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Inferring differentially expressed pathways using kernel maximum mean discrepancy-based test.

Esteban Vegas1, Josep M Oller2, Ferran Reverter2,3

  • 1Department of Statistics, University of Barcelona, Diagonal, 643, Barcelona, 08028, Spain. evegas@ub.edu.

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|June 14, 2016
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Summary
This summary is machine-generated.

Maximum Mean Discrepancy (MMD) analysis effectively detects differential gene expression in pathways. Kernel methods enhance this statistical test for integrating diverse omics data, improving biological pathway analysis.

Keywords:
Kernel maximum mean testKernel-based methodsOmics data integration

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Area of Science:

  • Bioinformatics
  • Statistical Genomics
  • Systems Biology

Background:

  • Pathway expression is inherently multivariate, requiring statistical methods to infer differences between mean vectors for detecting differential expression between conditions.
  • Maximum Mean Discrepancy (MMD) is a statistical test used to determine if two samples originate from the same distribution, with kernel methods simplifying its implementation.

Purpose of the Study:

  • To apply a Maximum Mean Discrepancy (MMD)-based statistical test for detecting differentially expressed pathways between biological conditions.
  • To leverage kernel methods for data integration within the MMD framework, specifically incorporating additional biological measurements.

Main Methods:

  • Utilized a Maximum Mean Discrepancy (MMD) statistical test, enhanced by kernel methods, to compare gene expression profiles between two conditions.
  • Integrated supplementary data, such as hepatic fatty acid levels, into the kernelized MMD test procedure.

Main Results:

  • The MMD-based test successfully identified differential gene expression.
  • Specifically, the test detected differential expression in genes associated with fatty acid metabolic pathways.
  • The integration of hepatic fatty acid levels into the test procedure was successfully achieved using the kernel method.

Conclusions:

  • Maximum Mean Discrepancy (MMD) is a versatile non-parametric statistical test.
  • Kernelization of data offers significant advantages, including handling more variables than samples and enabling omics data integration.
  • The kernelized MMD approach is applicable to various data types beyond vectors, including strings and sequences common in molecular biology.