Scientific Publications by FDA Staff

BMC Bioinformatics 2005 Jul 15;6 Suppl 2:S12

Cross-platform comparability of microarray technology: intra-platform consistency and appropriate data analysis procedures are essential.

Shi L, Tong W, Fang H, Scherf U, Han J, Puri RK, Frueh FW, Goodsaid FM, Guo L, Su Z, Han T, Fuscoe JC, Xu ZA, Patterson TA, Hong H, Xie Q, Perkins RG, Chen JJ, Casciano DA

Shi LM, US FDA, Natl Ctr Toxicol Res, 3900 NCTR Rd, Jefferson, AR 72079 USA US FDA, Natl Ctr Toxicol Res, Jefferson, AR 72079 USA Z Tech Corp, Jefferson, AR 72079 USA US FDA, Ctr Devices & Radiol Hlth, Rockville, MD 20850 USA US FDA, Ctr Biol Evaluat & Res, Bethesda, MD 20892 USA US FDA, Ctr Drug Evaluat & Res, Bethesda, MD 20892 USA

BACKGROUND: The acceptance of microarray technology in regulatory decision-making is being challenged by the existence of various platforms and data analysis methods. A recent report (E. Marshall, Science, 306, 630-631, 2004), by extensively citing the study of Tan et al. (Nucleic Acids Res., 31, 5676-5684, 2003), portrays a disturbingly negative picture of the cross-platform comparability, and, hence, the reliability of microarray technology. RESULTS: We reanalyzed Tan's dataset and found that the intra-platform consistency was low, indicating a problem in experimental procedures from which the dataset was generated. Furthermore, by using three gene selection methods (i.e., p-value ranking, fold-change ranking, and Significance Analysis of Microarrays (SAM)) on the same dataset we found that p-value ranking (the method emphasized by Tan et al.) results in much lower cross-platform concordance compared to fold-change ranking or SAM. Therefore, the low cross-platform concordance reported in Tan's study appears to be mainly due to a combination of low intra-platform consistency and a poor choice of data analysis procedures, instead of inherent technical differences among different platforms, as suggested by Tan et al. and Marshall. CONCLUSION: Our results illustrate the importance of establishing calibrated RNA samples and reference datasets to objectively assess the performance of different microarray platforms and the proficiency of individual laboratories as well as the merits of various data analysis procedures. Thus, we are progressively coordinating the MAQC project, a community-wide effort for microarray quality control.