Exploratory differential gene expression analysis in microarray experiments with no or limited replication.
We describe an exploratory, data-oriented approach for identifying candidates for differential gene expression in cDNA microarray experiments in terms of alpha-outliers and outlier regions, using simultaneous tolerance intervals relative to the line of equivalence (Cy5 = Cy3). We demonstrate the improved performance of our approach over existing single-slide methods using public datasets and simulation studies.
Loguinov
Alexander V
AV
Department of Nutritional Sciences and Toxicology, University of California at Berkeley, Morgan Hall, Berkeley, CA 94720, USA. Avl53@aol.com
Mian
I Saira
IS
Vulpe
Chris D
CD
D000704:Analysis of Variance; D003198:Computer Simulation; D004261:DNA Replication / Q000235:genetics; D018076:DNA, Complementary / Q000235:genetics; D004271:DNA, Fungal / Q000235:genetics; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data; D015966:Gene Expression Regulation, Fungal / Q000235:genetics; D008954:Models, Biological; D009687:Nuclear Proteins / Q000172:deficiency / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000706:statistics & numerical data; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics; D014157:Transcription Factors / Q000172:deficiency / Q000235:genetics
D018076:DNA, Complementary; D004271:DNA, Fungal; C084954:MAC1 protein, S cerevisiae; D009687:Nuclear Proteins; D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors