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 chi-square test yielded a combination of low false positive rates and a high degree of predictive accuracy.
Jabado
Omar J
OJ
Center for Infection and Immunity Mailman School of Public Health Columbia University New York, NY, USA.
Conlan
Sean
S
Quan
Phenix-Lan
PL
Hui
Jeffrey
J
Palacios
Gustavo
G
Hornig
Mady
M
Briese
Thomas
T
Lipkin
W Ian
WI
D000818:Animals; D020869:Gene Expression Profiling; D006801:Humans; D015233:Models, Statistical; D009693:Nucleic Acid Hybridization / Q000379:methods; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012015:Reference Standards; D014780:Viruses / Q000235:genetics*