GS5002 Academic Professional Skills and Techniques:
International Journal Club on Gene Expression Profile Analysis
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.
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
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
which test the significance of the intersection of differentially expressed
genes with a biological pathway.
The second group are the direct group analysis methods
which test whether a biological pathway is differentially expressed as a whole.
The third group are the network-based analysis methods
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
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
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.
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.
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
L. Geistlinger et al.
From sets to graphs: Towards a realistic enrichment analysis of
Bioinformatics, 27(13):i366-i373, 2011.
M. Zampieri et al.
A system-level approach for deciphering the transcriptional response to
Bioinformatics, 27(24): 3407-3414, 2011.
L. Chindelevitch et al.
Causal reasoning on biological networks: Interpreting transcriptional
Bioinformatics, 28(8):1114-1121, 2012.
Contact: Limsoon Wong / Last updated 8/1/2014.