The take-home message from the MicroArray Quality Control (MAQC) consortium was that microarrays can generate reproducible gene expression data, both across platforms and across laboratories. But it's not the last word in array performance issues.
The consortium set out neither to establish industry-wide standard operating procedures and algorithms, nor to establish quality metrics you can use to measure your own performance. Its data represent a best-case scenario, collected mostly using two RNA samples that vary far more than those researchers are likely to encounter in their own labs. "You're defining a ceiling for how well things should perform," says Marc Salit of the National Institute of Standards and Technology, who was involved in the project as a consultant. "The important thing is that people not draw conclusions that these are the results they should expect in their labs when working with difficult biological samples."
If you've been reluctant to plunge ...
