Wrocław Information Technology Initiative (WITI)
Computational and Statistical Analysis of Biomolecular Networks
May 25-28, 2009
Instructors
Olga Vitek, Departments of Statistics and Computer Science, Purdue University
Robert Gevers, Department of Computer Science, Purdue University
Course information
WITI home: here
Syllabus: here
Software: R, Bioconductor and Cytoscape
Texts: Hahne, Huber, Gentleman, Falcon. Bioconductor Case Studies. Springer, 2008.
Gentleman. R Programing for Bioinformatics. CRC Press, 2008.
Computer access: For computer access during lab sessions, contact
Magdalena Malina magdalena.malina@gmail.com
Day 1: Monday, May 25
Scientific questions, technologies, sources of data and tools.
Lecture: 9:15-11:00, 3.01 C13. Lecture notes.
References: Systems and systems biology: 1, 2 and 3.
Protein interaction networks: 1, 2.
R and Bioconductor. Data structures and libraries in R. Network visualization with Cytoscape.
Lab: 15:15-17:00, 317.2 D1. Handout.
References: Reference to the dataset used in the lab; Cytoscape tutorial.
Day 2: Tuesday, May 26
Graphs and graph algorithms for network representation and analysis.
Lecture: 9:15-11:00, 3.06 C13. Lecture notes.
References: Network organization, MCL, cluster evaluation.
Exploratory analysis, simulation and unsupervised clustering. Biological interpretation.
Lab: 13:15-15:00, 317.4 D1. Handout.
Datasets: Cytoscape datasets 1 and 2. R datasets 1, 2, 3, 4 and 5. R code.
Day 3: Wednesday, May 27
Functional annotations, and their role in network analysis and validation.
Lecture: 9:15-11:00, 4.01 C13. Lecture notes.
References: Network based prediction of function. Semantic similarity.
Gene Ontology. Semantic similarity. Hypergeometric testing. Biological interpretation.
Lab: 11:15-13:00, 317.3 D1. Handout.
References: GO. R datasets 1 and 2. Cytoscape dataset. R code.
Day 4: Thursday, May 28
Integration of network structures with quantitative experimental data.
Lecture: 9:15-11:00, 3.06 C13. Lecture notes.
References: Network-based disease classification. GSEA.
Gene set enrichment analysis. Multiple comparisons. Role of gene sets for class discovery and class prediction.
Lab: 11:15-13:00, 317.3 D1
Datasets: Cytoscape file.
Tentative schedule and handouts
