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In tests, clusters that we developed with SVD perfectly matched what was expected based on Linnaean taxonomy.
Meinshausen's hierarchical variable selection subsequently tests clusters of similar genes while keeping control of the family-wise error rate (FWER).
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Descriptive statistics, chi-square tests, cluster analyses, and ANOVAs were conducted.
We propose a method for the statistical analysis of fMRI data that tests cluster units rather than voxel units for activation.
Both tests clustered severe G6PD-deficient samples in the low G6PD activity range.
In addition, data from [51], in which were tested clusters of P-lacZ-white transgenes generating variegation in the eye for the white marker [54], [55], are also reported.
Hierarchical clustering of data sets revealed eight well-defined clusters, representing ECMP combinations that either promoted high normalized SOX17 expression in all three hESC lines tested (Cluster I), two out of three hESC lines tested (clusters II, III, and IV), one out of the three hESC lines tested (cluster V, VI, VII), or in none of the hESC lines tested (cluster VIII).
SaTScan™ employs Kulldorff's spatial scan statistic (16– 18) to identify and test clusters of childhood mortality.
It employs Kulldorff's spatial scan statistic to identify and test clusters of mortality.
To measure the uniformity of generated clusters, 10 test clusters were created with 100 points in each cluster.
A subset of these two categories was checked using PCR amplification, and 86% of the tested clusters were validated.
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