Analyzing gene expression data in terms of gene sets: methodological issues
By Jelle J. Goeman, Peter Buhlmann. Bioinform., 2007
808 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/e2f393d815d826b22ae36c7b606caf875ffecc9e
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In Artificial Intelligence
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Citations in the corpus
- RAPTT: An Exact Two-Sample Test in High Dimensions Using Random Projections (2014, background): “…is known that some of these methods either become undefined or perform poorly on a high-dimensional dataset, that is, when p > n. Testing the equality of means among high-dimensional populations occurs, for example, in biological applications (Ville, Blue, and Unser 2004; Goeman and Buhlmann 2007).”
- Gene set enrichment analysis with topGO (2006, background): “Goeman and Bühlmann (2007) There are also a number of strategies/algorithms to account for the GO topology, see Table 1, each of them having specific requirements.”
- GAGE: generally applicable gene set enrichment for pathway analysis (2009, neutral): “Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge [ 2- 12 ].”
- GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists (2009, neutral): “In [ 17 ] the authors study the advantages of sample re-sampling.”
- DNA methylation arrays as surrogate measures of cell mixture distribution (2012, methodology): “This is typically done by conducting a pathway analysis along the lines of one of the methods described in [36], the best option of which is Gene Set Enrichment Analysis (GSEA) [37].”
- GSVA: gene set variation analysis for microarray and RNA-Seq data (2013, background): “An important distinction among many of the GSE methods is the definition of the null hypothesis that is tested [18].”
- Gene-set approach for expression pattern analysis (2008, methodology): “An interesting analog of GSEA in a self-contained analysis of a gene set is suggested by Geoman and Buhlmann [ 11 ].”
- PathNet: a tool for pathway analysis using topological information (2012, neutral): “Comprehensive reviews of these approaches are available [2-4].”