Special Topics in Computer Science:
Computational Lab on Microarrays Data Analysis (1PR)
| Course no.: | 365.055 |
| Lecturer: | José Luis Mosquera Mayo |
| Times/locations: | Mon June 21 2010, 12:00-13:30, room BA 9907< Tue June 22 2010, 09:15-12:45, room HF 9904 Wed June 23 2010, 09:15-12:45, room UC 5 Thu June 24 2010, 09:15-11:45, room BA 9912 |
| Mode: | PR, 1h, blocked |
| Registration: | KUSSS |
Slides:
- Lecture2-PreprocessingAndNormalization.odp
- Lecture3-FindingDifferentialExpressedGenes.pdf
- Lecture4_IntroductionToTheFunctionalAnnotation.pdf
Lab Description:
In this lab students will learn concepts to perform an appropriate experimental design of a gene expression experiment using microarrys. Students are going to be introduced to data analysis techniques. They will learn preprocessing, filtering to normalization methods, how to use higherlevel analyses including linear models, clustering methods and annotation tools to study the biological significance, and of course different visualization methods. These techniques will be illustrated in a pratical way using the R statistical environment with BioConductor packages developed for microarray analysis, and some public freeware tools.Lab Goals:
Students will be trained in the theoretical basis of methods usually used for analyzing large datasets of gene expression experiments. These concepts will be put in practice with examples explained in class and reinforced with homework problems. These problems are selected to improve the skills of the students using R/BioConductor. In some cases standalone or web tools will be discussed. In addition to lectures, appropriate research papers will be handed out, and students will be encouraged to critically dissect these articles.Syllabus:
0. Introduction to Microarrays1. R and Bioconductor Project
2. Microarray Data Analysis Process
3. Experimental Design Issues for Microarrays
4. Exploration, Quality Control and Normalization
5. General Concepts on Selection of Differentially Expressed Genes
6. Linear Models for Selecting Differentially Expressed Genes
7. Multiple Testing in Large-scale Gene Expression Experiments
8. Annotation: Relating probesets to Genes
9. Class Discovery Analysis: Searching for Patterns of Common Regulation.
10. Visualization of Microarray Data
11. The Analysis of Biological Significance: Gene Ontology Analysis and Related Methods.