GS5002 Academic Professional Skills and Techniques:
International Journal Club on Gene Expression Profile Analysis
Instructor: Professor Wong Limsoon / 2011/2012 Semester 2 / IVLE

• Class will be held in Limsoon's office (COM2 #03-57)
• Time will be Thursday 10am-12nn on 2nd, 9th, and 16th of Feb and 1st, 8th, and 15th of March.

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 rectify batch effects (i.e., non-biological variations) [bolstad-2003], how to handle missing values [troyanskaya-2001] and, most importantly, how to identify genes that are meaningful in explaining the difference in disease phenotypes [zhang-2009, venet-2011].

There are three 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, khatri-2005, zeeberg-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 [liu-2007, goeman-2004, pavlidis-2002], which test whether a biological pathway is differentially expressed as a whole. The third group are the network-based analysis methods [sivachenko-2007, sohler-2004, soh-2011], which zoom into a subnetwork of a biological pathway and test whether the subnetwork is differentially expressed. 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 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

• [bolstad-2003] B. M. Bolstad, R. A. Irizarry, M. Astrand, T. P. Speed. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics, 19(2):185-193, 2003.

• [troyanskaya-2001] O. Troyanskaya, M. Cantor, G. Sherlock, P. Brown, et al. Missing value estimation methods for DNA microarrays. Bioinformatics, 17(6):520-525, 2001.

• [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] 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.

• [khatri-2005] P. Khatri, S. Draghici. Ontological analysis of gene expression data: Current tools, limitations, and open problems. Bioinformatics, 21(18):3587-3595, 2005.

• [zeeberg-2003] B. R. Zeeberg, W. Feng, G. Wang, M. D. Wang, et al. GoMiner: A resource for biological interpretation of genomic and proteomic data. Genome Biology, 4(4):R28, 2003.

  Group III: Direct Group Approaches

• [liu-2007, DEA] J. Liu, J. M. Hughes-Oliver, J. A. Menius. Domain-enhanced analysis of microarray data using GO annotations. Bioinformatics, 23(10):1225-1234, 2007.

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

• [goeman-2004] J. J. Goeman, S. A. van de Geer, F. de Kort, H. C. van Houwelingen. A global test for groups of genes: Testing association with a clinical outcome. Bioinformatics, 20(1):93-99, 2004.

  Group IV: Network-Based Approaches

• [sivachenko-2007, NEA] A. Y. Sivachenko, A. Yuryev, N. Daraselia, I. Mazo. Molecular networks in microarray analysis. Journal of Bioinformatics and Computational Biology, 5(2b):429-546, 2007.

• [sohler-1004, ToPNet] F. Sohler, D. Hanisch, R. Zimmer. New methods for joint analysis of biological networks and expression data. Bioinformatics, 20(10):1517-1521, 2004.

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

• [hanczar-2007] B. Hanczar, J. D. Zucker, C. Henegar, L. Saitta. Feature construction from synergic pairs to improve microarray-based classification. Bioinformatics, 23(21):2866-2872, 2007.

• [draghici-2007, Pathway Express] S. Draghici et al. A systems biology approach for pathway level analysis. Genome Research, 17:1537-1545, 2007.

Contact: Limsoon Wong / Last updated 30/11/2011.