1. Integrating Cross-Platform Microarray Data by Second-order Analysis: Functional Annotation and Network Reconstruction Ming-Chih Kao, PhD University of Michigan Medical School mckao@med.umich.edu

2. Xianghong Jasmine Zhou Assistant Professor of Biological Sciences USC Wing Hung Wong Professor of Statistics and of Health Research and Policy Stanford University

3. 2nd-Order Analysis Current Challenges in Microarray Data Analysis 1. How to effectively combine the expression data sets generated with different technology/laboratory platforms? 2. How to identify functionally related genes without co-expression pattern? 3. How to identify transcription cascades?

4. Microarray Platforms 2nd-Order Analysis Multiple Microarray Technology Platforms

5. 2nd-Order Analysis Public Microarray Data Sources ExperimentsDatasets S. cerevisiae78861 C. elegans34815 A. thaliana73644 M. mus1,55320 H. sapiens4,13590

6. Transcription Factor 1 Transcription Factor 3 Transcription Factor 2 gene1 gene2 gene3 gene5 gene4 gene6 gene7 Amplification of signal ? ?

8. Experimental groups exp. correlation First-order correlation Second-order Correlation

9. Chromatin Silencing Amino acid Starvation Gamma Radiation Protein Metabolism DNA Damage Heat Steady Expression of SDA1-CDC5 Expression Correlation POG1-MPT5, SDA1-CDC5 Expression of POG1-MPT5 Experimental groups Regulation of Cell Cycle: POG1-MPT5 and SDA1-CDC5 2nd-Order Analysis An Example

10. Group functionally related genes that may not exhibit similar expression patterns? Data  Stanford Microarray Database (cDNA array)  NCBI GEO Database (Affymetrix array)  Rosetta Compendium (cDNA array) 39 experimental groups subjected to different (types) of perturbations, such as cell cycle, heat shock, osmotic pressure, starvation, zinc, nitrogen depletion, etc. 2nd-Order Analysis Validation

11. 43 functional classes 2,429 genes 5,142 doublets 278,799 Quadruplets Homogenous Quadruplets 84% Heterogeneous Quadruplets 16% 2nd-Order Analysis Validation: Scheme

12. 2nd-Order Analysis Validation: Comparison

13. 2nd-Order Analysis Validation: Results 2 nd -order analysis groups functionally related genes  The derived quadruplets give rise to a set of 2,597 distinct and novel gene pairs  97% of the 2,597 pairs are missed by the standard methods Reasons for the poor performance of the 1 st - order method  Inter-dataset variations  Cross-doublet gene pairs need not show high expression correlation  Sensitivity to gene pairs which are only co- expressed in a subset of the data sets

14. c a b d e f 5 Cell Cycle c a b d e f 5 Heat shock Starvation c a b d e f 5 Nitrogen Depletion c a b d e f 5 c a b d e f 5 Radiation Osmotic pressure c a b d e f 5

15. 2nd-Order Analysis Interaction Modules

17. 2nd-Order Analysis Interaction Modules: Leave-one-out Cross Validation For each gene occurred in the 100 tightest and most stable clusters of known genes, we masked its function and make prediction based on our 2-step procedure, and check the predicted function and its true function. We made predictions for 179 doublets, among which 163 are correct  91% success ratio

18. 2nd-Order Analysis Interaction Modules: Functional Prediction 79 functions of 69 unknown yeast genes involved in diverse biological processes Experimental studies in the literature and in our laboratory  YLR183C in “mitosis” Regulation of G1/S transition  YLL051C in “cation transport” Ferric-chelate reductase activity and iron-regulated expression

19. 2nd-Order Analysis Frequently Occurring Tight Clusters Transcription Factors

20. 2nd-Order Analysis Frequently Occurring TCs with 2nd-Order Correlation

21. Transcription Factors Set 1 Transcription Factor Set 2 Cooperativity

22. 3 types of transcription cascades

23. 2nd-Order Analysis ChIP-Chip

24. 2nd-Order Analysis Transcription Module Results 60 transcription modules identified 34 pairs showed high 2nd-order correlation 29% (P<10 -5 ) of those modules pairs are participants in transcription cascades  2 pairs in Type I cascades  8 pairs in Type II cascades  3 pairs in Type III cascades These transcription cascades inter-connect into a partial cellular regulatory network

26. Avg. Expression Leu3 module vs. Met4 module Avg. Expression Correlation Leu3 module vs. Met4 module 1.0 1.0 2nd-Order Analysis Leu3 and Met4 Transcription Cascade

27. 2nd-Order Analysis Hierarchical clustering of transcriptional modules

28. 2nd-Order Analysis Assigning transcription factor to pathways For an unknown transcription factor in a module cluster, we can annotate its function by integrating 2 types of evidence: the functions of known genes in its target module the functions of known transcription factors regulating other modules in the same cluster

29. 2nd-Order Analysis Summary A framework to integrate many microarray data sets in a platform-independent way, and investigated its properties and applications. Group together functionally- related genes without direct expression similarity Cluster the functional interaction into modules and functional annotation for unknown genes Reveal the cooperativity in the regulatory network and reconstruct transcription cascades