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A practical tool for maximal information coefficient analysis.

Davide Albanese1, Samantha Riccadonna1, Claudio Donati1

  • 1Computational Biology Unit, Research and Innovation Centre, Fondazione Edmund Mach, via E. Mach 1, 38010 S. Michele all'Adige (TN), Italy.

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Summary
This summary is machine-generated.

MICtools identifies complex associations in omics data using TICe and MICe estimators. This software pipeline assesses statistical significance, highlighting relationships missed by traditional methods.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Identifying complex associations in large omics datasets is crucial for data exploration.
  • Mutual information-based measures, like TICe and MICe estimators, offer efficient and robust methods for association analysis.
  • A software implementation for these measures, including significance testing, is needed.

Purpose of the Study:

  • To present MICtools, a software pipeline for identifying and assessing complex associations in omics data.
  • To combine the TICe and MICe estimators into a comprehensive analysis procedure.
  • To provide a robust method for assessing the statistical significance of identified associations.

Main Methods:

  • MICtools integrates TICe and MICe estimators for relationship identification.
  • A permutation-based strategy is employed to assess the statistical significance of associations.
  • The pipeline's performance is validated using synthetic datasets and a metagenomic dataset.

Main Results:

  • MICtools effectively identifies relationships of varying complexity.
  • The software accurately calculates the strength of associations and their statistical significance.
  • Performance evaluation on synthetic data demonstrates the approach's capabilities.

Conclusions:

  • MICtools provides a powerful tool for omics data exploration.
  • The pipeline successfully highlights associations that may be missed by conventional methods.
  • The integration of TICe and MICe offers a significant advancement in association discovery.