1 Harvard Medical SchoolMassachusetts Institute of Technology Identifying Differentially Expressed Genes in Time Series Microarrays Jonathan J. Smith 1 Hsun-Hsien Chang 2 Marco F. Ramoni 2 1 Department of Mathematics, MIT 2 Division of Health Sciences and Technology, Harvard-MIT New England Statistics Symposium April 17, 2010

2 Harvard Medical SchoolMassachusetts Institute of Technology Background Microarray technologies enable profiling expression of thousands of genes in parallel on a single chip. Comparative analysis of gene expression across tissue states extracts signature genes for disease diagnosis. –Identify differentially expressed genes across tissue states, using t-statistics, fold-change, signal-to-noise ratio, principal component analysis, etc. Research trend: –Microarray technologies are cost down. –Collect times series gene expression microarrays to study biological functions.

3 Harvard Medical SchoolMassachusetts Institute of Technology Approach Challenge: –Existing methods (t-statistics, fold-change, SNR, PCA) cannot be extended to longitudinal expression analysis because temporal information is not well represented. Propose to use the framework of Bayesian networks to capture both the functional and temporal dependencies.

4 Harvard Medical SchoolMassachusetts Institute of Technology Bayesian Networks Bayesian networks are directed acyclic graphs where: –Node corresponds to random variables. –Directed arcs encode conditional probabilities of the target nodes on the source nodes.

5 Harvard Medical SchoolMassachusetts Institute of Technology Representation of Functional Dependence Case 1Case Tissue state 1 Case M Tissue state 2 Phenotypes are modeled by a binomial variable. G Pheno The gene is independent of the phenotypes. Gene G Expression of human subjects is modeled by a log-normal variable. Pheno GG The gene is dependent on the phenotypes.

6 Harvard Medical SchoolMassachusetts Institute of Technology Representation of Temporal Dependence Case 1Case Tissue state 1 Case M Tissue state 2 G(1) G(2) G(T) The time series expression of gene G is considered a 1 st order Markov chain. G(1)G(2)G(T)

7 Harvard Medical SchoolMassachusetts Institute of Technology Differentially Expressed Time Series G(1)G(2)G(T) Pheno The expression series is independent of the phenotypes. G(1)G(2)G(T) Pheno The expression series is dependent on the phenotypes. G(1)G(2)G(T) Pheno Phenotype variable modulates gene expression at every time point.

8 Harvard Medical SchoolMassachusetts Institute of Technology p( | Data ) Identify Function-Dependent Genes p( | Data ) Bayes Factor = p( Data | )

9 Harvard Medical SchoolMassachusetts Institute of Technology Clinical Study on Breast Cancer Breast cancer is the most prevalent cancer in women. Identification of genes inducing breast cancer will help drug development. We used breast cancer microarray data from Gene Expression Omnibus (accession number GSE11352). Our method identified 40 genes that may drive breast cancer development. Biologists confirmed that these genes are involved in cell death, developmental disorder, and endocrine system disorder (all are prerequisites of breast cancer).

10 Harvard Medical SchoolMassachusetts Institute of Technology Conclusion Develop a Bayesian network method for identification of genes in longitudinal expression microarray data. –Functional dependence: genes modulated by phenotypes. –Temporal dependence: gene expression time series modeled by 1 st order Markov chain. –Use Bayes factor to select differentially expressed genes.