• A large-scale assessment of microarray experimental and analysis methods is the MicroArray Quality Control (MAQC) project being conducted under the auspices of the FDA. The September 2006 issue of Nature Biotechnology has a series of papers on the results.

• Other studies that explore the best ways to analyze microarray data include:

Barash, Y., E. Dehan, et al. (2004). "Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays." Bioinformatics 20(6): 839-846.

Broberg, P. (2003). "Statistical methods for ranking differentially expressed genes." Genome Biol 4(6): R41.

Harr, B. and C. Schlotterer (2006). "Comparison of algorithms for the analysis of Affymetrix microarray data as evaluated by co-expression of genes in known operons" Nucl. Acids Res. 34(2): e8-.

He, Y. D., H. Dai, et al. (2003). "Microarray standard data set and figures of merit for comparing data processing methods and experiment designs." Bioinformatics 19(8): 956-65.

Hu, Z. and G. R. Willsky (2006). "Utilization of two sample t-test statistics from redundant probe sets to evaluate different probe set algorithms in GeneChip studies." BMC Bioinformatics 7: 12.

Irizarry, R. A., Z. Wu, et al. (2006). "Comparison of Affymetrix GeneChip expression measures." Bioinformatics 22(7): 789-794.

Irizarry, R. A., B. M. Bolstad, et al. (2003). "Summaries of Affymetrix GeneChip probe level data." Nucleic Acids Res 31(4): e15.

Kennedy, R. E., K. J. Archer, et al. (2006). "Empirical Validation of the S-Score Algorithm in the Analysis of Gene Expression Data." BMC Bioinformatics 7(1): 154.

Lemon, W. J., S. Liyanarachchi, et al. (2003). "A high performance test of differential gene expression for oligonucleotide arrays." Genome Biol 4(10): R67.

Millenaar, F. F., J. Okyere, et al. (2006). "How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results." BMC Bioinformatics 7: 137.

Qin, L. X., R. P. Beyer, et al. (2006). "Evaluation of methods for oligonucleotide array data via quantitative real-time PCR." BMC Bioinformatics 7: 23.

Rajagopalan, D. (2003). "A comparison of statistical methods for analysis of high density oligonucleotide array data." Bioinformatics 19(12): 1469-76.

Shedden, K., W. Chen, et al. (2005). "Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data." BMC Bioinformatics 6(1): 26.

Wang, Y., C. Barbacioru, et al. (2006). "Large scale real-time PCR validation on gene expression measurements from two commercial long-oligonucleotide microarrays." BMC Genomics 7(1): 59.

• A set of papers published in 2005 in Nature Methods look at reproducibility of microarray results.

In an accompanying News & Views, Gavin Sherlock writes:

"The three papers in this issue provide a cautionary tale for microarray research, but also a reason for optimism as compared with earlier studies. They demonstrate that it is possible to perform microarray experiments that are reproducible between labs and across platforms, provided standard methodologies are adopted for best performance."

Access the papers from the May 2005 issue here:

Independence and reproducibility across microarray platforms pp337 - 344
Jennie E Larkin, Bryan C Frank, Haralambos Gavras, Razvan Sultana & John Quackenbush

Multiple-laboratory comparison of microarray platforms pp345 - 350
Rafael A Irizarry, Daniel Warren, et al.

Standardizing global gene expression analysis between laboratories and across platforms pp351 - 356
Members of the Toxicogenomics Research Consortium: B.K. Weis