Quality Indicators Increase the Reliability of Microarray Data

• Published in: Genomics (San Diego, Calif.) Vol. 80; no. 4; pp. 385 - 394
• Main Authors: Raffelsberger, Wolfgang, Dembélé, Doulaye, Neubauer, Mike G., Gottardis, Marco M., Gronemeyer, Hinrich
• Format: Journal Article
• Language: English
• Published: San Diego, CA Elsevier Inc 01-10-2002; Elsevier
• Subjects: Biological and medical sciences; Cluster Analysis; Data Interpretation, Statistical; Diverse techniques; Fundamental and applied biological sciences. Psychology; Genetics; Life Sciences; Molecular and cellular biology; Oligonucleotide Array Sequence Analysis; Quality Indicators, Health Care; Regression Analysis; Reverse Transcriptase Polymerase Chain Reaction; biostatistics; retinoic acid; cell differentiation; gene expression profiling; DNA chip; Biomathematics; Molecular biology; Cell differentiation; Gene expression; Retinoic acid; Statistics
• ISSN: 0888-7543; 1089-8646
• DOI: 10.1006/geno.2002.6848

Large-scale gene expression profiling with DNA microarrays opens new dimensions to molecular biology but still lacks the overall precision of traditional low-scale techniques. We developed a novel strategy of data processing linking search stringency to quality indicators for efficient detection of low-level, regulated genes. Using retinoid-induced differentiation of NB-4 promyelocytic cells, the variation of expression profiles between biological duplicates was studied and compared with the changes induced by
all-trans retinoic acid (
atRA) treatment. An analysis of 4320 genes showed that retinoic acid has mainly geneactivating function in NB-4 cells. Treatment with
atRA for 18 hours induced metabolic genes that may be associated with cell differentiation and signaling factors triggering later events leading to apoptosis; cytokine genes were among the highest stimulated by
atRA. Notably, we identified a regulatory loop inhibiting
MYC action: as
MYC was downregulated, a cognate repressor of
MYC was upregulated.