GS5002 Academic Professional Skills and Techniques:
International Journal Club on Gene Expression Profile Analysis
and CS6101 Exploration of CS Research
Instructor: Professor Wong Limsoon / 2013/2014 Semester 2

• Class will generally be held in Executive Classroom (COM2-04-02).
• Time will generally be Thursday 9am-12nn on 12 (this is a Wednesday) & 20 March, and 3, 10, & 17 April.

Course Description

The possibility of using gene expression profiling by microarrays for diagnostic and prognostic purposes has generated much excitement and research in the last ten years. Nevertheless, a number of issues persist such as how to identify genes that are meaningful in explaining the difference in disease phenotypes [zhang-2009, venet-2011].

There are four main groups of approaches, that make use of biological pathways (e.g., enzymatic pathways, gene regulatory pathways, and protein interaction networks), for improving gene selection and for transitioning from the selected genes to the understanding of the sequences of causative molecular events. The first group are the overlap analysis methods [doniger-2003, which test the significance of the intersection of differentially expressed genes with a biological pathway. The second group are the direct group analysis methods [pavlidis-2002, subramanian-2005], which test whether a biological pathway is differentially expressed as a whole. The third group are the network-based analysis methods [soh-2011, haynes-2013, lim-2014] which zoom into a subnetwork of a biological pathway and test whether the subnetwork is differentially expressed. Thr fourth and latest group are based on more detailed logical and/or dynamic models of biological pathways [geistlinger-2011, zampieri-2011, chindelevitch-2012]. All of these approaches have their basis on the fact that every disease phenotype has some underlying biological causes. Therefore, it is reasonable to analyse the gene expression profiles of disease phenotype with respect to the biological contexts provided by biological pathways and protein interaction networks.

In this "journal club", we will read these (and possibly other related papers) to gain an appreciation of how biological networks can enhance gene expression profile analysis. Each student will be asked to pick and present one of these (or other relevant papers of his choice). Each student will be graded by all fellow students according to:

• The quality of ppt (readability, organization, attractiveness).
• The quality of presentation (organization, delivery, how well he makes biologists understand the material).
• The level of understanding of what he is presenting (in particular, Q&A).
• The level of participation in discussion.

Reading List (To be further refined)

Group I: Issues in Microarray Analysis

• [zhang-2009] M. Zhang, L. Zhang, J. Zou, C. Yao, et al. Evaluating reproducibility of differential expression discoveries in microarray studies by considering correlated molecular changes. Bioinformatics, 25(13):1662-1668, 2009.

• [venet-plos2011] D. Venet, J. E. Dumont, V. Detours. Most random gene expression signatures are significantly associated with breast cancer outcome. PLoS Computational Biology, 7(10):e1002240, 2011.

  Group II: Overlap-Based Approaches

• [doniger-2003, MAPPFinder] S. W. Doniger, N. Salomonis, K. D. Dahlquist, K. Vranizan, et al. MAPPFinder: Using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biology, 4(1):R7, 2003.

  Group III: Direct Group Approaches

• [pavlidis-2002, FCS] P. Pavlidis, D. P. Lewis, W. S. Noble. Exploring gene expression data with class scores. Proc. Pacific Symp Biocomput, 7:474-485, 2002.

• [subramanian-2005,GSEA] A. Subramanian, P. Tamayo, V. K. Mootha, S. Mukherjee, et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Nat. Acad. Sci. USA, 102(43):15545-15550, 2005.

  Group IV: Network-Based Approaches

• [soh-2011, SNet] D. Soh, D. Dong, Y. Guo, L. Wong. Finding consistent disease subnetworks across microarray datasets. BMC Bioinformatics, 12(Suppl. 13):S15, 2011.

• [haynes-2013, DEAP] W. A. Haynes, et al. Differential expression analysis for pathways. PLoS Comput Biol, 9(3):e1002967, 2013.

• [lim-2014, PFSNet] K. Lim, L. Wong. Finding consistent disease subnetworks using PFSNet. Bioinformatics, in press, 2014.

  Group V: Model-Based Approaches

• [geistlinger-2011] L. Geistlinger et al. From sets to graphs: Towards a realistic enrichment analysis of transcriptomic systems. Bioinformatics, 27(13):i366-i373, 2011.

• [zampieri-2011] M. Zampieri et al. A system-level approach for deciphering the transcriptional response to prion infection. Bioinformatics, 27(24): 3407-3414, 2011.

• [chindelevitch-2012] L. Chindelevitch et al. Causal reasoning on biological networks: Interpreting transcriptional changes. Bioinformatics, 28(8):1114-1121, 2012.

Contact: Limsoon Wong / Last updated 8/1/2014.