{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Jabado OJ"],"funding":["NEI NIH HHS","NIAID NIH HHS","NHLBI NIH HHS","NIGMS NIH HHS"],"pagination":["354"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC2909221"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11"],"pubmed_abstract":["<h4>Background</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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."],"journal":["BMC bioinformatics"],"pubmed_title":["Nonparametric methods for the analysis of single-color pathogen microarrays."],"pmcid":["PMC2909221"],"funding_grant_id":["R01 HL083850","T32GM008224","EY017404","AI57158-05","U54 AI057158","R24 EY017404","AI070411","U01 AI070411","HL083850"],"pubmed_authors":["Quan PL","Lipkin WI","Briese T","Hui J","Jabado OJ","Conlan S","Hornig M","Palacios G"],"additional_accession":[]},"is_claimable":false,"name":"Nonparametric methods for the analysis of single-color pathogen microarrays.","description":"<h4>Background</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.","dates":{"release":"2010-01-01T00:00:00Z","publication":"2010 Jun","modification":"2024-10-15T02:34:38.979Z","creation":"2019-03-27T00:32:40Z"},"accession":"S-EPMC2909221","cross_references":{"pubmed":["20584331"],"doi":["10.1186/1471-2105-11-354"]}}