[BioC] High-Throughput Data Analysis Course Announcement: Cold Spring Harbor Labs - June 14-27

[Reimers, Mark (NIH/NCI) [E]]

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