Statistical methods and tools for integrative analysis of perturbation signatures

1. Statistical methods and tools for integrative analysis of perturbation signatures Mario Medvedovic Laboratory for Statistical Genomics and Systems Biology Department of Environmental Health University of Cincinnati Medical Centerhttp://GenomicsPortals.org

2. Aims of the project • Methods for characterizing concordances in perturbation signatures and constructing meta-signatures • Explaining LINCS signatures and meta-signatures by constructing regulatory network models • Use of LINCS signatures and models to explain disease-related signatures • On- and off-line computational infrastructure Mario Medvedovic, Environmental Health, University of Cincinnati

3. Concordances in perturbations signatures (eg gene expression) • Given two differential expression signatures, are the genes differentially expressed in both signatures more common than expected by chance? • What are the genes with “unusually” high similarities in differential expression? • Currently used statistical methods for addressing these questions are inadequate. Mario Medvedovic, Environmental Health, University of Cincinnati

4. Concordances in perturbations signatures (eg gene expression) • “Meta-signature” • Generalized Random Set (GRS) analysis (Freudenberg et al., Bioinformatics 27: 70, 2011) Mario Medvedovic, Environmental Health, University of Cincinnati

5. Concordances in perturbations signatures (eg gene expression) • GRS works significantly better than alternatives • “Meta-signatures” of two “concordant” signatures are more functionally coherent • “Meta-signatures” accentuate common features of two (possibly) different regulatory programs Mario Medvedovic, Environmental Health, University of Cincinnati

6. Concordances in perturbations signatures (eg gene expression) • Extend the methodology to a group of signature • Form groups of concordant signatures and associated “meta-signatures” for different types of readouts • Integration across different perturbations Mario Medvedovic, Environmental Health, University of Cincinnati

7. Regulatory network models of signatures and meta-signatures • Integrated Perturbation Signature and Meta-Signatures • Integration across different types of readouts • Gene-level scores assessing the likelihood that the genes’ activity readout is affected by one or a set of perturbations • Correlating with existing pathways • De-novo regulatory network constructions by integrating with the global protein-protein protein-gene interaction networks Mario Medvedovic, Environmental Health, University of Cincinnati

8. Network models of LINCS signatures and meta-signaturs Primary targets of the perturbation Drug-target interaction data Integrated Regulatory Network Activity Signature in response to a perturbation Regulatory activity scores for all nodes Random Network Walk Model = + Signal transducers Biochemical response data Public domain ChIP-seq Network Meta-signatures Active sub-networks Known pathways Transcriptionregulation TF1 TFn Transcriptional response Public domain transcriptional response to perturbations Library of Regulatory Network models and signatures Change in gene expression Mario Medvedovic, Environmental Health, University of Cincinnati

9. Using LINCS signatures and models to explain disease-relate signatures • Correlate the disease-related readouts (eg gene expression profile) with corresponding LINCS signatures and meta-signatures • Associate LINCS models and complementary types of readouts with the disease • Construct disease-specific regulatory model • Associate LINCS phenotypic readouts (eg images, proliferation, apoptosis) with the disease Mario Medvedovic, Environmental Health, University of Cincinnati

10. Genomics Portalshttp://GenomicsPortals.org TranscriptionFactor Binding EpigenomicsEvents Gene Expression microRNAExpression StatisticalAnalysis MachineLearning CpGIslands • Signature comparison (GRS) • Network models • Interactive network visualization • LINCS signatures and meta-signatures • Public domain signatures and meta-signatures LINCS network models and network based signatures CGH LINCS Readouts of Cellular States (primary data) TranscriptionalModules Gene Ontology Interactionnetworks Literature concepts KEGG pathways Analytical Tools(R and Bioconductor) • New functional knowledge • New physiological understanding • New testable hypotheses Genomics Data GenomicsPortals Functional Knowledge Base Visualization Mario Medvedovic, Environmental Health, University of Cincinnati

11. Integration with other projects • Raw data • Meta data • Methods for deriving summaries and scores • Signatures, networks itNETZ: Integrative and Translational Network-based Cellular Signature Analyzer (PI: Zhou) • Analytical synergies • Methods • Tools • Data dumps • Direct db queries • Web access to analysis engines A Systems Approach to Elucidate Mechanisms of Drug Activity and Sensitivity (PI: Califano) • Integrated signatures and models • Meta-signatures • Disease-related signatures • Regulatory event signatures • Analysis engines for comparisons against signature, meta-signatures and networks • (PI:Schurer) Mario Medvedovic, Environmental Health, University of Cincinnati

12. Out team(http://BayesianGenomics.org ) The Team: PI: Mario Medvedovic, Bioinformatician, Assoc Professor, Department of Environmental Health Co-I: Siva Sivaganesan, Statistician, Professor in Dept of Mathematics Co-I: John Reinchard, Molecular Biologist, Research Scientist in Dept of Environmental Health Mukta Phatak, PhD Bioinformatician, Res Associate in SGSB Lab Jing Chen, PhD Bioinformatician, Res Associate in SGSB Lab Wen Niu, MS in CS and Mol Biol, Application Specialist in SGSB Lab Mario Medvedovic, Environmental Health, University of Cincinnati