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An Integrated Approach for Efficient Multi-Omics Joint Analysis.

Massimiliano S Tagliamonte1, Sheldon G Waugh2, Mattia Prosperi3

  • 1Dept. of Path., Imm., and Lab. Med, College of Medicine, UF, Gainesville, FL, USA.

ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine
|October 8, 2019
PubMed
Summary
This summary is machine-generated.

Multi-omics analysis presents challenges in dimensionality and complexity. This study developed a novel strategy for joint-domain analysis, integrating methylomics and microbiomics to identify colorectal cancer risk factors.

Keywords:
Methylationbioinformaticscorrelationdimension reductionjoint analysismicrobiomenetworkprincipal component analysis

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Area of Science:

  • Computational Biology and Bioinformatics
  • Genomics and Microbiomics
  • Cancer Research

Background:

  • Multi-omics data analysis, including methylomics and microbiomics, faces challenges due to high dimensionality, complex interactions, and variable heterogeneity.
  • Understanding the interplay between host epigenetics and the gut microbiome is crucial for deciphering colorectal cancer (CRC) risk.

Purpose of the Study:

  • To develop an efficient strategy for joint-domain analysis of multi-omics data.
  • To investigate the correlations between colon epithelium methylomics and fecal microbiomics in relation to colorectal polyp prevalence, a marker for CRC risk.

Main Methods:

  • Applied domain-specific pipelines for data pre-processing, including quality assessment, cleaning, and batch-effect removal.
  • Performed variable homogenization using domain-specific normalization and dimension reduction to obtain comparable, scale-free variables.
  • Implemented a joint-domain network analysis to identify microbial-methylation island patterns, with preliminary association analysis to pre-select variables.

Main Results:

  • Identified potentially significant associations between methylomics and microbiomics data concerning colorectal polyp prevalence.
  • The joint-domain network analysis revealed specific microbial-methylation island patterns.
  • Despite limitations in sample size, the approach identified several candidate associations after multiple comparison correction.

Conclusions:

  • The developed strategy enables efficient joint-domain analysis of complex multi-omics data.
  • The identified microbial-methylation patterns offer insights into potential mechanisms underlying polyp development.
  • The method facilitates the generation of novel mechanistic hypotheses for CRC etiology by linking omics patterns to biological pathways (e.g., KEGG).