By Mo Krochmal
To graphically illustrate concordance when comparing microarray platforms, scientists and industry executives often rely on the trusty old Venn diagram. But that’s not sufficient, according to an Affymetrix scientist speaking at the Macroresults for Microarrays conference in Boston earlier this year.
“Venn diagrams are too limited a way to look at things,” said Tom Ryder, senior director of assay development for Affymetrix, during a panel discussion on standardization and validation at the close of the two-day conference. Ryder says that alternate methods of data presentation should provide gradations of significance to address a need for “new analytical tools to define if information is coalescing to tell a common story.”
Margaret Cam, director of the microarray core facility for the National Institute of Diabetes and Digestive and Kidney Diseases of the NIH, has done just that. Her group has been preparing to submit a cross-platform study to a peer-reviewed journal. The information, which does include Venn diagrams and rank-order methods, was originally presented in a poster at a microarray conference held in Zurich this winter.
“Being a core facility, we need to make direct comparisons across array platforms, or it will be too difficult to analyze anybody’s array data,” Cam says. “Eventually, the industry will have to standardize to some extent. The commercial outfits will have to start thinking about standardization and find ways to help investigators make more sense of the data.”
The conference discussion was spurred by Venn diagrams comparing data produced by the various microarray platforms, which seem typically to contain small areas of overlap. What these representations show is that microarrays are like jazz: They produce a magnificent sound, in this case, of scientific discovery, within a framework of individual instrumentalists riffing on a theme.
The industry, however, needs to metaphorically seek the standards of a symphony, with each note rigorously following a composer’s score and the baton of the conductor if it is to create consistently reproducible results.
At a separate session, Andrew Brooks, an assistant professor at the University of Rochester Medical Center and the director of the Functional Genomics Center, presented results from a study conducted on some 4,000 microarrays from different institutions and companies, which were analyzed to assess variability. According to Brooks, the biggest source of data variability came from processes in the individual laboratories themselves, rather than variability introduced in the manufacturing of the arrays. “Lab-to-lab variation is the greatest source of error,” he says.
Stephen Tirrell, director of transcriptional profiling for Millennium Pharmaceuticals, which uses the Affymetrix platform, suggests that researchers could minimize variability by taking extreme effort to document processes, workflows, standard operating procedures and specifications. The minimum information about a microarray experiment, or MIAME standard, addresses some of these issues, but doesn’t necessarily touch on technical bias or other fundamental problems. Adds Brooks: “With MIAME, we have a point of reference, but we need to go beyond that.”
The pharmaceutical industry, perhaps the leading consumer of microarray chips and instrumentation, may become the impetus for standardization under the influence of the FDA, which is starting to show an interest in genomic data, recently releasing a draft document, “Multiplex Tests for Heritable DNA Markers, Mutations and Expression Patterns; Draft Guidance for Industry and FDA Reviewers.” (http://www.fda.gov/cdrh/oivd/guidance/ 1210.pdf). The review period will be followed by implementation, but that, perhaps, is years away.
In a narrower time frame, the National Institute of Standards and Technology is working to create a gold standard slide to calibrate microarray scanners, says Dile Holton, product manager for PerkinElmer Life Sciences. NIST is collaborating with scanner manufacturers and substrate makers to design a standard that will represent typical fluorescent microarray intensity values for CY3 and CY5 to allow standardized comparative measurements for sensitivity and uniformity between scanner technologies. The group hopes to have a final slide available later this year, Holton said in a microarray news group post.
Perhaps until standardization becomes a reality, researchers might just need to hum the melody.