Extraction of Organism Groups from Phylogenetic Profiles Using Independent Component Analysis

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Extraction of Organism Groups from Phylogenetic
Profiles Using Independent Component Analysis

• Yamanishi, M., Itoh, M., Kanehisa, M.
• Genome Informatics 13 61-70, 2002
• Summarized by Jeong-Ho Chang

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• Goal extract organism groups and their hierarchy
  from phylogenetic profiles using ICA.
• Find independent components that characterize
  major organism groups.
• Identify genes that are characteristic to each
  organism group.

Hierarchical clustering of organisms
Gene identification
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Phylogenetic profiles

• Definition a bit pattern that encodes the
  presence or absence of conserved (orthologous)
  genes in a set of organisms.
• Application
• Functional prediction of genes when two genes
  share similar phylogenetic profiles, it is
  assumed that these genes are functionally
  correlated.
• Construction of genome trees stems from the
  assumption that gene losses or acquisitions are
  major evolution phenomena.

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Independent Component Analysis

• ICA
• A linear transformation method in the field of
  statistics and signal processing.
• Represent a set of variables as a linear
  combination of latent variables which are
  statistically independent each other.

IC score
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Experiments

• Data set
• Phylogenetic profiles constructed from 2875
  orthologous genes in 77 organisms.
• KEGG/GENES database as of May 2002.
• 6 eukaryotes, 13 archaea, and 58 bacteria.
• Grouping of organisms
• 2875 x 77 ? 2875 x 18
• For the interpretation of biological meanings of
  each ICs, correlation coefficients for all
  combinations of 77 organisms and 18 ICs were
  computed.

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E
A
B

• 9 out of 18 components were well correlated with
  specific organism groups.
• 74 organisms were well represented by the 9 ICs.
• Exception Deinococcus raiodurans, Aquifex
  aeolicus, Thermotoga maritima

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• Hierarchy of organism groups
• Original data set ?? result of ICA.
• Distance in original data set hamming distance.
• Distance in reduced set correlation coefficient.
• In case of the reduced set, only 9 ICs are used.
• Complete linkage hierarchical clustering.

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• Identification of Genes
• The result of ICA can be used to identify genes
  that are clustered at high and low scores along
  each independent component.

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Discussion

• Proposed to use the ICA for extraction of
  organism groups from phylogenetic profiles.
• ICA is an appropriate method to detect biological
  features
• ICA attempts to maximize nongaussianity.
• PCA attempts to maximize variance ? interrupt the
  process of detecting biologically meaningful
  features.
• Future works
• The development from independent components to
  tree components.
• Incorporating phylogenetic tree structure for the
  similarity of two phylogenetic profiles.

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