About MAQC
Microarrays and next-generation sequencing represent core technologies in pharmacogenomics and toxicogenomics; however, before these technologies can successfully and reliably be used in clinical practice and regulatory decision-making, standards and quality measures need to be developed. The MAQC project is helping improve the microarray and next-generation sequencing technologies and foster their proper applications in discovery, development and review of FDA regulated products. Everyone is invited to participate in the MAQC project.
It is the FDA-led community wide consortium effort to address issues relating to the application of constantly evolving high-throughput genomics technologies to either assess safety and efficacy of FDA regulated products or their safe and effective use in clinical applications as in vitro diagnostic devices. The MAQC consortium completed three projects between 2005 -2014 (namely MAQC I, II and III), resulting in ~30 publications and one third of which were published in Nature Biotechnology. Furthermore, two papers of these papers were among the most cited in Nature Biotechnology in the last 20 years.
More information is here
List of publications resulting from the MAQC project is here
What's Available in the Technical Reference Library
What's Good About It
- These are examples of wide data and will be useful showing JMP's increased capability analyzing wide data problems
- The data is real and produces meaningful discoveries. For example, using the breast cancer data, perform a PCA, save the top 10 principal components and use them with GenReg to predict Estrogen Receptor status. I'm getting an R-square value of 75%.
- The project was very productive generating over 30 publications (see link above). Two of these papers were among the most cited in Nature Biotechnology in the last 20 years. Contributors include our own Russ Wolfinger (and Kelci Miclaus).
- The study had 4 phases and the data loaded into the library is from phase 2, MAQC-II. Phase 2 used micro arrays and they have similarities with semiconductor chips. Although the outcomes (cancer research) are biological, the technique will resonate with folks from semiconductor, which also has wide data problems.
