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Zhang et al. respond: Our study showed that the log-transformed gene expression level estimated by the PerfectMatch algorithm is linearly related to the log-transformed nominal

concentration. This log-linear relationship has a slope <1, which leads to an underestimation of fold-change in expression, as noted in our paper. The bias is easily correctable by

rescaling the log-transformed gene expression level by a fixed factor, according to the slope. Our analysis of the spike-in data set _al_lowed us to calibrate this factor to be around 2.

Because the slope bias appears to be consistent for all spike-in genes, this factor is expected to be generally applicable so that it is unnecessary to recalibrate it in routine use of the

technology. As long as the log-linear relationship holds, the slope bias _per se_ should have no effect on our power to identify differentially expressed genes, nor should it change the

shape of gene expression profiles. Therefore, the slope bias doesn't seem to us the most important issue in practical studies of gene expression. We think it is more relevant to

evaluate algorithms in terms of sensitivity and specificity in the context of identifying differential gene expression. A commonly used tool for this purpose is AUC. In a previous

publication by Irizarry's group, Cope _et al_.1 compute AUC defined as area under the Receiver operating characteristic (ROC) curve up to 100 false positives for GCRMA, RMA, MAS 5.0,

dChip and PerfectMatch algorithms and find AUC values of 0.82, 0.82, 0.36, 0.67, and 0.84, respectively2. These results indicate that GCRMA, RMA and PerfectMatch gave comparable

performances, whereas MAS 5.0 and dChip performed poorly. However, these results are inconsistent with Supplementary Figure 2 of Irizarry's correspondence. The origin of the

inconsistency is not clear because of insufficient information given in the correspondence. For a more detailed discussion of the related issues, please see our own Supplementary Note online

together with Supplementary Figure 1. _Note: _ _Supplementary information_ _ is available on the Nature Biotechnology website_. See Preprocessing of oligonucleotide array data by Irizarry

_et al._ REFERENCES * Cope, L.M., Irizarry, R.A., Jaffee, H.A., Wu, Z. & Speed TP. _Bioinformatics_ 20, 323–331 (2004). Article  CAS  Google Scholar  *

http://affycomp.biostat.jhsph.edu/AFFY2/TABLES.hgu/0.html Download references AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Biostatistics and Applied Mathematics, the

University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Box 447, Houston, 77030, Texas, USA Li Zhang, Chunlei Wu, Roberto Carta, Keith Baggerly & Kevin R Coombes Authors * Li

Zhang View author publications You can also search for this author inPubMed Google Scholar * Chunlei Wu View author publications You can also search for this author inPubMed Google Scholar *

Roberto Carta View author publications You can also search for this author inPubMed Google Scholar * Keith Baggerly View author publications You can also search for this author inPubMed 

Google Scholar * Kevin R Coombes View author publications You can also search for this author inPubMed Google Scholar SUPPLEMENTARY INFORMATION SUPPLEMENTARY NOTE SUPPLEMENTARY FIGURE 1

RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Zhang, L., Wu, C., Carta, R. _et al._ Response to Preprocessing of oligonucleotide array data. _Nat

Biotechnol_ 22, 658 (2004). https://doi.org/10.1038/nbt0604-658 Download citation * Issue Date: 01 June 2004 * DOI: https://doi.org/10.1038/nbt0604-658 SHARE THIS ARTICLE Anyone you share

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