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Related Experiment Videos

Cluster inference methods and graphical models evaluated on NCI60 microarray gene expression data.

P J Waddell1, H Kishino

  • 1Chugai Research Institute for Molecular Medicine, 153-2 Nagai Niihari Ibaraki 300-4101, Japan. waddell@cimmed.com

Genome Informatics. Workshop on Genome Informatics
|November 9, 2001
PubMed
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Developing new methods for inferring genome-wide gene expression relationships is crucial. Novel distances based on partial correlations, combined with tree analysis, offer a promising approach for uncovering regulatory genetic links.

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Genome-wide gene expression analysis lacks robust causal inference methods.
  • Standard correlation methods may not fully capture regulatory genetic links.

Purpose of the Study:

  • To develop and evaluate novel methodologies for inferring causal gene expression relationships.
  • To assess the utility of partial correlations and advanced clustering techniques in identifying regulatory networks.

Main Methods:

  • Examination of hierarchical clustering and tree analysis algorithms.
  • Introduction of two novel distance metrics based on partial correlations.
  • Application of tree consensus and partition metrics for method comparison.
  • Graphical modeling of cell cycle gene interactions.

Related Experiment Videos

Main Results:

  • New distance metrics produce significantly different tree structures compared to standard methods.
  • Graphical models face challenges due to latent variables and conditional distributions.
  • TP53 and other cancer-related genes influence sampling distributions.

Conclusions:

  • Partial correlations offer improved evidence for regulatory genetic links.
  • Accurate causal inference requires including all relevant genes and splice variants in microarray design.
  • Ignoring gene variants and related genes leads to analytical complications.