MOTIVATION: In cDNA microarray experiments all samples are labeled with either Cy3 or Cy5. Systematic and gene-specific dye bias effects have been observed in dual-color experiments. In contrast to systematic effects which can be corrected by a normalization method, the gene-specific dye bias is not completely suppressed and may alter the conclusions about the differentially expressed genes. METHODS: The gene-specific dye bias is taken into account using an analysis of variance model. We propose an index, named label bias index, to measure the gene-specific dye bias. It requires at least two self-self hybridization cDNA microarrays. RESULTS: After lowess normalization we have found that the gene-specific dye bias is the major source of experimental variability between replicates. The ratio (R/G) may exceed 2. As a consequence false positive genes may be found in direct comparison without dye-swap. The stability of this artifact and its consequences on gene variance and on direct or indirect comparisons are addressed. AVAILABILITY: http://www.inapg.inra.fr/ens_rech/mathinfo/recherche/mathematique
MOTIVATION: In cDNA microarray experiments all samples are labeled with either Cy3 or Cy5. Systematic and gene-specific dye bias effects have been observed in dual-color experiments. In contrast to systematic effects which can be corrected by a normalization method, the gene-specific dye bias is not completely suppressed and may alter the conclusions about the differentially expressed genes. METHODS: The gene-specific dye bias is taken into account using an analysis of variance model. We propose an index, named label bias index, to measure the gene-specific dye bias. It requires at least two self-self hybridization cDNA microarrays. RESULTS: After lowess normalization we have found that the gene-specific dye bias is the major source of experimental variability between replicates. The ratio (R/G) may exceed 2. As a consequence false positive genes may be found in direct comparison without dye-swap. The stability of this artifact and its consequences on gene variance and on direct or indirect comparisons are addressed. AVAILABILITY: http://www.inapg.inra.fr/ens_rech/mathinfo/recherche/mathematique
Authors: Madeleine S Q Kortenhorst; Marianna Zahurak; Shabana Shabbeer; Sushant Kachhap; Nathan Galloway; Giovanni Parmigiani; Henk M W Verheul; Michael A Carducci Journal: Clin Cancer Res Date: 2008-11-01 Impact factor: 12.531
Authors: Matthew E Ritchie; Matthew S Forrest; Antigone S Dimas; Caroline Daelemans; Emmanouil T Dermitzakis; Panagiotis Deloukas; Simon Tavaré Journal: BMC Bioinformatics Date: 2010-05-26 Impact factor: 3.169