Nonparametric methods for the analysis of single-color pathogen microarrays

Background: The analysis of oligonucleotide microarray data in pathogen surveillance and discovery is a challenging task. Target template concentration, nucleic acid integrity, and host nucleic acid composition can each have a profound effect on signal distribution. Exploratory analysis of fluorescent signal distribution in clinical samples has revealed deviations from normality, suggesting that distribution-free approaches should be applied. Results: Positive predictive value and false positive rates were examined to assess the utility of three well-established nonparametric methods for the analysis of viral array hybridization data: (1) Mann-Whitney U, (2) the Spearman correlation coefficient and (3) the chi-square test. Of the three tests, the chi-square proved most useful. Conclusions: The acceptance of microarray use for routine clinical diagnostics will require that the technology be accompanied by simple yet reliable analytic methods. We report that our implementation of the ch...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Omar J. Jabado, Sean Conlan, P. Lan Quan, Jeffrey Hui, Gustavo Palacios, Mady Hornig, Thomas Briese, W. Ian Lipkin
• Keywords: BMCBI 2010, Chi-square Test, False Positive, Nucleic Acid