Significance analysis of functional categories in gene expression studies: a structured permutation approach

By William T. Barry, Andrew B. Nobel, Fred A. Wright. Bioinform., 2005

388 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a89579758b9ed13d3ba62c3b501dd727b179db44

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

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).”
Finding large average submatrices in high dimensional data (2009, background): “This threshold was selected to adjust for the anti-conservative behavior of the hypergeometric test in the presence of even moderate levels of gene correlation (see Barry et al. (2005) for more details).”
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).”
GAGE: generally applicable gene set enrichment for pathway analysis (2009, neutral): “Sample randomization methods test significance of gene sets based on permutation of sample labels, with GSEA [3,4], SAFE [10] and SAM-GS [9] as representatives.”
Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges (2012, methodology): “The pathway-level statistics used by current approaches include the Kolmogorov-Smirnov statistic [21,29], sum, mean, or median of gene-level statistic [24], the Wilcoxon rank sum [30], and the maxmean statistic [31].”
Tests for High-Dimensional Covariance Matrices (2010, background): “Identifying significant gene sets, instead of individual genes, for certain treatments under evaluation is gaining substantial interests; see Barry, Nobel, and Wright (2005), Efron and Tibshrini (2007), and Newton et al. (2007).”
A two-sample test for high-dimensional data with applications to gene-set testing (2010, methodology): “The {ρl}(3)l=1 were generated independently from U(2, 3), which were ρ1 = 2.”
A Low Dose of Dietary Resveratrol Partially Mimics Caloric Restriction and Retards Aging Parameters in Mice (2008, methodology): “To discover gene classes that are significantly different between the two groups in each comparison, we used the Significance Analysis of Function and Expression (SAFE) analysis [25].”
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].”
Two Sample Tests for High Dimensional Covariance Matrices (2012, background): “…matrices comes from the need to identify sets of genes which are signiﬁcantly diﬀerent with respect to two treatments in genetic research; see Barry, Nobel and Wright (2005), Efron and Tibshrini (2007), Newton et al. (2007) and Nettleton, Recknor and Reecy (2008) for comprehensive…”
Camera: a competitive gene set test accounting for inter-gene correlation (2012, background): “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 ”
Gene-set approach for expression pattern analysis (2008, background): “A second interesting extension is the absolute enrichment analysis that takes into account both up and down-regulated genes in calculating enrichment scores [19, 22, 26, 31].”
Chipster: user-friendly analysis software for microarray and other high-throughput data (2011, methodology): “As opposed to testing genes individually, the user can also perform gene set tests based on the globaltest package [24] and SAFE [25], which calculate a test statistic per GO category or KEGG pathway taking into account the expression levels of the genes.”
A framework to identify physiological responses in microarray-based gene expression studies: selection and interpretation of biologically relevant genes. (2008, methodology): “For the t-test, the threshold choice is flexible and the significance level is chosen by the researcher (3, 8).”
Analyzing gene expression data in terms of gene sets: methodological issues (2007, neutral): “Contact: j.j.goeman@lumc.nl”