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Clusters are tested for association with case-control status (Gusev et al. 2011).
All possible clusters are tested for statistical significance by a log likelihood ratio test, which accounts for multiple testing (6 ).
Only a small number of all possible zones (potential clusters) are tested, to minimize multiple testing and false positives.
Consequently, the results obtained in this study need to be validated in subsequent studies, in which clusters are tested in a pre hoc fashion.
For each method of analysis, identified mortality clusters are tested as to whether they are randomly distributed over space, over time or over space and time, and evaluated with statistical significance tests.
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The clusters were tested for neutral evolution using Tajima's Test of Neutrality [ 63] implemented in MEGA 6.0.
The stability of the clusters was tested by UV visible absorption spectrophotometry and TEM micrographs observation.
Different targeting sequences derived from the Arxula adeninivorans and Hansenula polymorpha rDNA clusters were tested in A. adeninivorans integration/expression vectors.
Protein clusters were tested for significant changes using the R Bioconductor (www.bioconductor.org) packages GAGE (Luo et al. [2009]) and GlobalTest (Goeman et al. [2006]), setting p < 0.05 and a relative fold change (FC) of 1.5 (log2 FC > 0.58) as thresholds.
Non-random associations of disease status with the two main clusters were tested using two-tailed Fisher's exact test.
Data were analysed for spatial clustering using the Bernoulli model and the significance of clusters were tested using the Kulldorff scan statistic.
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