Workshop on the Analysis of Microarray Data
Sunday afternoon, July, 8
Katie Pollard, University of California, Davis
This workshop will cover statistical and computational problems in the
analysis of microarray data. We will review the biological basis of
microarrays and pre-processing issues such as background correction and
array normalization. Then we will focus on multiple testing
procedures, including choices of error rate, test statistics, null
distribution and error control. Multiple hypothesis testing problems arise
naturally in high dimensional genomic experiments such as
microarray studies. Identifying differently expressed genes in two or
more populations is a typical example. Other testing problems
include analyses of significantly correlated gene expression profiles and
identification of genes whose expression patterns are associated with an
outcome of interest, such as a clinical measurement. We will discuss
currently available methods for addressing these questions with an
emphasis on procedures implemented in the R multtest package.
Bio sketch:
Katie Pollard received her Ph.D. from UC Berkeley Division of
Biostatistics under the supervision of Mark van der Laan. Her research at
Berkeley included developing computationally intensive statistical methods
for analysis of microarray data with applications in cancer biology. She
has developed Bioconductor packages for clustering and multiple hypothesis
testing. In November 2003, she began an NIH Postdoctoral Fellowship in the
labs of David Haussler and Todd Lowe in the Center for Biomolecular
Science & Engineering at UC Santa Cruz. Her projects involved comparative
genomics, population genetics, and transcriptomics approaches to studying
human and microbial genome sequences. In November 2005, she became an
Assistant Professor at UC Davis. She is affiliated with the Davis Genome
Center and Department of Statistics. (Personal Website:
http://docpollard.com/katie.html)
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