Kellie J. Archer

Sanger Hall B1-069   Department of Medicinal Biostatistics   Virginia Commonwealth University   1101 E. Marshall St.   Richmond, VA 23298	Tel: 804-827-2039 Fax: 804-827-8900 E-mail: KJArcher@vcu.edu
Web: http://www.people.vcu.edu/~kjarcher/
Research: Statistical methods for the analysis of microarray data

BBSI project: mixed models analysis of Affymetrix Genechip data.

This project will focus on performing a mixed models analysis of Affymetrix Genechip data. The Affymetrix Genechip technology exploits the hybridization process by affixing several small single strands of DNA (i.e., spots) to the surface of a chip in precisely defined locations and then allowing a sample of DNA or RNA isolated from a biological source to react (bind) to their complementary strands. Rather then an entire gene being placed in a spot, the Affymetrix Genechip is an oligonucleotide array consisting of a several perfect match (PM) and their corresponding mismatch (MM) probes that interrogate for a single gene. The PM is the exact complementary sequence of the target genetic sequence, composed of 25 base pairs. Each PM probe has a corresponding MM probe, which has the same sequence with exception that the middle base (13th) position has been reversed. The underlying idea behind the MM probes is to account for background. There are roughly 11-20 PM/MM probe pairs that interrogate for each gene, called a probe set. Current research has focused on identifying differentially expressed genes using probe set summaries. This project will explore the use of mixed models applied to PM data for feature selection.

Other research interests

At VCU, gene expression is being quantified using the Affymetrix Genechip technology, which is characterized by the ability to simultaneously study the expression of thousands of genes. Other areas of research include (i) development of novel methods for performing image analysis, normalization, and expression summaries for oligonucleotide microarray data; (ii) development of unsupervised and supervised learning methods for microarray data analysis; and (iii) development of software libraries for preprocessing steps and for microarray data analysis. In addition, possibilities exist to develop new methods for analyzing loss of heterozygosity data.

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