ArticleBMC bioinformatics2010
Nonparametric methods for the analysis of single-color pathogen microarrays.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Comparison of High-Level Microarray Analysis Methods in the Context of Result Consistency.PloS one · 2015Article
- Unraveling virus identity by detection of depleted probes with capillary electrophoresis.Analytica chimica acta · 2012Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundThe 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.
resultsPositive 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.
conclusionsThe 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.
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