American Statistical Association
New York City
Metropolitan Area Chapter

Chapter Workshop
March 30, 2007

The New York Metro Area Chapter of the American Statistical Association

In Collaboration With

The Department of Public Health of the Weill Medical College of Cornell University

Are Pleased to Invite You to a Workshop



Dhammika Amaratunga, Ph.D.
Senior Research Fellow
Johnson & Johnson Pharmaceutical Research & Development


Javier Cabrera, Ph.D.
Director of the Institute of Biostatistics
Rutgers University


The emergence of DNA microarray technology has empowered researchers in functional genomics to monitor gene expression profiles, thousands of genes (perhaps even an entire genome) at a time. Of course, this means lots of data! And, in fact, the large volume of data generated by these experiments, and the challenge their analysis presents, has caused a paradigm shift in biology and created an opportunity for some very interesting statistical work.

A typical analysis of a DNA microarray experiment involves a pre-processing stage and a processing stage. In the pre-processing stage, microarrays are checked for quality, systematic effects are removed by nonlinear normalization, and the data are screened for outliers and other unusual features. At the processing stage, the pre-processed data are analyzed for gene expression profiles that differ significantly across sample groupings. Although this can be done using a gene-by-gene approach with conventional statistical methods, better results are obtained with more sophisticated tools that incorporate strategies for pooling information across genes, either through modeling, Bayesian-type arguments, regularization, supervised classification, unsupervised classification or by incorporating putative functional information regarding the genes themselves.

In this short course, we intend to cover many of the statistical considerations involved in a typical microarray data analysis. We will do this mainly in the context of a single microarray dataset, through which we will illustrate the statistical issues involved at both stages of the analysis and many of the more promising statistical approaches.

Biographical Sketches

Dhammika Amaratunga is a Senior Research Fellow in Non-clinical Biostatistics at Johnson & Johnson Pharmaceutical Research & Development, LLC. In recent years, his primary focus has been on gene expression data analysis, about which he and his collaborators have written numerous publications, including a book, taught courses and given several presentations. He has a Ph.D. in Statistics from Princeton University.

Javier Cabrera is the Director of the Institute of Biostatistics at Rutgers University. He received his Ph.D. from Princeton University and has lectured in statistics at Rutgers University, National University of Singapore, and Hong Kong University of Science & Technology. He is author and co-author of many publications in the areas of data mining and functional Genomics, including a book in Exploration and Analysis of DNA microarray and Protein array data.

Drs. Amaratunga and Cabrera are co-authors of
Exploration and Analysis of DNA Microarray and Protein Array Data,
published by John Wiley (2003).

Workshop Slides

Friday, March 30, 2007

9:00 A.M. to 1:00 P.M.
Lunch is at 12:00 P.M.

Weill Medical College of Cornell University
Department of Public Health
411 East 69th Street
3rd Floor Conference Room, KB-301
New York, New York

Registration and Fees
$40 for NYC Metro Area Chapter Members
$50 for Non-Chapter Members
$25 for Students (include copy of current student ID with payment)
Registration fee includes a mid-morning refreshment break and lunch.

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