A benchmark for statistical microarray data analysis that preserves actual biological and technical variance
Related Experiment Video
Updated: Jun 17, 2026

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
Published on: March 15, 2011
Benoît De Hertogh1, Bertrand De Meulder, Fabrice Berger
1Unité de Recherche en Biologie Moléculaire, Facultés Universitaires Notre-Dame de la Paix (F.U.N.D.P.), Rue de Bruxelles 61, B-5000 Namur, Belgium.
Background:
Recent reanalysis of spike-in datasets underscored the need for new and more accurate benchmark datasets for statistical microarray analysis. We present here a fresh method using biologically-relevant data to evaluate the performance of statistical methods.
Results:
Our novel method ranks the probesets from a dataset composed of publicly-available biological microarray data and extracts subset matrices with precise information/noise ratios. Our method can be used to determine the capability of different methods to better estimate variance for a given number of replicates. The mean-variance and mean-fold change relationships of the matrices revealed a closer approximation of biological reality.
Conclusions:
Performance analysis refined the results from benchmarks published previously.We show that the Shrinkage t test (close to Limma) was the best of the methods tested, except when two replicates were examined, where the Regularized t test and the Window t test performed slightly better.
Availability:
The R scripts used for the analysis are available at http://urbm-cluster.urbm.fundp.ac.be/~bdemeulder/.
Related Concept Videos
DNA Microarrays
Variability: Analysis
The range is a simple measure of variability, indicating the difference between the highest and...
Statistical Analysis: Overview
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Bioequivalence Data: Statistical Interpretation
Comparing Copy Number Variations and SNPs
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Detection of Gross Error: The Q Test

