This course will assist people just getting started in Bioconductor. It is designed for people who have some statistical or computational background, but are not experienced bioconductor users. See http://meetings.cshl.edu/courses/c-data06.shtml. INTEGRATED DATA ANALYSIS FOR HIGH THROUGHPUT BIOLOGY June 14 - 27, 2006 Applications due: March 15, 2006 (This deadline will be extended for one week) Instructors: Harmen Bussemaker, Columbia University Vincent Carey, Harvard University Partha Mitra, Cold Spring Harbor Laboratory Mark Reimers, National Cancer Institute Anirvan Sengupta, Rutgers University High-throughput biology, epitomized by the ubiquitous DNA microarray, is rapidly generating enormous observation sets. Biologists seeking to make sense of this growing body of data need to have a firm grasp of statistical methodology. This course is designed to build competence in quantitative methods for the analysis of high-throughput molecular biology data, from which meaningful inferences about biological processes can be drawn. - Review of multivariate statistics - R mini-tutorial - Expression and other microarrays - experimental design, scanning and image analysis, quality control, normalization and probe-level analysis for spotted arrays or prefabricated chips, exploratory analysis, tests of significance and multiple testing, using R and Bioconductor - Discrimination and classification of samples - Identifying general regulation themes (e.g. Gene Ontology categories) in gene lists by statistical means - Protein identification and quantification using Mass Spectrometry - Promoter analysis in yeast using CHIP and expression data - Identifying regulatory polymorphism using SNP and expression data - Characterizing the effect of DNA amplifications and deletions on gene expression in cancer using CGH and expression data on the same samples Confirmed speakers include Rick Young (MIT: yeast and CHIP); Audrey Gasch (Wisconsin: yeast microarrays); Bruce Futcher (SUNY: microarray techniques); Terry Speed (Berkeley: proteomics); Rafa Irizarry (Johns Hopkins: Affymetrix arrays); Keith Baggerly (Texas; proteomics) The first week of the course will concentrate on analysis of specific types of microarray data (expression, Affymetrix, CGH, CHIP-chip, and SNP arrays), and proteomics. The second week will explore biological problems involving the integration of several types of high-throughput data. Data sets will be drawn from yeast, human polymorphisms, and cancer biology. This course is supported with funds provided by the National Cancer Institute Mark Reimers, Biostatistician, National Cancer Inst., 9000 Rockville Pike, bldg 37, room 5068 Bethesda MD 20892