Analysis of high density expression microarrays with signed-rank call algorithms

• Published in: Bioinformatics (Oxford, England) Vol. 18; no. 12; pp. 1593 - 1599
• Main Authors: Liu, W.-m, Mei, R., Di, X., Ryder, T. B., Hubbell, E., Dee, S., Webster, T. A., Harrington, C. A., Ho, M.-h., Baid, J., Smeekens, S. P.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.12.2002
• Subjects: Algorithms; Biological and medical sciences; Fundamental and applied biological sciences. Psychology; Gene Expression - genetics; Gene Expression Profiling - methods; Gene Expression Regulation - genetics; General aspects; Humans; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Models, Genetic; Models, Statistical; Oligonucleotide Array Sequence Analysis - methods; Reproducibility of Results; Sensitivity and Specificity; Software; Statistics, Nonparametric; Transcription, Genetic - genetics; Yeasts - genetics; Data analysis; Sensitivity; Specificity; Complementary DNA; DNA chip; Robustness; Detection; Gene expression; Bioinformatics; Algorithm
• ISSN: 1367-4803; 1367-4811; 1367-4811
• DOI: 10.1093/bioinformatics/18.12.1593

Motivation: We consider the detection of expressed genes and the comparison of them in different experiments with the high-density oligonucleotide microarrays. The results are summarized as the detection calls and comparison calls, and they should be robust against data outliers over a wide target concentration range. It is also helpful to provide parameters that can be adjusted by the user to balance specificity and sensitivity under various experimental conditions. Results: We present rank-based algorithms for making detection and comparison calls on expression microarrays. The detection call algorithm utilizes the discrimination scores. The comparison call algorithm utilizes intensity differences. Both algorithms are based on Wilcoxon's signed-rank test. Several parameters in the algorithms can be adjusted by the user to alter levels of specificity and sensitivity. The algorithms were developed and analyzed using spiked-in genes arrayed in a Latin square format. In the call process, p-values are calculated to give a confidence level for the pertinent hypotheses. For comparison calls made between two arrays, two primary normalization factors are defined. To overcome the difficulty that constant normalization factors do not fit all probe sets, we perturb these primary normalization factors and make increasing or decreasing calls only if all resulting p-values fall within a defined critical region. Our algorithms also automatically handle scanner saturation. Availability: These algorithms are available commercially as part of the MAS 5.0 software package. Contact: wei-min_liu@affymetrix.com * To whom correspondence should be addressed. † Current address: Mail Code CR145, Oregon Health Sciences University, Portland, OR 79201