Nonparametric methods for the analysis of single-color pathogen microarrays

Jabado, Omar; Conlan, Sean; Quan, Phenix-Lan; Hui, Jeffrey; Palacios, Gustavo; Hornig, Mady; Briese, Thomas; Lipkin, W. Ian

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 chi-square test yielded a combination of low false positive rates and a high degree of predictive accuracy.


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Also Published In

BMC Bioinformatics

More About This Work

Academic Units
Center for Infection and Immunity
Published Here
September 9, 2014