Statistical significance for genomewide studies

Abstract

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 the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.

Figures

Fig. 1.

A density histogram of the 3,170 p values from the Hedenfalk et al. (14) data. The dashed line is the density histogram we would expect if all genes were null (not differentially expressed). The dotted line is at the height of our estimate of the proportion of null p values.

Fig. 2.

Results from the Hedenfalk et al. (14) data. (a) The q values of the genes versus their respective t statistics. (b) The q values versus their respective p values. (c) The number of genes occurring on the list up through each q value versus the respective q value. (d) The expected number of false positive genes versus the total number of significant genes given by the q values.

Fig. 3.

The versus λ for the data of Hedenfalk et al. (14). The solid line is a natural cubic spline fit to these points to estimate .