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Statistical methods and tools for integrative analysis of perturbation signatures Mario Medvedovic Laboratory for Statistical Genomics and Systems Biology.

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Presentation on theme: "Statistical methods and tools for integrative analysis of perturbation signatures Mario Medvedovic Laboratory for Statistical Genomics and Systems Biology."— Presentation transcript:

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 Center

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” Mario Medvedovic, Environmental Health, University of Cincinnati  Generalized Random Set (GRS) analysis (Freudenberg et al., Bioinformatics 27: 70, 2011)

5 Concordances in perturbations signatures (eg gene expression) Mario Medvedovic, Environmental Health, University of Cincinnati  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

6 Concordances in perturbations signatures (eg gene expression) Mario Medvedovic, Environmental Health, University of Cincinnati  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

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 Mario Medvedovic, Environmental Health, University of Cincinnati Signal transducers TF 1 TF n Primary targets of the perturbation Transcription regulation Transcriptional response Biochemical response data Drug-target interaction data Change in gene expression Public domain ChIP-seq Public domain transcriptional response to perturbations Regulatory activity scores for all nodes + Random Network Walk Model = Integrated Regulatory Network Activity Signature in response to a perturbation Network Meta-signatures Active sub- networks Known pathways Library of Regulatory Network models and signatures

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 Literature concepts Genomics Portals Genomics Data Gene Ontology KEGG pathways Functional Knowledge Base Analytical Tools (R and Bioconductor) Epigenomics Events Transcription Factor Binding Gene Expression CGH Interaction networks Transcriptional Modules Statistical Analysis Machine Learning CpG Islands microRNA Expression New functional knowledge New physiological understanding New testable hypotheses Genomics Portals Mario Medvedovic, Environmental Health, University of Cincinnati Visualization LINCS Readouts of Cellular States (primary data) 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

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

12 Out team (http://BayesianGenomics.org )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


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