ArticleBMC bioinformatics2004
Comparing functional annotation analyses with Catmap.
Article in BMC bioinformatics, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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50 citing papers in PubMed.
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- FUNNEL-GSEA: FUNctioNal ELastic-net regression in time-course gene set enrichment analysis.Bioinformatics (Oxford, England) · 2017Article
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- Using predictive specificity to determine when gene set analysis is biologically meaningful.Nucleic acids research · 2017Article
- Rapid genome-wide evolution in Brassica rapa populations following drought revealed by sequencing of ancestral and descendant gene pools.Molecular ecology · 2016Article
- Nuclear hormone receptor DHR96 mediates the resistance to xenobiotics but not the increased lifespan of insulin-mutant Drosophila.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- The limitations of simple gene set enrichment analysis assuming gene independence.Statistical methods in medical research · 2016Article
- Longevity GWAS Using the Drosophila Genetic Reference Panel.The journals of gerontology. Series A, Biological sciences and medical sciences · 2015Article
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3 authors.
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Abstract
backgroundRanked gene lists from microarray experiments are usually analysed by assigning significance to predefined gene categories, e.g., based on functional annotations. Tools performing such analyses are often restricted to a category score based on a cutoff in the ranked list and a significance calculation based on random gene permutations as null hypothesis.
resultsWe analysed three publicly available data sets, in each of which samples were divided in two classes and genes ranked according to their correlation to class labels. We developed a program, Catmap (available for download at http://bioinfo.thep.lu.se/Catmap), to compare different scores and null hypotheses in gene category analysis, using Gene Ontology annotations for category definition. When a cutoff-based score was used, results depended strongly on the choice of cutoff, introducing an arbitrariness in the analysis. Comparing results using random gene permutations and random sample permutations, respectively, we found that the assigned significance of a category depended strongly on the choice of null hypothesis. Compared to sample label permutations, gene permutations gave much smaller p-values for large categories with many coexpressed genes.
conclusionsIn gene category analyses of ranked gene lists, a cutoff independent score is preferable. The choice of null hypothesis is very important; random gene permutations does not work well as an approximation to sample label permutations.
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