publication . Other literature type . Article . 2003

Statistical significance for genomewide studies

Robert Tibshirani; John D. Storey;
Open Access English
  • Published: 25 Jul 2003
  • Publisher: National Academy of Sciences
With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called ...
Medical Subject Headings: nutritional and metabolic diseasesnervous system diseases
free text keywords: Physical Sciences, False positive rate, Per-comparison error rate, False positive paradox, Genetics, False discovery rate, Data mining, computer.software_genre, computer, Null hypothesis, Data set, p-value, Multiple comparisons problem, Biology
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