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From high-throughput data to network biology: gain in statistical power and biological relevance
Stockholm Bioinformatics CentreAndrey Alexeyenko
Why Most Published Research Findings Are False
“Positive facts”: the discoveries we are after, e.g. genomic associations, differentially expressed genes, relations “phenotype<->disease” etc.
Statistical model: no positive facts, and an allowed rate of Type I error
True negatives False positives Positive facts True positives
Biological reality: negative facts are the vast majority, positive facts are yet to be discovered
Negative facts
Network is just a graph!
The fact that I can draw a network does not yet make it a biological reality!..
D. rerio, 17.3% D. melanogaster, 9.8%
C. elegans, 9.3%
S. cerevisiae, 10.2%
A. thaliana, 6.5%
R. norvegicus, 5.1%
M. musculus, 25.4%
H. sapiens, 16.5%
D. rerio, 17.3% D. melanogaster, 9.8%
C. elegans, 9.3%
S. cerevisiae, 10.2%
A. thaliana, 6.5%
R. norvegicus, 5.1%
M. musculus, 25.4%
H. sapiens, 16.5%
Phylogenetic profiling, 18.6%
Protein interactions, 10.6%
Protein expression, 6.1%
TF targeting, 12.3%
miRNA targeting, 2.0%
Sub-cellular localization, 7.3% mRNA expression, 43.1%
Phylogenetic profiling, 18.6%
Protein interactions, 10.6%
Protein expression, 6.1%
TF targeting, 12.3%
miRNA targeting, 2.0%
Sub-cellular localization, 7.3% mRNA expression, 43.1%
A
Enrichment of functional groups
Enrichment analysis in the networks turns to be more powerful than on gene lists
Why going “list network” is an advancement?
• Functional context
• “Anchoring”, i.e. interdependence
• Biological interpretability
• Statistical features
• Data integration
Many of those can be applied to the lists as well, but mind the flexibility!
Ways to augment confidence
Trivial:1) increase power2) decrease false prediction rate
• Data integration– Evaluation prior to integration!
• Consider biological context
• Remove spurious edges
• Generalize to a higher level of organization
Ways to evaluate confidence
• Supervised learning
• Balance comprehensiveness and complexity (s.c. information criteria)
• Benjamini-Hochberg
• Show it a biologist
• Go out to the real world and test