A New Method of Detecting Differentially Expressed Genes through Probe Level Data from Oligonucleotide Arrays

Document Type

Presentation Abstract

Presentation Date

10-18-2007

Abstract

Oligonucleotide arrays such as Affymetrix GeneChips use multiple probes, or a probe set, to measure the abundance of mRNA of every gene of interest. Some analysis methods attempt to summarize the multiple observations into one single score before conducting further analysis such as detecting differentially expressed genes (DEG), clustering and classification. However, there is a risk of losing a significant amount of information and consequently reaching inaccurate or even incorrect conclusions during this data reduction. We developed a novel statistical method to detect DEG for both two-group and k-group cases. It utilizes probe level data and requires no assumptions about the distribution of the dataset. The method was tested on benchmark datasets and compared with existing summarization methods (RMA, GCRMA, MAS5, PLIER, etc.). The results show that our method successfully detects DEG with positive predictive value of 94% while maintaining a low false discovery rate and consistently out performs the existing methods.

Additional Details

Thursday, 18 October 2007
4:10 p.m. in Math 103
3:30 p.m. Refreshments in Math Lounge 109

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