On testing the significance of sets of genes

By Bradley Efron, Robert Tibshirani. 2006

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This paper discusses the problem of identifying dieren tially expressed groups of genes from a microarray experiment. The groups of genes are externally dened, for example, sets of gene pathways derived from biological databases. Our starting point is the interesting Gene Set Enrichment Analysis (GSEA) procedure of Subramanian et al. (2005). We study the problem in some generality and propose two potential improvements to GSEA: the maxmean statistic for summarizing gene-sets, and restandardization for more accurate inferences. We discuss a variety of examples and extensions, including the use of gene-set scores for class predictions. We also describe a new R language package GSA that implements our ideas.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1c7ffc9b8da5873f06a3edc4448f80a01f174aeb

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Citations in the corpus

Stable Feature Selection for Biomarker Discovery (2010, background): “, 2005) and the maxmean pproach (Efron and Tibshirani, 2007). The reader is referred to am and Kim (2008) and Dinu et al.”
The Consensus Molecular Subtypes of Colorectal Cancer (2015, methodology): “is supported by grants from the Dutch Cancer Society (UVA2011-4969, UVA2014-7245), Worldwide Cancer Research (14-1164), the Maag Lever Darm Stichting (MLDS-CDG 14-03) and the European Research Council (ERG-StG 638193).”
MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data (2010, methodology): “Over the past 5 years, many diﬀerent algorithms have been developed for group-based enrichment analysis, including GSEA (9), GSEA-P (26), PAGE (27), globaltest (11), SAFE (12), SAM-GS (13) and GSA (14).”
BMC Bioinformatics (2005, neutral): “As a data-guided simulation using known gene sets [21,25], we introduce an algorithm for using real data in the Results section.”
KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis (2021, methodology): “It integrates seven widely used FCS methods: GSEA (14), GSA (16), PADOG (17), PLAGE (18), GAGE (19), GLOBALTEST (20) and SAFE (21) and two prominent PT methods: GANPA (23), and CEPA (24), into a single ensemble score (R score).”
Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods (2013, background): “Maxmean statistic Finally, Efron and Tibshirani (33) propose to use, what they call the maxmean statistic.”
Integrating diverse genomic data using gene sets (2011, background): “There are approaches for gene set analysis in other contexts that operate directly on the raw data - for example, on expression levels [33] or mutation counts [34].”
GSVA: gene set variation analysis for microarray and RNA-Seq data (2013, methodology): “Many methodological variations of GSE methods have been proposed [1–6], including non-parametric enrichment statistics [4,7], battery testing [8–10], and focused gene set testing [1,11,12].”
Camera: a competitive gene set test accounting for inter-gene correlation (2012, methodology): “A number of authors have concluded that gene set test P-values should always be evaluated by permutation or resampling of RNA samples, even when a competitive hypothesis is of interest, because sample permutation preserves the inter-gene correlation structure and therefore protects the test against ”
Feature Screening via Distance Correlation Learning (2012, background): “There is a surged interest in pathway analyses in the recent literature (Ashburner et al. 2000; Mootha et al. 2003; Subramanian et al. 2005; Tian et al. 2005; Bild et al. 2006; Efron and Tibshirani 2007; Jones et al. 2008).”
Data integration in the era of omics: current and future challenges (2014, background): “Gene Set Enrichment Analysis (GSEA, [39,40]) is a popular approach for integrating previous biological knowledge in the analysis of transcriptomics, which has been extended to other domains such as genomics and proteomics (e.g. [41] in this supplement) and the analysis of genomic regions (GREAT,…”
Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis (2019, methodology): “…significance tests and pathway analysis methods have gained widespread acceptance among many researchers involved in “omics” data analysis (Draghici et al., 2007; Efron & Tibshirani, 2007; Goeman, van de Geer, de Kort, & van Houwelingen, 2004; Goffard & Weiller, 2007; Subramanian et al., 2005).”
Gene-set approach for expression pattern analysis (2008, methodology): “Efron and Tibshirani [26] introduced five test statistics for a GSEA algorithm— mean, mean.abs, maxmean, GSEA and GSEA.abs— to test five simulated conditions and concluded that the maxmean statistic is the only method with consistently low P -values in all situations.”