Nonparametric methods for the analysis of single-color pathogen microarrays

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

Nonparametric methods for the analysis of single-color pathogen microarrays
Jabado, Omar J.
Conlan, Sean
Quan, Phuong-Lan
Hui, Jeffrey
Palacios, Gustavo F.
Hornig, Mady
Briese, Thomas
Lipkin, W. Ian
Center for Infection and Immunity
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Bmc Bioinformatics
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. 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. 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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Omar J. Jabado, Sean Conlan, Phuong-Lan Quan, Jeffrey Hui, Gustavo F. Palacios, Mady Hornig, Thomas Briese, W. Ian Lipkin, , Nonparametric methods for the analysis of single-color pathogen microarrays, Columbia University Academic Commons, .

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