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Analyzing 2D gel images using a two-component empirical Bayes model.

Li F, Seillier-Moiseiwitsch F - BMC Bioinformatics (2011)

Bottom Line: The estimation of the mixture density does not take into account assumptions about the density.The proposed constrained estimation method always yields valid estimates and more stable results.The proposed estimation approach proposed can be applied to other contexts where large-scale hypothesis testing occurs.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, MD, USA. feng.li@fda.hhs.gov

ABSTRACT

Background: Two-dimensional polyacrylomide gel electrophoresis (2D gel, 2D PAGE, 2-DE) is a powerful tool for analyzing the proteome of a organism. Differential analysis of 2D gel images aims at finding proteins that change under different conditions, which leads to large-scale hypothesis testing as in microarray data analysis. Two-component empirical Bayes (EB) models have been widely discussed for large-scale hypothesis testing and applied in the context of genomic data. They have not been implemented for the differential analysis of 2D gel data. In the literature, the mixture and densities of the test statistics are estimated separately. The estimation of the mixture density does not take into account assumptions about the density. Thus, there is no guarantee that the estimated component will be no greater than the mixture density as it should be.

Results: We present an implementation of a two-component EB model for the analysis of 2D gel images. In contrast to the published estimation method, we propose to estimate the mixture and densities simultaneously using a constrained estimation approach, which relies on an iteratively re-weighted least-squares algorithm. The assumption about the density is naturally taken into account in the estimation of the mixture density. This strategy is illustrated using a set of 2D gel images from a factorial experiment. The proposed approach is validated using a set of simulated gels.

Conclusions: The two-component EB model is a very useful for large-scale hypothesis testing. In proteomic analysis, the theoretical density is often not appropriate. We demonstrate how to implement a two-component EB model for analyzing a set of 2D gel images. We show that it is necessary to estimate the mixture density and empirical component simultaneously. The proposed constrained estimation method always yields valid estimates and more stable results. The proposed estimation approach proposed can be applied to other contexts where large-scale hypothesis testing occurs.

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A simulated gel image from group 2. A simulated gel image from group 2. The 11 altered spots are circled.
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Figure 6: A simulated gel image from group 2. A simulated gel image from group 2. The 11 altered spots are circled.

Mentions: To further validate the proposed approach, we analyzed a set of simulated 2D gel images, which was generated by randomly perturbing an actual gel image as described in [29]. The 20 simulated gels were divided into two groups of 10. To simulate the group (treatment or intervention) effect, we artificially altered 11 manually selected spots such that these 11 spots were significantly differentially expressed across groups. Figures 5 and 6 show two simulated gel images from different groups with the 11 altered spots circled. The test statistics for the 147 spots were obtained using the RegStatGel software. We applied both estimation approaches. The results are shown in Figure 7. The interval [-2.5, 2.5] was used for estimating the component. The left column shows the results from the CME approach without constraint and the right column shows the results from the proposed constrained approach. The upper panel shows the histogram of z values, the estimated mixture density (solid green curve) and the empirical (blue dashed curve). The lower panel shows the estimated local FDR from each approach. The '+' signs in the lower panel locate the observed points. Both approaches identified all and only the 11 spots. Both approaches yield local FDR estimates for the 11 spots much lower than for the other proteins. Again, the unconstrained approach shows a bizarre local FDR curve.


Analyzing 2D gel images using a two-component empirical Bayes model.

Li F, Seillier-Moiseiwitsch F - BMC Bioinformatics (2011)

A simulated gel image from group 2. A simulated gel image from group 2. The 11 altered spots are circled.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3300069&req=5

Figure 6: A simulated gel image from group 2. A simulated gel image from group 2. The 11 altered spots are circled.
Mentions: To further validate the proposed approach, we analyzed a set of simulated 2D gel images, which was generated by randomly perturbing an actual gel image as described in [29]. The 20 simulated gels were divided into two groups of 10. To simulate the group (treatment or intervention) effect, we artificially altered 11 manually selected spots such that these 11 spots were significantly differentially expressed across groups. Figures 5 and 6 show two simulated gel images from different groups with the 11 altered spots circled. The test statistics for the 147 spots were obtained using the RegStatGel software. We applied both estimation approaches. The results are shown in Figure 7. The interval [-2.5, 2.5] was used for estimating the component. The left column shows the results from the CME approach without constraint and the right column shows the results from the proposed constrained approach. The upper panel shows the histogram of z values, the estimated mixture density (solid green curve) and the empirical (blue dashed curve). The lower panel shows the estimated local FDR from each approach. The '+' signs in the lower panel locate the observed points. Both approaches identified all and only the 11 spots. Both approaches yield local FDR estimates for the 11 spots much lower than for the other proteins. Again, the unconstrained approach shows a bizarre local FDR curve.

Bottom Line: The estimation of the mixture density does not take into account assumptions about the density.The proposed constrained estimation method always yields valid estimates and more stable results.The proposed estimation approach proposed can be applied to other contexts where large-scale hypothesis testing occurs.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, MD, USA. feng.li@fda.hhs.gov

ABSTRACT

Background: Two-dimensional polyacrylomide gel electrophoresis (2D gel, 2D PAGE, 2-DE) is a powerful tool for analyzing the proteome of a organism. Differential analysis of 2D gel images aims at finding proteins that change under different conditions, which leads to large-scale hypothesis testing as in microarray data analysis. Two-component empirical Bayes (EB) models have been widely discussed for large-scale hypothesis testing and applied in the context of genomic data. They have not been implemented for the differential analysis of 2D gel data. In the literature, the mixture and densities of the test statistics are estimated separately. The estimation of the mixture density does not take into account assumptions about the density. Thus, there is no guarantee that the estimated component will be no greater than the mixture density as it should be.

Results: We present an implementation of a two-component EB model for the analysis of 2D gel images. In contrast to the published estimation method, we propose to estimate the mixture and densities simultaneously using a constrained estimation approach, which relies on an iteratively re-weighted least-squares algorithm. The assumption about the density is naturally taken into account in the estimation of the mixture density. This strategy is illustrated using a set of 2D gel images from a factorial experiment. The proposed approach is validated using a set of simulated gels.

Conclusions: The two-component EB model is a very useful for large-scale hypothesis testing. In proteomic analysis, the theoretical density is often not appropriate. We demonstrate how to implement a two-component EB model for analyzing a set of 2D gel images. We show that it is necessary to estimate the mixture density and empirical component simultaneously. The proposed constrained estimation method always yields valid estimates and more stable results. The proposed estimation approach proposed can be applied to other contexts where large-scale hypothesis testing occurs.

Show MeSH
Related in: MedlinePlus