Error distribution for gene expression data. (English) Zbl 1083.62114
Summary: We present a new instance of Laplace’s second Law of Errors and show how it can be used in the analysis of data from microarray experiments. This error distribution is shown to fit microarray expression data much better than a normal distribution. The use of this distribution in a parametric bootstrap leads to more powerful tests as we show that the \(t\)-test is conservative in this setting. We propose biological explanations for this distribution based on the Pareto distribution of the variables used to compute the log ratios.
MSC:
62P10 | Applications of statistics to biology and medical sciences; meta analysis |
62F40 | Bootstrap, jackknife and other resampling methods |
92C40 | Biochemistry, molecular biology |