The multiple testing problem is a well-known statistical stumbling block in high-throughput
data analysis, where large scale repetition of statistical methods introduces unwanted noise …

The quantitative comparison of data acquired under different conditions is an important
aspect of experimental science. The most widely used statistic for quantitative comparisons …

Background Genomics and proteomics analyses regularly involve the simultaneous test of
hundreds of hypotheses, either on numerical or categorical data. To correct for the …

Motivation Permutation tests offer a straightforward framework to assess the significance of
differences in sample statistics. A significant advantage of permutation tests are the relatively …

Characterizing the technical precision of measurements is a necessary stage in the planning
of experiments and in the formal sample size calculation for optimal design. Instruments that …

Motivation: Advantages of statistical testing of high-throughput screens include P-values,
which provide objective benchmarks of compound activity, and false discovery rate …

Big multi-omics data in bioinformatics often consists of a huge number of features and
relatively small numbers of samples. In addition, features from multi-omics data have their …

The multiple hypothesis testing problem is inherent in high-throughput quantitative genomic,
transcriptomic, proteomic, and other “omic” screens. The correction of p-values for multiple …

Motivation Reproducibility assessment is essential in extracting reliable scientific insights
from highthroughput experiments. Inconsistency between technical replicates poses a …

Motivation: Due to advances in experimental technologies, such as microarray, mass
spectrometry and nuclear magnetic resonance, it is feasible to obtain large-scale data sets …