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

Area of Science:

• Bioinformatics
• Microarray Technology
• Infectious Disease Diagnostics

Background:

• Oligonucleotide microarray data analysis is complex due to factors like target concentration, nucleic acid integrity, and host composition.
• Clinical sample analysis shows non-normal fluorescent signal distribution, necessitating distribution-free analytical methods.
• Reliable analytical methods are crucial for adopting microarray technology in routine clinical diagnostics.

Purpose of the Study:

• To evaluate the effectiveness of nonparametric statistical methods for analyzing viral array hybridization data.
• To identify the most suitable method for accurate pathogen surveillance and discovery using microarrays.

Main Methods:

• Assessed the positive predictive value and false positive rates of three nonparametric methods: Mann-Whitney U test, Spearman correlation coefficient, and chi-square test.
• Applied these methods to analyze viral array hybridization data from clinical samples.

Main Results:

• The chi-square test demonstrated superior utility compared to the Mann-Whitney U test and Spearman correlation coefficient.
• The implemented chi-square test achieved a low false positive rate and high predictive accuracy.

Conclusions:

• The chi-square test provides a simple, reliable analytical method for microarray data.
• This method enhances the accuracy and reliability of microarray-based diagnostics for pathogen identification.