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Identification of core subset of gene-sets associated with a cont | 29232
Journal of Proteomics & Bioinformatics

Journal of Proteomics & Bioinformatics
Open Access

ISSN: 0974-276X

+44 1223 790975

Identification of core subset of gene-sets associated with a continuous phenotype


5th International Conference on Proteomics & Bioinformatics

September 01-03, 2015 Valencia, Spain

Shabnam Vatanpour1, Farzana Yasmin1, Xiaoming Wang1, Saumyadipta Pyne2 and Irina Dinu1

1University of Alberta, Canada 2CR Rao Advanced Institute of Mathematics, Statistics and Computer Science, India

Posters-Accepted Abstracts: J Proteomics Bioinform

Abstract :

DNA microarray studies open a new platform with an opportunity to study thousands of genes at the same time. Gene-Set Analysis is a popular approach to examine the association between gene expression of a predefined gene-set and a phenotype. However, often not all the genes within a significant gene set contribute to its significance. Identifying the core subset enhances our understanding of disease biological mechanism. Many methods have been proposed for a binary outcome (diseased versus diseasefree subjects), but only a few for continuous phenotype (tumor size). The challenges consist of a large number of genes in a set and a small sample size and accommodating correlations between genes across a set. We developed a powerful method to reduce the gene-sets associated with a continuous phenotype The method is based on the Linear Combination Test (LCT) for gene-sets, which incorporates the gene expression covariance matrix into the test statistic, via a shrinkage estimation approach. We applied LCT to identify significant gene-sets associated with a continuous phenotype and incorporated Significance Analysis of Microarrays to reduce them to their core subsets. We evaluated the performance of LCT-GSR in a simulation study. This methodological approach helps researchers to identify biologically meaningful genes that are mainly contributed to the association with outcome by screening massive databases, provides additional insights into disease progression and improved treatment strategies, and reduces the costs by focusing on smaller number of genes. It can be applied to a wide range of common situations in which dichotomizing the phenotype is neither easy nor meaningful.

Biography :

Email: vatanpour@ualberta.ca

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