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
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: 1An underlying network exists which relates the interactions between the entities in the GMT file 2Each 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.
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.
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 May 27;145(5):787-99
Validataion Compare inferred PPI network to the following databases: – BioCarta – HPRD PPIInnateDB – IntAct – KEGG – MINT mammalia – MIPS – BioGrid
Application to stem cells We used two types of high-throughput data from the ESCAPE database (www.maayanlab.net/ESCAPE).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-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.
Chip X 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.
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 1Drug 1, Drug 2,...Side-effect 1, Side-effect 2,... AERS Record 2Dug 3, Drug 4,...Side-effect 3, Side effect 4,...…
AERS sub network
AERS Large-scale Adjacency Matrix
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