module networks sushmita roy bmi/cs 576 [email protected] nov 18 th & 20th, 2014
TRANSCRIPT
RECAP
• Probabilistic graphical models (PGMs) provide a natural representation of molecular networks
• Learning PGMs from data requires us to solve two problems – Parameter estimation given a graph structure– Structure learning which requires us to learn both structure and parameters
• Bayesian networks are a type of PGMs popular for representing molecular networks
• Learning Bayesian networks from gene expression data requires additional considerations– Sparse candidate algorithm: heuristics to reduce the number of candidate
parents– Bootstrap to assess model confidence– Module networks
What you should know
• Motivation of module networks• What is a module network?• Compare and contrast module network
learning algorithm to other network inference algorithms– E.g. Sparse candidate
Module Networks
• Motivation: – Most complex systems have too many variables– Not enough data to robustly learn networks– Large networks are hard to interpret
• Key idea: Group similarly behaving variables into “modules” and learn the same parents and parameters for each module
• Relevance to gene regulatory networks– Genes that are co-expressed are likely regulated in
similar ways
Segal et al 2005
Definition of a module
• Statistical definition (specific to module networks by Segal 2005)– A set of random variables that share a statistical
model• Biological definition of a module– Set of genes that are co-expressed and co-
regulated
An expression module
Set of genes that behave similarly across conditions
Modules
Gasch & Eisen, 2002
Genes
Genes
Genes
Key questions of Module Networks
• How to represent the Conditional Probability Distributions (CPD) for children?– Regression Tree
• How to learn module networks?
Defining a Module Network
• A probabilistic graphical model over N random variables
• Set of module variables M1.. MK
• Module assignments A that specifies the module (1-to-K) for each Xi
• CPD per module P(Mj|PaMj), PaMj are parents of module Mj
– Each variable Xi in Mj has the same conditional distribution
Bayesian network vs Module network
Each variable takes three values: UP, DOWN, SAME
Bayesian network vs Module network
• Bayesian network– Different CPD per random variable– Learning only requires to search for parents
• Module network– CPD per module• Same CPD for all random variables in the same module
– Learning requires parent search and module membership assignment
Learning a Module Network
• Given training dataset , fixed number of modules (K)
• Learn– Module assignments A of each variable to a
module – The parents of each module
Score of a Module networkModule network
Data
K: number of modules, Xj : jth module PaMj Parents of module Mj
Likelihood of module j
Module network learning algorithm
Module assignment search
• Happens in two places• Module initialization– Interpret as clustering of the random variables
• Module re-assignment
Module initialization as clustering of variables
for module network
Module re-assignment
• Must preserve the acyclic graph structure• Must improve score• Module re-assignment happens using a
sequential update procedure:– Update only one variable at a time– The change in score of moving a variable from one
module to another while keeping the other variables fixed
Module re-assignment via sequential update
Representing the Conditional probability distribution
• Xi are continuous variables
• How to represent the distribution of Xi given the state of its parents?
• How to capture context-specific dependencies?
• Module networks use a regression tree
Modeling the relationship between regulators and targets
• suppose we have a set of (8) genes that all have in their upstream regions the same activator/repressor binding sites
A regression tree
• A rooted binary tree T• Each node in the tree is either an interior node
or a leaf node• Interior nodes are labeled with a binary test
Xi<u, u is a real number observed in the data• Leaf nodes are associated with univariate
distributions of the child
A regression tree to capture a CPD
X1 > e1
X2 > e2
YES
NO
NO YESLeaf
Interior node
X3
X1 X2
Expression of gene represented by X3 modeled using Gaussians at each leaf node
e1, e2 are values seen in the data
An example regression tree for a Module network
A very simple regression tree
X2
X3
e1 e2
X2 > e1
X2 > e2
YESNO
NO YESX3
Algorithm for growing a regression tree
• Input: dataset D, child variable Xi, candidate parents Ci of Xi
• Output: Tree T• Initialize T to a leaf node, estimated from all
samples of Xi
• While not converged– For every leaf node l in T
• Find with the best split at l• If split improves score
– add two leaf nodes, i and j below l– Update samples and parameters associated with , i and j
Learning a regression tree• Assume we are searching for the parents of a variable X3 and it
already has two parents X1 and X2
• X4 will be considered using “split” operations of existing leaf nodes
X1 > e1
X2 > e2
YES
NO
NO YES
X4 > e3
NO YES
X1 > e1
X2 > e2
YES
NO
NO YES
N1 N2N3
Nl: Gaussian associated with leaf l
N2N3
N4N5
Convergence in regression tree
• Depth of tree• Improvement in score• Maximum number of parents• Minimum number of samples per leaf node
Assessing the value of using Module Networks
• Using simulated data– Generate data from a known module network – Known module network was in turn learned from real data
• 10 modules, 500 variables
– Evaluate using• Test data likelihood• Recovery of true parent-child relationships are recovered in learned
module network
• Using gene expression data– External validation of modules (Gene ontology, motif
enrichment)– Cross-check with literature
Test data likelihood
Each line type represents size of training data
10 Modules is the best for almost all training data set sizes
Recovery of graph structure
Application of Module networks to yeast expression data
Segal et al, Regev, Pe’er, Gasch 2005
Module networks has better performance than simple Bayesian network
Gain in test data likelihood over Bayesian network using expression data
The Respiration and Carbon Module
Regulation tree
Global View of Modules
• modules for common processes often share common– regulators– binding site motifs
Summary
• Module networks– A type of Bayesian network– Identifies modules (sets of similarly behaving random
variables) and learns parents for each module– Conditional probability distributions capture “rules” of
regulatory relationships– Learning requires inferring parent->module
relationships and module assignments• In practice give more realistic networks compared
to Bayesian networks