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sTPLS: identifying common and specific correlated patterns under multiple biological conditions.

Jinyu Chen1, Wenwen Min2

  • 1School of Mathematics, Statistics and Mechanics, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing 100124, China.

Briefings in Bioinformatics
|April 26, 2025
PubMed
Summary

A new method, sparse tensor-based partial least squares (sTPLS), integrates multi-omics data to uncover shared and condition-specific biological relationships. This approach aids in understanding tissue development and disease mechanisms across various biological contexts.

Keywords:
cell communicationcommonality and specificitycomodule discoveryintegrative analysisregulatory relationship

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Large-scale biological data offers insights into tissue development and disease.
  • Existing methods struggle to identify common and condition-specific associations across diverse biological conditions.

Purpose of the Study:

  • To develop a novel method for integrating multiple datasets with different biological conditions.
  • To identify shared and condition-specific associations between different feature types.

Main Methods:

  • Developed the sparse tensor-based partial least squares (sTPLS) method.
  • Integrated pairwise datasets from different biological conditions.
  • Applied sTPLS to pharmacogenomic, single-cell, and tensor-structured data.

Main Results:

  • Identified condition-specific and shared gene-drug comodules across seven cancer types.
  • Uncovered condition-specific and shared gene-peak comodules in single-cell data.
  • Revealed shared and distinct cell communication patterns in COVID-19 patients.

Conclusions:

  • sTPLS effectively identifies biologically meaningful relationships across diverse conditions.
  • The method is versatile for multi-omics integrative analysis.
  • sTPLS enhances understanding of tissue development and disease progression.