Abstract
Although they have become a widely used experimental technique for identifying differentially expressed (DE) genes, DNA microarrays are notorious for generating noisy data. A common strategy for mitigating the effects of noise is to perform many experimental replicates. This approach is often costly and sometimes impossible given limited resources; thus, analytical methods are needed which increase accuracy at no additional cost. One inexpensive source of microarray replicates comes from prior work: to date, data from hundreds of thousands of microarray experiments are in the public domain. Although these data assay a wide range of conditions, they cannot be used directly to inform any particular experiment and are thus ignored by most DE gene methods. We present the SVD Augmented Gene expression Analysis Tool (SAGAT), a mathematically principled, data-driven approach for identifying DE genes. SAGAT increases the power of a microarray experiment by using observed coexpression relationships from publicly available microarray datasets to reduce uncertainty in individual genes' expression measurements. We tested the method on three well-replicated human microarray datasets and demonstrate that use of SAGAT increased effective sample sizes by as many as 2.72 arrays. We applied SAGAT to unpublished data from a microarray study investigating transcriptional responses to insulin resistance, resulting in a 50% increase in the number of significant genes detected. We evaluated 11 (58%) of these genes experimentally using qPCR, confirming the directions of expression change for all 11 and statistical significance for three. Use of SAGAT revealed coherent biological changes in three pathways: inflammation, differentiation, and fatty acid synthesis, furthering our molecular understanding of a type 2 diabetes risk factor. We envision SAGAT as a means to maximize the potential for biological discovery from subtle transcriptional responses, and we provide it as a freely available software package that is immediately applicable to any human microarray study.
Highlights
Since their inception over 13 years ago [1], DNA microarrays have become a staple experimental tool used primarily for exploring the effects of biological interventions on gene expression
We have developed the Singular Value Decomposition (SVD) Augmented Gene expression Analysis Tool (SAGAT) for identifying differentially expressed (DE) genes
We explore SAGAT’s ability to improve DE gene identification on simulated data, and we validate the method on three highly replicated biological datasets
Summary
Since their inception over 13 years ago [1], DNA microarrays have become a staple experimental tool used primarily for exploring the effects of biological interventions on gene expression. Microarrays have enabled a range of experimental queries, including a survey of gene expression across dozens of mammalian tissues [2], a comparison of human cancers in over 2000 tumor samples [3], and the identification of differentially expressed (DE) genes between pairs of conditions. As of 2009, there are publicly available microarray data for w2400 human conditions (at the Gene Expression Omnibus [4]). These data make possible a huge number of pairwise comparisons for DE gene analysis. Given this sizable opportunity for biological discovery, we focus our attention on the task of DE gene identification
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