Structure Learning for Inferring a Biological Pathway Charles Vaske Stuart Lab.

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Structure Learning for Inferring a Biological Pathway Charles Vaske Stuart Lab

Biological Pathways Cell is a dynamical system Somewhat modularized (into pathways) Given pathway elements, how do they communicate? –Protein modification –Gene expression changes

Input Set of genes essential to phenotype RNAi perturbation - gene knockdown Expression measurement S Genes - essential to phenotype - each is individually perturbed E Genes - affected by S Genes - expression is measured

Input E Genes S genes & controls

Desired Output: Structure Probabilistic Model Binary Variable Domain Restricted factor form –Deterministic signalling –Shared measurement error rates Markowetz, et al. 2005

Model Averaging Maximum Likelihood estimate might not be interesting Gain a posterior on particular model features

First Attempt: All linear models Calculate likelihood of data under each model Find posterior of individual edges Tiered Sgenes (matches initial discovery method)

Link Robustness Noise level estimated from replicate spots Added noise to data, reran experiment 10 times

Link Significance

Experimentally verified

Scaling to More Sgenes All linear permutations is a hack MCMC structure sampling?