ABS04 - 2004 Applied Bayesian Statistics School
STATISTICS & GENE EXPRESSION GENOMICS:
METHODS AND COMPUTATIONSCentro Congressi Panorama, Trento, Italy
15 - 19 June, 2004
********************************** June 15th - Lecture 1: Overview ********************************** Genome-scale data and biological investigations Examples of molecular phenotyping in areas such as cancer Statistical principles and basics Molecular biology principles and basics DNA microarray basics and technologies Statistics resources for DNA microarray data Basic data issues: management, imaging, normalisation, other PRACTICAL SESSION Basics of managing, manipulating and exploring gene expression data sets in R and Matlab Web resources for gene and genomic information, in particular Bioconductor resources for R Examples: various data sets, with major focus on Affymetrix microarrays ******************************************************************************* June 16th - Lecture 2: Aspects of oligonucleotide array data processing and summary ******************************************************************************* Expression intensity models Aspects of exploratory data analysis with examples selected from studies in cancer phenotyping, cell cycle studies, designed experiments in mouse models, cardiovascular disease genomics and other Exploratory uses of regression - collinearities among genes Data reduction - gene subset selection and screening principal components/singular factors Interactive illustrations and examples (Matlab) Images, clustering PRACTICAL SESSION Exploratory data analysis using example data sets (Matlab, R with Bioconductor) Normalisation, screening and computing expression intensity estimates Linking analysis results to gene ontologies and biological literatures Clustering examples and tools (R, xcluster, cluster/treeview) ******************************************************************************* June 17th - Lecture 3: Regression modelling with many predictors ******************************************************************************* Gene expression as covariate information Bayesian analysis of linear and binary principal component/factor regression Some theory of Bayesian actor models and related topics Relevance of shrinkage priors - generalised g-priors Phenotyping examples in breast cancer, cell line and animal studies Trans-species molecular phenotyping: cancer signatures Case control vs prospective studies Metagenes and data summaries for predictive modelling ******************************************************************************* June 18th - Lecture 4: Bayesian predictive regression tree models ******************************************************************************* Binary response prediction using Bayesian tree models for retrospective sampling Examples in cardiovascular disease prediction, breast cancer risk prediction More general statistical tree models: survival analysis using classification trees Use of metagene summaries in predictive models Examples of recurrence prediction Clinico-genomic models Exploration of binary tree software (Matlab) PRACTICAL SESSION Fitting regression models in Matlab and R Exploring gene subset predictor selection Bayesian factor/binary regression explorations More on Bioconductor tools for data processing and expression intensity estimation ******************************************************************************* June 19th - Lecture 5: Graphical models for gene expression data representation, visualisation and exploration ******************************************************************************* Theory of graphical models and compositional networks Ideas of model fitting using directed graphs Bayesian analysis and theory - prior specification, posterior computation Large-scale regression model search - "Shotgun Stochastic Search" and its uses in exploring and evaluating large-scale graphical models Examples from breast cancer, cell growth and signalling gene pathways, and other areas Illustrations of software (GraphExplore) for exploring and manipulating graphs