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From high-throughput data to network biology: gain in statistical power and biological relevance

Stockholm Bioinformatics CentreAndrey Alexeyenko

PLoS Med 2005 2(8):e124

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!..

Conversion “data pieces confidence” in a Bayesian framework

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

Enrichment of functional groups

Partial correlations

rPLC = 0.88

rPLC = 0.95

rPLC = 0.76

Benjamini-Hochberg correction

Quantitative modeling of multi-component system with mutually dependent elements

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

Ways to employ confidence

• Initialize network

• Add node and edge attributes to the network

• Filter network elements for higher relevance

• Build more complex models accounting for confidence


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