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Using RNA sample titrations to assess microarray platform performance and normalization techniques


We have assessed the utility of RNA titration samples for evaluating microarray platform performance and the impact of different normalization methods on the results obtained. As part of the MicroArray Quality Control project, we investigated the performance of five commercial microarray platforms using two independent RNA samples and two titration mixtures of these samples. Focusing on 12,091 genes common across all platforms, we determined the ability of each platform to detect the correct titration response across the samples. Global deviations from the response predicted by the titration ratios were observed. These differences could be explained by variations in relative amounts of messenger RNA as a fraction of total RNA between the two independent samples. Overall, both the qualitative and quantitative correspondence across platforms was high. In summary, titration samples may be regarded as a valuable tool, not only for assessing microarray platform performance and different analysis methods, but also for determining some underlying biological features of the samples.

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Figure 1: RNA samples.
Figure 2: Percentage of genes showing the monotonic titration responses Āi > i > i > i and i > i > i > Āi plotted against the linear Āi / i and i / Āi ratios, respectively, for each commercial whole-genome microarray platform, using various normalization methods.
Figure 3: Impact of normalization on the distributions of titrating genes as a function of signal intensity.
Figure 4: Titration-response concordance for each commercial whole-genome microarray platform, using different normalization methods, with data from each platform separated by site and fold-change direction.


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This study used a number of computing resources, including the high-performance computational capabilities of the Biowulf PC/Linux cluster at the US National Institutes of Health in Bethesda, Maryland ( This research was supported in part by the Intramural Research Program of the US National Institutes of Health, National Library of Medicine.

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Correspondence to Richard Shippy.

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Competing interests

The following authors declare competing financial interests: R.S., S.F-S., P.W., X.G., E.C., Y.A.S., R.A.S., A.B.L., N.N., Y.T., S.C.B., & J.A.W. In addition, J.C.W. is a consultant for and has significant financial interest in Gene Express, Inc.

Supplementary information

Supplementary Fig. 1

Linear regression fitting of Ci/Bi vs. Ai/Bi for each platform, normalization method and site to estimate the true mixture coefficients αcc for the C sample from the data. (DOC 773 kb)

Supplementary Fig. 2

Illustration providing a possible explanation for the asymmetry (i.e., median A>B (βC) ≠ medianA<B (βC) and median A>B (βD) ≠ medianA<B (βD)) in the box plots on either side of Figure 4 (particularly the differences in median values). (DOC 60 kb)

Supplementary Fig. 3

Percentage of genes achieving the specified level of apparent power between the RNA titration mixtures and independent samples. (DOC 56 kb)

Supplementary Fig. 4

Impact of different normalization methods on signal CVs for each microarray platform separated by site and sample. (DOC 429 kb)

Supplementary Fig. 5

Derivation of formulas within Box 1 to clarify calculations for mRNA fractions. (DOC 42 kb)

Supplementary Table 1

Gene counts for each commercial whole genome microarray platform separated by site. (DOC 43 kb)

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Shippy, R., Fulmer-Smentek, S., Jensen, R. et al. Using RNA sample titrations to assess microarray platform performance and normalization techniques. Nat Biotechnol 24, 1123–1131 (2006).

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