Random Permutation Test Research Articles

Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks.

In an effort to find gene regulatory networks and clusters of genes that affect cancer susceptibility to anticancer agents, we joined a database with baseline expression levels of 7,245 genes measured by using microarrays in 60 cancer cell lines, to a database with the amounts of 5,084 anticancer agents needed to inhibit growth of those same cell lines. Comprehensive pair-wise correlations were calculated between gene expression and measures of agent susceptibility. Associations weaker than a threshold strength were removed, leaving networks of highly correlated genes and agents called relevance networks. Hypotheses for potential single-gene determinants of anticancer agent susceptibility were constructed. The effect of random chance in the large number of calculations performed was empirically determined by repeated random permutation testing; only associations stronger than those seen in multiply permuted data were used in clustering. We discuss the advantages of this methodology over alternative approaches, such as phylogenetic-type tree clustering and self-organizing maps.

Genetic diversity and microgeographic differentiation of Yushan cane (Yushania niitakayamensis; Poaceae) in Taiwan

Yushan cane (Yushania niitakayamensis) is distributed in southeast Asia. In Taiwan, the species occurs in mountains 1000–3600 m above sea level. The species appears to spread mainly by rhizomes and flowers only rarely. Nine locations across its distribution range in Taiwan were sampled. Locations at higher altitudes generally consist of grassland and forest undergrowth habitats while those of lower altitudes generally consist of forest undergrowth only. Thus two sampling sites (montane grassland and forest undergrowth) were selected from each location at higher altitudes while only one sampling site was selected from each location at lower altitudes, resulting in a total of 13 sampling sites. Within each sampling site, 20 individual plants were sampled. The results of the cluster analysis and the principal coordinate analysis based on random amplified polymorphic DNA (RAPD) indicated that the populations are generally differentiated according to geographical separation and altitudinal differences that interrupt gene flow. The populations at higher altitudes, where the species is distributed somewhat contiguously, were found to be more similar genetically. Analysis of molecular variance (AMOVA) revealed that the among‐location, between sampling sites within location, and among individuals within sampling site components accounted for 15.27%, 4.80% and 79.93% of the total variance, respectively. For locations with two sampling sites, two‐level AMOVA revealed that the diversities between sampling sites (sun and shade habitats) within locations ranged from 2.91% to 7.99% of the total diversity. Random permutation tests revealed that these diversities were significant, implying that there is microgeographic differentiation due to habitat differences.

Evaluation of a novel molecular vibration-based descriptor (EVA) for QSAR studies: 2. Model validation using a benchmark steroid dataset.

The EVA molecular descriptor derived from calculated molecular vibrational frequencies is validated for use in QSAR studies. EVA provides a conformationally sensitive but, unlike 3D-QSAR methods such as CoMFA, superposition-free descriptor that has been shown to perform well with a wide range of datasets and biological endpoints. A detailed study is made using a benchmark steroid dataset with a training/test set division of structures. Intensive statistical validation tests are undertaken including various forms of crossvalidation and repeated random permutation testing. Latent variable score plots show that the distribution of structures in reduced dimensional space can be rationalized in terms of activity classes and that EVA is sensitive to structural inconsistencies. Together, the findings indicate that EVA is a statistically robust means of detecting structure-activity correlations with performance entirely comparable to that of analogous CoMFAs. The EVA descriptor is shown to be conformationally sensitive and as such can be considered to be a 3D descriptor but with the advantage over CoMFA that structural superposition is not required. EVA has the property that in certain situations the conformational sensitivity can be altered through the appropriate choice of the EVA sigma parameter.