English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

An objective of microarray data analysis is to identify gene expressions that are associated with a disease related outcome.  For each gene, a test statistic is computed to determine if an association exists, and this statistic generates a marginal p-value. In an effort to pool this information across genes, a p-value density function is derived. The p-value density is modeled as a mixture of a uniform (0,1) density and a scaled ratio of normal densities derived from the asymptotic normality of the test statistic. The p-values are assumed to be weakly dependent and a quasi-likelihood is used to estimate the parameters in the mixture density. The quasi-likelihood and the weak dependence assumption enables estimation and asymptotic inference on the false discovery rate for a given rejection region, and its inverse, the p-value threshold parameter for a fixed false discovery rate.  A false discovery rate analysis on a localized prostate cancer data set is used to illustrate the methodology. Simulations are performed to assess the performance of this methodology.

statistical applications in genetics and molecular biology

Inference on the Limiting False Discovery Rate and the P-value Threshold Parameter Assuming Weak Dependence between Gene Expression Levels within Subject