Sentence examples for multiple testing penalties from inspiring English sources

Conversely, Big Data will require additional multiple testing penalties, need for replication, even if reproducibility of results may be an issue and interpretability too.

In addition, the approach helps to manage the multiple testing penalties across numerous categories.

However, some researchers, mindful of historical difficulties of interpreting complex genetic data (17), prefer to apply a cautious approach to secure robust (i.e. taking into account stage 2 multiple testing penalties) and independent (of GWAS discovery) replication data (3, 4).

However we were able to allow for this possibility without incurring a multiple testing penalty.

These approaches reduced the multiple testing penalty by lowering the fraction of the genome probed to 0.19% of the total, while increasing the likelihood of studying SNPs within relevant BAV genes and pathways.

A discovery phase with insufficient stringency will likely lower a study's ability to replicate both main effects and interaction effects as the multiple testing penalty at the replication phase is increased.

First, it can reduce the number of tests performed and hence the multiple testing penalty incurred.

Still, the discovery of SNPs associated with metabolite levels is challenging since testing all metabolites measured in typical metabolomics studies with all SNPs comes with a severe multiple testing penalty.

The Bonferroni correction widely applied to human GWA analyses imposes a substantial multiple testing penalty, effectively limiting the ability to detect loci with small effects to reduce the detection of spurious loci.

The identification of more significant hits after adjustment was likely not simply due to a lighter multiple testing penalty; cis-eQTL required 1,468,327 tests, far less than the 19,323,486 tests for creQTL, and produced no statistically significant results even at the more liberal q-value cutoff of 0.05.

Both techniques have strengths and weaknesses for instance, a GWAS allows for unexpected gene discovery by taking an agnostic approach to the selection of single nucleotide polymorphisms (SNP); it is limited, however, by requiring very large samples to overcome the multiple testing penalty and by the difficulty of explaining association results when identified SNPs are intergenic.