Nonparametric methods for the analysis of single-color pathogen microarrays
Related Experiment Video
Updated: Jun 11, 2026
A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces
Published on: March 1, 2017
Omar J Jabado1, Sean Conlan, Phenix-Lan Quan
1Center for Infection and Immunity Mailman School of Public Health Columbia University New York, NY, USA.
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.
