An adaptive method for cDNA microarray normalization

doi:10.1186/1471-2105-6-28

BMC Bioinformatics

Volume 6

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Research article

An adaptive method for cDNA microarray normalization

Yingdong Zhao¹^* , Ming-Chung Li²^* and Richard Simon¹

¹Biometric Research Branch, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA

²The EMMES Corporation, Rockville, Maryland, USA

author email corresponding author email* Contributed equally

BMC Bioinformatics 2005, 6:28doi:10.1186/1471-2105-6-28


Published:	11 February 2005

Additional files

Additional File 3:

Figure 4: Bar plots show comparison of RMSE by using the adaptive method (black bar) and global method (grey bar) with simulated data generated from a mixture model with c = 1.5, a = 90, a₀= 120, γ = 8, γ₁= 6, and γ₂= 10 at three different noise levels (A) SD = 0; (B) SD = 0.25; and (C) SD = 0.50.

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Additional File 4:

Figure 5: Histograms and the estimated densities of log(ratio) and log(intensity) for a simulated data of a mixture model with c = 1.5, a = 90, a₀= 120, γ = 8, γ₁= 6, and γ₂= 10. The superimposed curve on each plot is generated from the fitted model.

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Additional File 2:

Equations 8A-8C: a three-component mixture model.

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Figure 6: Histograms and the estimated densities of log(ratio) and log(intensity) for a set of real data generated from array svcc109. The superimposed curve on each plot is generated from the fitted model. The procedure to generate the data was described in the paper and the sampling rate was shown in Table 3 [see 6].

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Table 3: Different number of genes sampled in each interval.

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Table 4: Comparison of RMSE by using the adaptive method and global method with real data by a different sampling method. The procedure to generate the data was described in the paper and the sampling rate was shown in Table 3 [see 6].

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Additional File 1:

Derivation of joint distributions of (R, G) for p_n, p_u, and p_o

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