Robust methods for accurate diagnosis using pan-microbiological oligonucleotide microarrays.
BACKGROUND
To address the limitations of traditional virus and pathogen detection methodologies in clinical diagnosis, scientists have developed high-throughput oligonucleotide microarrays to rapidly identify infectious agents. However, objectively identifying pathogens from the complex hybridization patterns of these massively multiplexed arrays remains challenging.


METHODS
In this study, we conceived an automated method based on the hypergeometric distribution for identifying pathogens in multiplexed arrays and compared it to five other methods. We evaluated these metrics: 1) accurate prediction, whether the top ranked prediction(s) match the real virus(es); 2) four accuracy scores.


RESULTS
Though accurate prediction and high specificity and sensitivity can be achieved with several methods, the method based on hypergeometric distribution provides a significant advantage in term of positive predicting value with two to sixty folds the positive predicting values of other methods.


CONCLUSIONS
The proposed multi-specie array analysis based on the hypergeometric distribution addresses shortcomings of previous methods by enhancing signals of positively hybridized probes.
Liu
Yang
Y
Center for Biomedical Informatics and Section of Genetic Medicine, Dept, of Medicine, The University of Chicago, Chicago, 5841 South Maryland Ave, Ill, USA. yuwang_la@yahoo.com
Sam
Lee
L
Li
Jianrong
J
Lussier
Yves A
YA
D001424:Bacterial Infections / Q000175:diagnosis; D019295:Computational Biology / Q000379:methods; D007239:Infections / Q000175:diagnosis*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010272:Parasitic Diseases / Q000175:diagnosis; D012680:Sensitivity and Specificity; D014777:Virus Diseases / Q000175:diagnosis