Inference of predictive gene interaction networks Benjamin Haibe-Kains DFCI/HSPH December 15, 2011 at the Dana-Farber Cancer Institute and Harvard School of Public Health at the Dana-Farber Cancer Institute and Harvard School of Public Health The Computational Biology and Functional Genomics Laboratory Benjamin Haibe-Kains (DFCI/HSPH) R/Bioconductor Course December 15, 2011 1 / 12
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Inference of predictive gene interaction networks
Benjamin Haibe-Kains
DFCI/HSPH
December 15, 2011
at the Dana-Farber Cancer Institute and Harvard School of Public Healthat the Dana-Farber Cancer Institute and Harvard School of Public Health
The Computational Biology and Functional Genomics Laboratory
Benjamin Haibe-Kains (DFCI/HSPH) R/Bioconductor Course December 15, 2011 1 / 12
Background
Phenotypes result from biological networks, not individual genes
New biotechnologies allow us to analyze multiple genes in parallel:I next generation sequencingI gene expression profilingI Chip-seqI . . .
Understand the complex interactions between genes and the behaviorof a network is fundamental
I to bring new biological insightsI to make ”useful” predictions
Benjamin Haibe-Kains (DFCI/HSPH) R/Bioconductor Course December 15, 2011 2 / 12
Relevance
Aim: Infer reliable predictive gene interaction networks from geneexpression data
Beyond biological understanding, such networks would be efficient toolsfor:
predicting the response of an organism (cancer patient) toperturbations (targeted therapies)
identifying the key genes to target for significantly decreasing apathway activity
optimizing combination of drugs and therapy regiments
Benjamin Haibe-Kains (DFCI/HSPH) R/Bioconductor Course December 15, 2011 3 / 12
Gene Interaction Network
Genes are represented as”nodes”
Interactions are representedby ”edges”
Edges can be directed toshow ”causal” interactions
Edges are not necessarilydirect interactions
gene a
gene b
gene c
gene d
gene e
gene f
gene g
gene h
Benjamin Haibe-Kains (DFCI/HSPH) R/Bioconductor Course December 15, 2011 4 / 12
Gene Interaction Network and Perturbation
gene a
gene b
gene c
gene d
gene e
gene f
gene g
gene h
Highexpression
Lowexpression
gene a
gene b
gene c
gene d
gene e
gene e
gene g
gene h
Perturbation
Benjamin Haibe-Kains (DFCI/HSPH) R/Bioconductor Course December 15, 2011 5 / 12
Challenges in network inference. . . and how we address them
1 Problem complexity : seed the search for the ”best” network by usingprior biological knowledge about gene interactions
ß Predictive Networks web application
2 Curse of dimensionality : development of a local regression-basednetwork inference to enable analysis of hundreds of genes in parallel
3 Lack of validation: development of performance criteria to assess thequality of network models
4 Lack of software: implementation of network inference methods andrelated tools in R
ß predictionet package
Benjamin Haibe-Kains (DFCI/HSPH) R/Bioconductor Course December 15, 2011 6 / 12
Challenges in network inference. . . and how we address them
1 Problem complexity : seed the search for the ”best” network by usingprior biological knowledge about gene interactions
ß Predictive Networks web application
2 Curse of dimensionality : development of a local regression-basednetwork inference to enable analysis of hundreds of genes in parallel
3 Lack of validation: development of performance criteria to assess thequality of network models
4 Lack of software: implementation of network inference methods andrelated tools in R
ß predictionet package
Benjamin Haibe-Kains (DFCI/HSPH) R/Bioconductor Course December 15, 2011 6 / 12
Challenges in network inference. . . and how we address them
1 Problem complexity : seed the search for the ”best” network by usingprior biological knowledge about gene interactions
ß Predictive Networks web application
2 Curse of dimensionality : development of a local regression-basednetwork inference to enable analysis of hundreds of genes in parallel
3 Lack of validation: development of performance criteria to assess thequality of network models
4 Lack of software: implementation of network inference methods andrelated tools in R
ß predictionet package
Benjamin Haibe-Kains (DFCI/HSPH) R/Bioconductor Course December 15, 2011 6 / 12
Challenges in network inference. . . and how we address them
1 Problem complexity : seed the search for the ”best” network by usingprior biological knowledge about gene interactions
ß Predictive Networks web application
2 Curse of dimensionality : development of a local regression-basednetwork inference to enable analysis of hundreds of genes in parallel
3 Lack of validation: development of performance criteria to assess thequality of network models
4 Lack of software: implementation of network inference methods andrelated tools in R
ß predictionet package
Benjamin Haibe-Kains (DFCI/HSPH) R/Bioconductor Course December 15, 2011 6 / 12