Making Informed Choices about Microarray Data Analysis

High-throughput methods are revolutionizing biological research, and numerous published articles describe innovative insights obtained through analysis of microarray data. Microarray technologies are now available for measuring gene expression, DNA copy number, methylation, chromatin state, protein binding, SNPs, and other aspects of gene physiology. To address the needs of many biomedical researchers who do not often have the resources to perform sophisticated data manipulation, several companies and institutions have prepared pre-packaged software to guide the researcher through and perform all the steps of standard microarray analysis. Commercial packages include Genespring, Nexus (from Biodiscovery), GeneSifter (from Geospiza), Expressionist (from Genedata), Partek Genomics Suite, and many others. Several institutional packages are described at the end of this article. With such packaged software readily available, who really needs to think about microarray analysis or to collaborate with array analysis specialists? The premise of this review is that innovative microarray analyses are rarely straightforward and that most analyses present problems and opportunities that are not readily identified or adequately addressed by packaged, comprehensive software. For example, one common problem in my experience is that common quality assessment (QA) practices may be unable to eliminate all seriously compromised chips. Furthermore, the current best practices for expression arrays don't work well for many of the new kinds of arrays (see Text S1, section S2). Certainly, commercial software has valuable uses; e.g., the quality and flexibility of commercial graphics programming for data visualization that outstrips that of open-source software. However, this review proposes a Malthusian maxim for microarrays: that the number of potential complications in high-throughput biology grows exponentially, while the expertise embodied in packaged software has grown only linearly. Thus, a researcher must know enough about high-throughput methods to ascertain when commercialized software can be helpful and when an expert in microarray analysis should be consulted. Most data analyses consist of the steps outlined in Figure 1. This review will follow these steps. The last two steps (significance tests and biological interpretation) and exploratory analysis are not covered here. A complementary approach to some of the early stages of a study was presented recently in [1]. Figure 1 Steps in a typical microarray analysis. Experimental Design Researchers know good data depends on good experimental design. There are several design issues that benefit from statistical thinking, but the most prominent issue is whether there will be enough samples to find most of the genes that are changed. Most researchers are aware of the multiple comparisons issue; when they perform a straightforward power calculation, they know to set the target significance threshold much lower than it would be for single tests. However, the actual level of significance one would need to detect a significant difference after standard Bonferroni multiple comparisons adjustments to the p-value would be so small that an experiment would require far too many samples to be practical. For this reason, in practice most researchers use the false discovery rate (FDR) [2] to assess significance in data analysis. It makes sense, therefore, to perform power calculations using FDR, but such a calculation is not so easy to perform. Another approach to power estimation is to make an analogy with published data to estimate the power of one's own study; such an approach is implemented in the Microarray Power Atlas ([3] and http://www.poweratlas.org). A general principle of experimental design is to make comparisons under circumstances as closely matched as possible. A second issue that has tripped up many researchers is that arrays processed at different times or by different technicians often show pronounced batch differences [4], [5]. For example, there may be different dynamic ranges in different batches, or certain subsets of probes may show greater signal independent of biological differences. Thus, it is important to try to process all the arrays under as close to identical conditions as possible, or, if that is impossible, to randomize the assignment of samples to processing times. Variations in ozone concentration make a significant difference (R. Lucito, E. Ljungstrom, personal communications; [6], [7]). In designing two-color array experiments, the most common advice is to make contrasts that are the most informative [1], [8]. Such a design helps to maximize the information available in a fixed number of arrays. For various reasons, such as flexibility in the face of hybridization failures, and in order to allow comparisons between present and possible future experiments, many experimenters choose to co-hybridize both case/treatment and control samples with a common reference sample. In keeping with the general principle in the previous paragraph, it is better not to co-hybridize samples that are expected to differ markedly, because extreme ratios of gene expression, far from one, usually have much larger errors. Therefore, if one is using a common reference, it is better to use a reference from a tissue similar to the samples under study, rather than a reference from a different tissue.