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Network inference from repeated observations of node sets

Network inference from repeated observations of node sets. Neil Clark, Avi Ma'ayan. Network Inference. Protein-Protein interaction network. Cell signaling network. Overview. Network inference - the deduction of an underlying network of interactions from indirect data .

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Network inference from repeated observations of node sets

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  1. Network inference from repeated observations of node sets Neil Clark, AviMa'ayan

  2. Network Inference Protein-Protein interaction network Cell signaling network

  3. Overview • Network inference - the deduction of an underlying network of interactions from indirect data. • A general class of network inference problem • Network inference approach • Application: • inference of physical interactions: PPI • Inference of gene associations: Stem cell genes • inference of statistical interactions: Drug/side effect network

  4. GMT files

  5. The inference problem • Input: a set of entities (genes or proteins or ...) in the form of a GMT file - the results of experiments, or sampling more generally. • Assumptions: • 1 An underlying network exists which relates the interactions between the entities in the GMT file • 2 Each line of the GMT file contains information on the connectivity of the underlying network • The problem: Given a GMT file can we extract enough information to resolve the underlying network?

  6. A synthetic example

  7. Approach... • Forget for the moment that we know the underlying network and pretend we only have the GMT file. • Attempt to use the accumulation of our course data to infer the fine details of the underlying network. • Consider the set of all networks that are consistent with our data - there are likely to be many. • Use an algorithm to sample this ensemble of networks randomly. • The mean adjacency matrix gives the probability of each link being present within the ensemble.

  8. Inference live!

  9. Information content

  10. Analytic Approximation • When applying this approach to real data typically there are large numbers of nodes • Sample space of networks can be very large -> computationally demanding • Write a simple analytical approximation which mimics the action of the algorithm.

  11. Compare analytic approximation

  12. Correction for sampling bias • Destroy any information by a random permutation of the GMT file and compare the actual edge weight to the distribution of edge weights from the randomly permuted GMT files:

  13. Application to Infer PPIs MalovannayaA et al. Analysis of the human endogenous coregulatorcomplexome. Cell. 2011 May 27;145(5):787-99

  14. PPI network

  15. Validataion • Compare inferred PPI network to the following databases: • BioCarta • HPRD PPIInnateDB • IntAct • KEGG • MINT mammalia • MIPS • BioGrid

  16. Comparison

  17. Validation

  18. Validation

  19. Application to stem cells • We used two types of high-throughput data from the ESCAPE database (www.maayanlab.net/ESCAPE). • Chip X data: from Chip-Chip and Chip-seqexperiments. • 203,190 protein DNA binding interactions in the proximity of coding regions from 48 ESC-relevant source proteins. • Logof followed by microarray data: A manually compiled database of Protein-mRNA regulatory interactions deriving from loss-of-function gain-of-function followed by microarray profiling. • 154,170 interactions from 16 ESC-relevant regulatory proteins from loss-of-function studies, and 54 from gain-of-function studies.

  20. Chip X network

  21. Logof network

  22. Combining networks • Each data source gives a different perspective on the associations between the genes • New insights may possibly be gained by combining the different perspectives. e.g. small but consistent associations across different perspectives will be revealed by the enhanced signal-to-noise ratio.

  23. Combination of Chip X and Logof

  24. An extension of the approach...

  25. Application II: Inference of Network of statistical relationships in AERS database • Adverse Event Reporting System (AERS) database contains records of .... AERS Record 1 Drug 1, Drug 2, ... Side-effect 1, Side-effect 2, ... AERS Record 2 Dug 3, Drug 4, ... Side-effect 3, Side effect 4, ... ……

  26. AERS sub network

  27. AERS Large-scale Adjacency Matrix

  28. And finally…

  29. Summary • We described a general class of problem in network inference. • A network of physical interactions between proteins is inferred based on high-throughput IP/MS experiments • The method has been applied to examine associations between stem-cell genes from multiple perspectives • We have begun to apply the approach to the inference of statistical interactions between drugs and side-effects based on the AERS database • More details can be found on the website www.maayanlab.net/S2N

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