FUNNEL-GSEA: FUNctioNal ELastic-net regression in time-course gene set enrichment analysis. Results: We propose an inferential framework for GSEA based on functional data analysis, which utilizes the temporal information based on functional principal component analysis, and disentangles the effects of overlapping genes by a functional extension of the elastic-net regression. Furthermore, the hypothesis testing for the gene sets is performed by an extension of Mann-Whitney U test which is based on weighted rank sums computed from correlated observations. By using both simulated datasets and a large-scale time-course gene expression data on human influenza infection, we demonstrate that our method has uniformly better receiver operating characteristic curves, and identifies more pathways relevant to immune-response to human influenza infection than the competing approaches. Availability and Implementation: The methods are implemented in R package FUNNEL, freely and publicly available at: https://github.com/yunzhang813/FUNNEL-GSEA-R-Package.
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References in zbMATH (referenced in 2 articles )
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- Li, Xue; Yang, Wenhong; Fang, Yanning: Epigenetic profiles reveal that ADCYAP1 serves as key molecule in gestational diabetes mellitus (2019)
- Meng, YuXiu; Cai, Xue Hong; Wang, LiPei: Potential genes and pathways of neonatal sepsis based on functional gene set enrichment analyses (2018)