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BMC Bioinformatics. 2010 Jun 28;11:354. doi: 10.1186/1471-2105-11-354.

Nonparametric methods for the analysis of single-color pathogen microarrays.

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  • 1Center for Infection and Immunity Mailman School of Public Health Columbia University New York, NY, USA.

Abstract

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.

PMID:
20584331
[PubMed - indexed for MEDLINE]
PMCID:
PMC2909221
Free PMC Article

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