Rich Probabilistic Models for Gene ExpressionEran Segal (Stanford)Ben Taskar (Stanford)Audrey Gasch (Berkeley)Nir Friedman (Hebrew University)Daphne Koller (Stanford)

Our GoalsFind patterns in gene expression data

ExperimentsGenesData OrganizationInducedRepressed

ExperimentsGenesStandard Clustering Organization

Bi-Clustering OrganizationExperimentsGenes

Note: rows and columns no longer correspond to genes andexperimentsDesired OrganizationDetect similarities over subsetsof genes and experiments

Incorporate Heterogeneous DataFind correlations directlyFocus on novel discoveries

Clinical informationExperimental DetailsAnnotations (GO, MIPS, YPD)ACGCCTAOur Approach

LevelGeneExp. clusterExperimentGene ClusterExpressionProbabilistic Relational Models(Koller & Pfeffer 98; Friedman,Getoor,Koller & Pfeffer 99)

LevelGeneExp. clusterExperimentGene ClusterExpressionResulting Bayesian Network

LevelGeneExp. clusterExperimentGene ClusterExpressionProbabilistic Relational Models

LevelGeneExp. clusterExperimentGene ClusterAdding Heterogeneous DataExpression

LevelGeneExpressionGene ClusterLipidHSFEndoplasmaticGCN4Exp. clusterExperimentExp. typeResulting Bayesian Network

LevelGeneExp. clusterExperimentGene ClusterLipidHSFEndoplasmaticGCN4Exp. typeExpressionProblem: Exponential Blowup

Solution: Context SpecificityUltra Violet Light

Solution: Context SpecificityLevelDNA repairUV LightGeneExpressionExperimentUV = NoUV = YesUltra Violet LightDNA repair genes transcribedDNA Damage

Solution: Context SpecificityLevelDNA repairUV LightGeneExpressionExperimentUltra Violet LightDNA repair genes transcribedDNA Damage

Modeling Context SpecificityLevelGeneExp. clusterExperimentGene ClusterLipidHSFEndoplasmaticGCN4Exp. typeExpressionGrouping = a leaf inthe tree

LEARNERLearning the ModelsExperimental DetailsAnnotations (GO, MIPS, YPD)ACGCCTA

Automatic InductionStructure Learning:Dependency structureTree structureMissing Data:Gene cluster & experiment cluster never observed

Learning ProcessLevelGeneExp. clusterExperimentGene ClusterLipidHSFEndoplasmaticGCN4Exp. typeExpression

Learning ProcessGeneExp. clusterExperimentGene ClusterLipidHSFEndoplasmaticGCN4Exp. typeExpressionExperiment SimilarityExp. Cluster = 2Level

Learning ProcessGeneExp. clusterExperimentGene ClusterLipidHSFEndoplasmaticGCN4Exp. typeExpressionGene SimilarityExp. Cluster = 2LevelGene Cluster = Yes

Learning ProcessGeneExp. clusterExperimentGene ClusterLipidHSFEndoplasmaticGCN4Exp. typeExpressionSeparability by binding siteExp. Cluster = 2LevelHSF= Yes. . . . . . Gene Cluster = Yes

Learning ProcessGeneExp. clusterExperimentGene ClusterLipidEndoplasmaticGCN4Exp. typeExpressionAttribute dependencies: induce cluster changesExp. Cluster = 2LevelHSFHSF= Yes. . . . . . Gene Cluster = Yes

Learning ProcessGeneExp. clusterExperimentGene ClusterLipidEndoplasmaticGCN4Exp. typeExpressionExp. Cluster = 2LevelHSFHSF= YesGCN4 = YesGCN4 = Yes. . . . . . . . . . . . . . . Achieved desired clusteringGene Cluster = Yes. . .

Yeast Stress Data (Gasch et al 2001)Measured response to stress cond.92 arraysWe selected ~900 genesAdded data: TRANSFAC, MIPSResults:15 significant TFs7 significant function categories793 Groupings

Context Specific Groupings

Context Specific Groupings

Example Biological FindingDiscovered grouping of 17 genesAll induced in diauxic shiftAll have 2 binding sites for MIG1 transcription factorMany not known to be regulated by MIG1Context-sensitive groupings were key to finding cluster

Compendium Data (Hughes et al 2000)300 samples of yeast deletion mutantsExpressionLevelGene

AClusterGClusterLipidHSFEndoplasmaticGCN4Array/Mutated Gene

Level2,2Level3,2Level1,2Gene Cluster1HSF1Gene Cluster2HSF2HSF3Lipid1Lipid3Level1,1Level3,1Gene 1 mutantGene 3 mutantArray. cluster1Array. cluster3Gene 1Gene 2Gene 3Level3,2Gene Cluster4HSF4Level3,1Level2,1Gene 4Gene Cluster3Resulting Bayesian Network

Experimental SetupGoal: predict the effect of mutating specific genes without performing the experiment (!)

Experimental Setup?Lipid4Array. cluster?Level2,2Level3,2Level1,2Gene Cluster1HSF1Gene Cluster2HSF2HSF3Lipid1Lipid3Level1,1Level3,1Gene 1 mutantGene 3 mutantArray. cluster1Array. cluster3Level3,2Gene Cluster4HSF4Level3,1Level2,1Gene Cluster3Gene 4 mutant

Results

ConclusionsPresented a unified probabilistic framework:Models complex biological domainsExpressive data organizationIncorporates heterogeneous dataFuture directions:Incorporate DNA and protein sequence dataDiscover regulatory networksPaper: http://www.cs.stanford.edu/~eranSoftware (soon): http://dags.stanford.edu/bioContact: eran@cs.stanford.edu

Thank You!