A general index for linear and nonlinear correlations for high dimensional genomic data.

Zhihao Yao1,2, Jing Zhang1,2, Xiufen Zou3,4

  • 1School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China.

BMC Genomics
|December 1, 2020
PubMed
Summary

We developed the Kernel-Based RV-coefficient (KBRV) to detect linear and nonlinear correlations in high-dimensional data. This new method improves gene regulatory network construction by analyzing multiple data types effectively.

Keywords:
High-dimensional dataNonlinear correlationRV-coefficient

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Area of Science:

  • Genomics and Bioinformatics
  • Systems Biology

Background:

  • High-throughput sequencing generates high-dimensional data, necessitating methods to detect correlations.
  • Integrating diverse datasets like RNA-sequencing, methylation, and copy number data is crucial for accurate gene regulatory network construction.

Purpose of the Study:

  • To develop a general index for detecting linear and nonlinear relationships between high-dimensional datasets.
  • To introduce a novel method for enhancing the accuracy of gene regulatory network construction.

Main Methods:

  • Proposed the Kernel-Based RV-coefficient (KBRV) by incorporating kernel functions into the modified RV-coefficient (RV2).
  • Validated KBRV using permutation tests and simulated data.
  • Applied KBRV to real-world ovarian cancer and human myeloid differentiation datasets.

Main Results:

  • KBRV effectively detects both linear and nonlinear correlations between matrices.
  • Demonstrated KBRV's superiority over traditional vector correlation methods in analyzing complex biological data.
  • Successfully applied KBRV to construct more accurate gene regulatory networks.

Conclusions:

  • KBRV is an efficient and versatile index for analyzing high-dimensional data.
  • The KBRV method shows significant potential for advancing gene regulatory network construction and multi-omics data integration.