Making the Choice
The good news for scientists is that there really weren’t such huge differences across the various platforms in the study. This can also be a bit of bad news. After all, how is a scientist to choose?
“Seeing as there isn’t a huge technological difference among the platforms, the primary driver is going to be value,” Baker adds. “Scientifically valid results will happen no matter which platform you use, but it’s going to be the platform that gives you the most value that will emerge in the forefront. Choosing a gene-expression platform should be driven by factors such as technical performance, cost, usability, input requirements, and content quality. And when reagents cost less, that allows experimental designs to be expanded, yielding more far-reaching results with the same research budget.
“I think it’s fair to say that we are all pleased with the results,” Wolber remarks. “The FDA took on this study and took it upon itself to clear the air with regard to this technology and putting standards in place. Microarray technology has improved tremendously over the last four years, and this study was a crucial step that demonstrates that this is a method that is really growing up.”
Shippy agrees. “This study also brings value to the array companies, in that now they have access to a thoroughly characterized baseline data set on all major expression profiling platforms. Array companies will be better able to further advance their products, through assay and platform modifications, which should mature the technology even further.
“The study also shows that data-analysis methods make a difference,” Wolber also cautions. “This study explored only a subset of many possible analysis methods, so the first choice you might make, based on the initial publications, might not be the best. However, the data is now freely available to the public and should drive future improvements in data analysis.”
Affymetrix (www.affymetrix.com) also participated in this joint effort. “The study was designed to demonstrate that by practicing good scientific methods in the laboratory, one can obtain tight, reproducible data,” says Janet Warrington, vp, emerging markets and molecular diagnostics R&D.
Warrington notes that the samples that were used in the study are probably now the most well-characterized RNA libraries, by microarray and RT-PCR assay, commercially available.
“No standard controls or guidelines per se resulted from this study,” Warrington says. “But hopefully, it will contribute to a better understanding of the important decisions that one makes in designing and executing an experiment. It is important to choose the appropriate algorithm or algorithms that correspond with the experimental question that one is trying to address.
“We hope that the results will build confidence that when scientists design adequately powered experiments, use good laboratory practices, and select and implement algorithms carefully that they will obtain highly reproducible, robust results,” Warrington concludes. “Strong scientific methods are required to generate good results.”
The biggest winner in the MAQC Project, Shippy notes, was the scientific community. “With this project, we have raised the profile of microarray technology, we have received the FDA stamp of approval, and now we are fully positioned to advance this field. In the short term, we’ve created the data set, the RNA material, and, of course, the papers. Personally, the most enjoyable aspect for me was working with the best and the brightest minds in the scientific field to provide novel ideas on how to handle and interpret microarray data.”
Another short-term gain is the ability to now assess performance of the platforms. “Normalization plays a critical role for successful microarray experiments,” Shippy adds. “And now we know that all of the microarray platforms are viable.”
“Scientists will try out the recommendations of the consortium in their microarray studies,” Goodsaid notes. “We will then know what the long-term impact of these recommendations will be.”