Related Experiment Video
Updated: Aug 9, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
Published on: July 29, 2022
Error distribution for gene expression data
Elizabeth Purdom1, Susan P Holmes
1Stanford University, USA. epurdom@stat.stanford.edu
Abstract:
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 a biological explanations for this distribution based on the Pareto distribution of the variables used to compute the log ratios.
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