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Activity Number: 237 - Feature Selection and Statistical Learning in Genomics
Type: Contributed
Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #323168 View Presentation
Title: FUNctioNal ELastic-Net (FUNNEL) Pipeline for Gene Set Enrichment Analysis with Overlapping
Author(s): Yun Zhang* and Juilee Thakar and Xing Qiu
Companies: University of Rochester and University of Rochester and University of Rochester
Keywords: functional data analysis ; high-dimensional data ; elastic-net ; linear discriminant analysis ; rank-based test ; gene set enrichment analysis
Abstract:

Gene Set Enrichment Analysis (GSEA) is a computational tool that incorporates knowledge in a prior defined gene sets (e.g. known biological mechanisms) to data-driven gene expression analysis. However, current methods ignore the heavy gene overlapping among multiple gene sets. For time-course data, we propose a new algorithm called "FUNNEL-GSEA" based on functional data analysis. FUNNEL borrows temporal information from neighboring time points and decomposes the overlapping genes by functional extensions of principal component analysis and elastic-net regression. We also establish an equivalence between penalized concurrent functional regression and penalized high-dimensional multivariate regression, which greatly boosts the computational efficiency. Furthermore, we introduce a weighted Mann-Whitney U test for the gene-set-level hypothesis testing, which can also be useful in general circumstances. By applying FUNNEL in both simulations and a large-scale time-course gene expression data on human influenza infection, the proposed algorithm shows uniformly better ROC curves and identifies more relevant gene sets to influenza than competing approaches. FUNNEL-GSEA is a free R package.


Authors who are presenting talks have a * after their name.

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