{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["5"],"submitter":["Martin DE"],"pubmed_abstract":["<h4>Background</h4>A key step in the analysis of microarray expression profiling data is the identification of genes that display statistically significant changes in expression signals between two biological conditions.<h4>Results</h4>We describe a new method, Rank Difference Analysis of Microarrays (RDAM), which estimates the total number of truly varying genes and assigns a p-value to each signal variation. Information on a group of differentially expressed genes includes the sensitivity and the false discovery rate. We demonstrate the feasibility and efficiency of our approach by applying it to a large synthetic expression data set and to a biological data set obtained by comparing vegetatively-growing wild type and tor2-mutant yeast strains. In both cases we observed a significant imp"],"journal":["BMC bioinformatics"],"pagination":["148"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC526220"],"repository":["biostudies-literature"],"pubmed_title":["Rank Difference Analysis of Microarrays (RDAM), a novel approach to statistical analysis of microarray expression profiling data."],"pmcid":["PMC526220"],"pubmed_authors":["Hall MN","Demougin P","Bellis M","Martin DE"],"additional_accession":[]},"is_claimable":false,"name":"Rank Difference Analysis of Microarrays (RDAM), a novel approach to statistical analysis of microarray expression profiling data.","description":"<h4>Background</h4>A key step in the analysis of microarray expression profiling data is the identification of genes that display statistically significant changes in expression signals between two biological conditions.<h4>Results</h4>We describe a new method, Rank Difference Analysis of Microarrays (RDAM), which estimates the total number of truly varying genes and assigns a p-value to each signal variation. Information on a group of differentially expressed genes includes the sensitivity and the false discovery rate. We demonstrate the feasibility and efficiency of our approach by applying it to a large synthetic expression data set and to a biological data set obtained by comparing vegetatively-growing wild type and tor2-mutant yeast strains. In both cases we observed a significant imp","dates":{"release":"2004-01-01T00:00:00Z","publication":"2004 Oct","modification":"2026-05-03T06:42:54.733Z","creation":"2019-03-27T01:08:20Z"},"accession":"S-EPMC526220","cross_references":{"pubmed":["15476558"],"doi":["10.1186/1471-2105-5-148"]}}