1. Case Study: Characterizing Diseased States from Expression/Regulation Data Tuck et al., BMC Bioinformatics, 2006.

2. Background ● How do we classify processes/expression related to disease/phenotype (separating signal/data)? ● How do we use all of the data available to us – sequences, expression, regulation? ● Present case study of acute leukemia and breast cancer (normal vs. diseased cells).

3. Summary of Contributions ● Constructing sample-specific regulatory networks. ● Identify links between transcription factors and regulated genes that differentiate healthy states from diseased states. ● Generalize to simultaneous changes in functionality of multiple regulatory links, pointing to a regulatory gene / emanating from one TF.

4. Summary of Contributions ● Examine distances in transcriptional networks for subsets of genes that characterize diseased state. ● Observation that genes that optimally classify samples are concentrated in neighborhoods. ● Genes that are deregulated in diseased sttes exhibit high connectivity. ● TF-regulated gene links and centrality of genes can be used to characterize diseased cells.

5. Background ● Current work largely focuses on identification of individual differentially expressed genes, or co- regulated gene sets. ● There is significant work on module identification (graph models, SVD, connected components, etc.) ● There is work on expression patterns of genes that can classify tumor types. ● There is some work on transcription networks prior to this work as well [TRANSFAC/CREME]

6. Constructing Disease Cell Networks ● Intersect connectivity network representing TF binding to gene promoter regions, with co-expression networks representing TF target gene co-expression. ● Use TRANSFAC to relate known TF binding sites to promoter regions of genes and known TF-target gene interactions. ● For data derived from each microarray (Sample or patient), construct a co-expression network such that each TF-gene pair is assigned +1 or -1 based on up/down co-regulation.

7. Constructing Disease Cell Networks ● Intersection of connectivity and individual co- expression networks gives condition specific (CS) regulatory networks. ● CS networks derived from 6 gene expression studies using 3 types of datasets – normal cell lineages, tumor vs. normal tissues, and disease specific tumors associated with variable climical outcomes. ● 4821 genes and 196 Tfs on early Affy arrays and 13363 genes and 233 Tfs on newer arrays.

8. Constructing Disease Cell Networks

10. Classifying based on network features. ● Assume that each disease sample has a distinct regulatory network (pattern of activated links that gives rise to its expression profile). ● Examine how different aspects of network structure characterize different phenotypes.

11. Classifying based on network features. Link Based Approach ● Examine differences between patient samples by analyzing activity status of regulatory links ● Construct networks unique to patients ● Yields complete discriminatory networks.

12. Classifying based on network features. Degree Based Approach ● “Centrality” of individual genes in networks ● Degree – number of TFs activating or suppressing a particular gene (in degree), or number of genes regulated by a single TF (out degree). ● Use genome wide degree profile – identifying nodes with largest changes in centrality (rewiring) will assist is in detecting hotspots.

13. Classifying based on network features. Sample Classification ● Create regulatory networks for every sample and apply a classifier.  Rank features to identify set of TF-gene links  Use training sets to identify features and rank links, genes, and degree of nodes that undergo most substantial changes ● Acute lymphoblastic leukemia vs. acute mueloid leukemia ● Two different myeloid leukemia types ● Different matched cell types (renal-cell carcinoma vs. normal)

14. Classifying based on network features. Sample Classification ● Create regulatory networks for every sample and apply a classifier.  Rank features to identify set of TF-gene links  Use training sets to identify features and rank links, genes, and degree of nodes that undergo most substantial changes ● Acute lymphoblastic leukemia vs. acute mueloid leukemia ● Two different myeloid leukemia types ● Different matched cell types (renal-cell carcinoma vs. normal)

15. Classifying based on network features. Sample Classification ● Pass top links to train a basic classifier ● Cross validate.

16. Classifying based on network features.

23. Classification Techniques

26. Classification results