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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. 1. A general class of network inference problem 2. Network inference approach 3. Application: 1. inference of physical interactions: PPI 2. Inference of gene associations: Stem cell genes 3. inference of statistical interactions: Drug/side effect network GMT files 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? A synthetic example 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. Inference live! Information content 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. 𝑝𝑖𝑗 = 1 − 𝑘 2𝛼 1− 𝑛𝑖𝑗𝑘 Compare analytic approximation 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: Application to Infer PPIs Malovannaya A et al. Analysis of the human endogenous coregulator complexome. Cell. 2011 May 27;145(5):787-99 PPI network Validataion • Compare inferred PPI network to the following databases: – BioCarta – HPRD PPIInnateDB – IntAct – KEGG – MINT mammalia – MIPS – BioGrid Comparison Validation Validation 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-seq experiments. – 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-offunction followed by microarray profiling. – 154,170 interactions from 16 ESC-relevant regulatory proteins from loss-of-function studies, and 54 from gainof-function studies. Chip X network Logof network 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. 𝑝𝑖𝑗 = 1 − 1− 𝑘1 2𝛼 𝑛𝑖𝑗 𝑘 1 1− 𝑘2 2𝛽 𝑛𝑖𝑗 𝑘 2 … … … Combination of Chip X and Logof An extension of the approach... 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, ... AERS Record 2 Dug 3, Drug 4, ... … … Side-effect 1, Side-effect 2, ... Side-effect 3, Side effect 4, ... AERS sub network AERS Large-scale Adjacency Matrix And finally… 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 sideeffects based on the AERS database • More details can be found on the website • www.maayanlab.net/S2N