Research article

Autocorrelation analysis reveals widespread spatial biases in microarray experiments

Amnon Koren¹ , Itay Tirosh¹ and Naama Barkai^1,2

¹  Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel

²  Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel

author email corresponding author email

BMC Genomics 2007, 8:164doi:10.1186/1471-2164-8-164

Published:	12 June 2007

Abstract

Background

DNA microarrays provide the ability to interrogate multiple genes in a single experiment and have revolutionized genomic research. However, the microarray technology suffers from various forms of biases and relatively low reproducibility. A particular source of false data has been described, in which non-random placement of gene probes on the microarray surface is associated with spurious correlations between genes.

Results

In order to assess the prevalence of this effect and better understand its origins, we applied an autocorrelation analysis of the relationship between chromosomal position and expression level to a database of over 2000 individual yeast microarray experiments. We show that at least 60% of these experiments exhibit spurious chromosomal position-dependent gene correlations, which nonetheless appear in a stochastic manner within each experimental dataset. Using computer simulations, we show that large spatial biases caused in the microarray hybridization step and independently of printing procedures can exclusively account for the observed spurious correlations, in contrast to previous suggestions. Our data suggest that such biases may generate more than 15% false data per experiment. Importantly, spatial biases are expected to occur regardless of microarray design and over a wide range of microarray platforms, organisms and experimental procedures.

Conclusions

Spatial biases comprise a major source of noise in microarray studies; revision of routine experimental practices and normalizations to account for these biases may significantly and comprehensively improve the quality of new as well as existing DNA microarray data.