Event Detail

Event Type: 
Probability Seminar
Thursday, April 19, 2007 - 07:00
Kidder 364

Speaker Info

École Normale Supérieure, Paris

There is a strong genetic component in cancer and many other diseases. Often the disease involves, or is caused by, dysfunction in the cellular machinery: genes in normal and disease cells behave in different ways. Those differences in gene expression can be measured using microarray technology, which in the recent past has become a fundamental technique in biology and medicine. A typical microarray measures expression levels for 20 000 genes at the same time: this massive parallel power also raises important statistical and computational problems. After a brief introduction to the technology, we focus on the issue of gene interactions. Standard microarray analysis treats each gene separately, and ranks them according to some measure of differential expression. But in reality genes interact, they act in concert rather than alone; an analysis that accounts for those interactions has the potential to be more statistically accurate and biologically meaningful. We introduce a method called GXNA (Gene eXpression Network Analysis). GXNA uses a gene interaction graph to search for clusters of related genes that are differentially expressed. It has several desirable features, such as fast runtimes and the computation of objective, permutation-based significance levels, and it shows promising results when applied to data sets involving cancer and the human immune system. This is joint work with Rebecca Critchley-Thorne, Peter Lee, and Susan Holmes.