Integration of FullCoverage Probabilistic Functional Networks with Relevance to Specific Biological

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Integration of Full-Coverage Probabilistic
Functional Networks with Relevance to Specific
Biological Processes

• James, K., Wipat, A. Hallinan, J.
• School of Computing Science, Newcastle University
• Data Integration in the Life Sciences 2009

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Integrated functional networks

• Bring together data from a wide range of sources
• High throughput data is
• Large (one node per gene multiple interactions
  per node)
• noisy (FP 20 90)
• Incomplete (to different extents)
• Assess quality of each dataset against a Gold
  Standard
• Weighted edges reflect sum probability that edge
  actually exists
• Network can be thresholded to draw attention to
  most probable edges
• Suitable for manual (interactive) or
  computational analysis

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Dataset bias

• Different experiment types provide different
  types of information
• Overlap between datasets usually low
• 1 of synthetic lethal pairs physically interact
• Genes involved in the same process may be
  transcribed together
• Ribosomal biogenesis in yeast
• Some types of interaction may provide more
  information about a particular biological process
• Complex formation Y2H
• Signal transduction phosphorylation

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Bias in HTP datasets
From Myers and Troyanskaya, Bioinformatics 2007.
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Bias Relevance

• Most network analyses are related to a Process of
  Interest (PoI)
• PFINs tend to be very large
• Interactions with equal probability will have
  different utility
• Several attempts to eliminate bias
• Loss of data
• We aim to use bias
• Relevance

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Hypothesis

• Functional annotations can be applied to
  probabilistic integrated functional networks to
  identify interactions relevant to a biological
  process of interest

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Network integration
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Network integration
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Effect of D value
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Relevance scoring

• GO annotations
• One-tailed Fishers exact test to score
  over-representation of genes related to POI
• POI term of interest plus any descendants except
  inferred from electronic annotation
• Control network integrated in order of confidence
• Relevance network integrated in order of
  relevance
• We use Lee et al. (2004), but method can be
  applied to any network, any data integration
  algorithm

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Relevance scoring
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Data sets

• Saccharomyces cerevisiae data from BioGRID v.38
• Split by PMID, duplicates removed
• Datasets gt 100 interactions treated individually
• 50 data sets, max 14,421 interactions
• Datasets lt 100 grouped by BioGRID Experimental
  categories
• 22 data sets, min 33 interactions
• Gene Ontology terms
• Telomere Maintenance (GO0000723)
• Ageing (GO0007568)

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Choice of D value

• GO annotations
• Assign function to nodes based on annotation of
  neighbour with highest weighted edge
• Leave-one-out on full network
• Construct Receiver Operating Characteristic (ROC)
  curve
• Area Under Curve (AUC)
• SE(W) using Wilcoxon statistic

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Classifier output
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ROC Curves
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D value
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D value
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Ranking
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Results
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Evaluation - Clustering

• MCL Markov-based clustering algorithm
• Considers network topology and edge weights

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Results
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Cluster annotation
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Conclusions

• Function assignment is statistically
  significantly better, but probably not
  practically useful
• Simplistic algorithm
• Dependant upon existing annotation
• Clustering
• Fewer, larger clusters
• Clusters draw together genes of interest
• Different GO terms perform differently
• Relevance networks are better for interactive
  exploration
• Related PoIs

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Future work

• Which GO terms work best with relevance?
• Why?
• Further exploration of experimental types and
  relevance
• Implement algorithms in Ondex
• Optimize function assignment / clustering
  algorithms
• Extend technique to edges

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Acknowledgements

• Centre for Integrated Systems Biology of Ageing
  and Nutrition (CISBAN)
• Newcastle Systems Biology Resource Centre
• Research Councils of the UK
• BBSRC SABR Ondex Project
• Integrative Bioinformatics Research Group