Paper Info
Title
Nonparametric methods for the analysis of single-color pathogen microarrays.
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.
Year
DOI
Venue
2010
10.1186/1471-2105-11-354
BMC Bioinformatics
Keywords
Field
DocType
nucleic acid hybridization,false positive rate,epidemiology,gene expression profiling,algorithms,virology,microarrays,nucleic acid,bioinformatics,chi square test
Chi-square test,False positive rate,Microarray,Biology,Nonparametric statistics,Bioinformatics,Spearman's rank correlation coefficient,DNA microarray,Gene expression profiling,Binomial test
Journal
Volume
Issue
ISSN
11
1
1471-2105
Citations 
PageRank 
References 
9
0.35
13
Authors
8
Name
Order
Citations
PageRank
Omar J. Jabado190.35
Sean Conlan2442.61
P. Lan Quan390.68
Jeffrey Hui490.35
Gustavo Palacios590.68
Mady Hornig690.35
Thomas Briese790.35
W Ian Lipkin8171.40