A power law global error model for the identification of differentially expressed genes in microarray data

Background: High-density oligonucleotide microarray technology enables the discovery of genes that are transcriptionally modulated in different biological samples due to physiology, disease or intervention. Methods for the identification of these so-called "differentially expressed genes" (DEG) would largely benefit from a deeper knowledge of the intrinsic measurement variability. Though it is clear that variance of repeated measures is highly dependent on the average expression level of a given gene, there is still a lack of consensus on how signal reproducibility is linked to signal intensity. The aim of this study was to empirically model the variance versus mean dependence in microarray data to improve the performance of existing methods for identifying DEG. Results: In the present work we used data generated by our lab as well as publicly available data sets to show that dispersion of repeated measures depends on location of the measures themselves following a power law...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Norman Pavelka, Mattia Pelizzola, Caterina Vizzardelli, Monica Capozzoli, Andrea Splendiani, Francesca Granucci, Paola Ricciardi-Castagnoli
• Keywords: Biological Samples, BMCBI 2004, Expressed Genes, Microarray Data