Stefano Monti. Workshop Format

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1 Gad Getz Stefano Monti Michael Reich Broad Institute of MIT & Harvard October 18-20, 2006 Cambridge, MA Workshop Format Morning lectures: Principles of statistics, machine learning and pattern recognition. Their application to the analysis of gene expression data. Afternoon hands-on on s: Practice sessions w/ GenePattern. Application of the concepts presented in the lectures. 1

2 User Profile Knowledge of basic mathematical concepts assumed (square root, log, function, ) No (or little) previous analysis experience required. Basic familiarity with microarrays and expression analysis terms. Mixed audience: nobody satisfied. Outline of the course Lectures Day 1: Introduction: Functional Genomics GenePattern mini-tutorial FG Pipeline: Data Acquisition Preprocessing & Visualization Day 2: Supervised Analysis Differential analysis/gsea Class Prediction/Classification Validation Day 3: [Survival analysis] Unsupervised Analysis Clustering, Bi-clustering Annotation Hands-on on s Day 1: Preprocessing Data visualization/dimensionality reduction: HeatMaps PCA,NMF,MDS Day 2: Differential Analysis/Annotation Classification: Model building/selection Evaluation Day 3: Day 3: Clustering: HC, NMF, CC, Bi-clustering GO Annotation Final Project 2

3 The use of high-throughput gene expression micro-arrays and computational tools for molecular profiling. Functional genomics definition The use of systematic approaches to answer questions for the majority of genes in a genome, including when is a gene expressed? with which other genes does it interact? what phenotype results if a gene is switched-on/-off/mutated? Functional genomics aspires to answer such questions systematically for all genes in a genome in contrast to conventional approaches that do so for one gene at a time. 3

4 Paradigm for Functional Genomics Biological States/ Phenotypes Tumor vs. Normal Chemical treatment vs. untreated Remission vs. Refractory Disease Successful vs. unsuccessful Treatment i Time courses Readouts Polymorphism Mutation Loss of Heterozigosity Expression Levels Analysis and Understanding Protein Relative abundance Modification Activity Statistical What pathways inference are affected by a disease? Statistics Classification/Prediction Machine Learning What pathways are modulated by a specific drug? Clustering Pattern Recognition What signatures predict tumor type or patient Feature outcome? extraction / projection Pattern discovery What genes confer susceptibility to disease? Network extraction High-throughput assays technologies Polymorphism Copy number variation Loss of Heterozigosity SNP arrays CGH arrays sequencing Expression levels Microarrays SAGE Protein Relative abundance Modification Activity Mass Spectrometry ChIP2chip 4

5 High-throughput assays technologies Polymorphism Copy number variation Loss of Heterozigosity SNP arrays CGH arrays Expression levels Microarrays SAGE Protein Relative abundance Modification Activity Mass Spectrometry ChIP2chip m micro-array Measures the gene activity of 10K of genes at once Read out organized in a high-dimensional numerical matrix Samples Genes Transcription translation Traits Diseases Proteins Physiology m Metabolism Drug Resistance Computational analysis 5

6 Number of articles PubMed query: microarray in title/abstract No. of articles Year Extrapolated from first 5 months The functional genomics pipeline Experimental design affects outcome data analysis Data acquisition microarray processing Data preprocessing scaling/normalization/filtering Data analysis/hypothesis generation Supervised Analysis Differential analysis, Classification, Unsupervised Analysis Clustering, Bi-clustering, Enrichment analysis GO annotation, GSEA, Validation/Annotation In silico testing Cross validation, train/test, etc, In vitro testing Back to the lab 6

7 Introductory references 1. Hastie, T, Tibshirani R, and Friedman J. The Elements of Statistical Learning. Springer-Verlag, Nature Genetics supplements: The Chipping Forecast I [Nat Genet, 21(1s), 1999], II [Nat Genet, 32(4s), 2002], III [Nat Genet, 37(6s), 2005]. 3. Allison, D. B., Cui, X., Page, G. P., and Sabripour, M. Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet, 7: 55-65, Hoffman, E. P., Awad, T., et al. Expression Profiling - Best Practices for Data Generation and Interpretation in Clinical Trials. Nat Rev Genet, 5: , Larkin, J. E., Frank, B. C., et al. Independence and reproducibility across microarray platforms. Nat Meth, 2: , Irizarry, R. A., Warren, D., et al. Multiple-laboratory comparison of microarray platforms. Nat Meth, 2: ,

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