文档介绍：Inferring works from perturbed expression profiles Dana Pe’er, Aviv Regev, Gal Elidan and Nir Friedman Bioinformatics, Suppl. 1 2001
Motivation
Expression profiles give genome wide information about the state of metabolism, gene regulation, signal transduction, etc.
One would like to infer functional relationships between the genes from this data.
Perturbations such as mutations give insight into the effects of particular genes and help us infer causal relationships.
Tool – works
Random Variables: Gene Expression Levels
Probabilistic Dependencies: Regulatory Interactions
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Goal of Paper
Extend Bayesian framework in cellular context to deal with mutations
Develop better methods to discretize data
Define and learn new features in our model such as mediators, activators, and inhibitors
Construct works of strong statistical significance
works
Network is learned through maximizing a score function with respect to the collected data.
D = Data; G = Graph; Pa = Parent; X = expression level; m = sample number
Equivalent Graphs
Two graphs may imply the same dependencies and are called equivalent.
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So instead of directed graphs we make partially directed graphs.
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Learning with Mutations
If gene X is mutated we replace its expression level by a constant. For example if X is knocked out, its expression is replaced by 0.
Our new score function is:
Where Int(m) is the set of “intervened”(mutated) variables in experiment m.
Notice that two structurally equivalent graph are no longer guaranteed to get the same score. If two graphs get the same score under this scoring function they are called “intervention equivalent.”
Other Perturbations
Temperature sensitivity, ic mutations, and environmental stress can also be model in the work framework.
A node is added for each condition which can take the values “on” or “off.”
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Temperature
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What Do works Buy Us
1) Markov Neighbors (Direct Relationships)