ICA

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Clustering Gene Expression Data Using Independent
Component Analysis
Stephen C. Billups University of Colorado at
Denver Department of Mathematics Larry
Hunter University of Colorado Health Sciences
Center Department of Pharmacology
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xAs ? noise
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Key Points

• ICA clustering attractive for gene expression
  data
• Accounts for and identifies independent hidden
  effects that influence gene expression.
• Allows clusters with markedly different shapes
  and dimensionalities to be identified.
• Bayesian approach allows prior knowledge to be
  incorporated. (semi-supervised learning).
• Algorithm works only when underlying effects
  have non-Gaussian distributions.
• The algorithm is made tractable by using a
  variational Bayesian method with some sensible
  simplifications.
• Opportunities exist for improving the
  computational efficiency.