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BFG12
Bioinformatics and Functional GenomicsUsing R and BioconductorCourse Timetable (provisional) |
| BFG12 | Bioinformatics and Functional Genomics |
| Mon, June 18th |
Gene Expression Profiling - Day #1
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| 09:30 - 11:00 |
Brief introduction to R and Bioconductor |
| 11:00 - 11:30 | Coffee Break |
| 11:30 - 12:30 | Introduction to microarrays genome-wide expression data integration and analysis. Gene expression profiling. |
| 12:30 - 14:00 | Lunch Break |
| 14:00 - 16:00 |
Start the analyses: signal calculation and normalization (RMA and other algorithms). Gene-based versus probe-based assignments (use of GATExplorer platform). From genes to proteins: Human Protein Atlas |
| 16:00 - 16:30 | Tea Break |
| 16:30 - 18:00 |
Differential expression. Use different algorithms to calculate robust differential expresssion: SAM, EBAM, altered Expression and Limma. Hierarchical clustering of samples and genes (hclust). Databases that integrate gene expression data and gene signatures: GEO, ArrayExpress, GeneSigDB |
| Tue, June 19th |
Classification and Prediction using genomic data - Day #2
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| 09:30 - 11:00 | Application of machine learning techniques to the analysis of gene expression data to allow classification of different states or different diseases subtypes: use of netClassifier package |
| 11:00 - 11:30 | Coffee Break |
| 11:30 - 12:30 |
Error calculations: sensitivity, specificity, callrate. External validation using independent samples.
Class-prediction using de novo query samples
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| 12:30 - 14:00 | Lunch Break |
| 14:00 - 16:00 | Build gene networks associated to diseases using classification derived parameters: netClassifier, networks assigned to each class that include correlations and interactions |
| 16:00 - 16:30 | Tea Break |
| 16:30 - 18:00 | Build gene networks associated to diseases using classification derived parameters: netClassifier, networks assigned to each class that include correlations and interactions (contd.) |
| Wed, June 20th |
Gene Functional Enrichment - Day #3
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| 09:30 - 11:00 | Gene functional enrichment analysis (EA): definition and types of enrichment; databases with orthogonal biological annotations (GO, KEGG, InterPro) |
| 11:00 - 11:30 | Coffee Break |
| 11:30 - 12:30 | Query gene lists: using tools for statistical functional enrichment based on multiple annotation (DAVID, GeneCodis, GeneTerm-Linker) |
| 12:30 - 14:00 | Lunch Break |
| 14:00 - 16:00 | Application of algorithms that measure outcome and response to assign specific functions to gene expression datasets: globaltest |
| 16:00 - 16:30 | Tea Break |
| 16:30 - 18:00 |
Application of algorithms that measure outcome and response to assign specific functions to gene expression datasets: globaltest (contd.) Final wrap-up session |
| Course Homepage |
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Instituto Gulbenkian de Ciência, Apartado 14, 2781-901 Oeiras, Portugal Last updated: June 15th 2012 |