02310nas a2200145   4500008004100000022001400041245007800055210006900133260008100202300002000283490000600303520172500309100001902034856011102053       2008                        eng d  a1471-210500aPopulation proportion approach for ranking differentially expressed genes0 aPopulation proportion approach for ranking differentially expres  a236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLANDbBIOMED CENTRAL LTDcSEP  aArticle No. 3800 v93 a<p>Background: DNA microarrays are used to investigate differences in gene expression between two or more classes of samples. Most currently used approaches compare mean expression levels between classes and are not geared to find genes whose expression is significantly different in only a subset of samples in a class. However, biological variability can lead to situations where key genes are differentially expressed in only a subset of samples. To facilitate the identification of such genes, a new method is reported. Methods: The key difference between the Population Proportion Ranking Method (PPRM) presented here and almost all other methods currently used is in the quantification of variability. PPRM quantifies variability in terms of inter-sample ratios and can be used to calculate the relative merit of differentially expressed genes with a specified difference in expression level between at least some samples in the two classes, which at the same time have lower than a specified variability within each class. Results: PPRM is tested on simulated data and on three publicly available cancer data sets. It is compared to the t test, PPST, COPA, OS, ORT and MOST using the simulated data. Under the conditions tested, it performs as well or better than the other methods tested under low intraclass variability and better than t test, PPST, COPA and OS when a gene is differentially expressed in only a subset of samples. It performs better than ORT and MOST in recognizing non differentially expressed genes with high variability in expression levels across all samples. For biological data, the success of predictor genes identified in appropriately classifying an independent sample is reported.</p>1 aGadgil, Mugdha  uhttps://library.ncl.res.in/content/population-proportion-approach-ranking-differentially-expressed-genes-0