This year the program will focus on Microarray Gene Expression Data Analysis. The program leaders are Shili Lin and Joseph Verducci. The first week is spent in a tutorial, which combines morning lectures with active learning laboratories in the afternoon. The following two weeks are spent working on guided team projects and participating in a mini-conference to share project results. The program is meant primarily for graduate students; college instructors and qualified undergraduates will also be considered.
The 2005 Summer Program dates are August 1 - 19.
|Monday, August 1|
|9:00-12:00pm||Introductory statistics - PPT|
|2:00-4:00pm||Introduction to genetics, molecular biology, microarray technology, and epigenetics (GMBME) - PDF|
|Tuesday, August 2|
|9:00-11:00am||Image analysis and normalization - PDF|
|1:00-3:00pm||Introduction to Bioconductor and R - PPT|
|3:30-5:00pm||Lab visit||Wednesday, August 3|
|9:00-12:00pm||Identification of differentially expressed genes (including demo/lab using Bioconductor) - PDF|
|2:00-3:00pm||GMBME - continued||Thursday, August 4|
|9:00-12:00pm||Cluster of gene expression data (including demo/lab using Bioconductor). Class discovery and classification based on gene expression profile - PDF|
|2:00-3:30pm||Lab visit||Friday, August 5|
|9:00-12:00pm||Description of the projects|
|2:00-3:30pm||GMBME - continued|
In this project, the participants will work on quantifying gene expression levels from fluorescent intensities measured on microarray hybridization experiments. After the image analysis, additional work will be carried out to normalize the data to eliminate systematic biases.
The goal of this project is to identify the set of genes that are differentially expressed under different settings, for example, under different disease status/stages or different experimental conditions.
This project will involve grouping genes according to their "similarities", such as similar expression patterns under several experimental conditions or several time points in a cell cycle.
This project allows the team to explore different statistical methods for tumor subtype classification based on tissue specific gene expression profiles of tumorous samples.
The modification of chromatin immunoprecipitation (ChIP) to allow analysis on microarray (ChIP-on-chip) represents a genome-wide approach to investigate interactions between proteins and DNA. In this workshop, we will introduce the use of this novel technique to interrogate cancer epigenome such as profiling of histone modifications.