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Adaptable gene-specific dye bias correction for two-channel DNA microarrays.

Margaritis T, Lijnzaad P, van Leenen D, Bouwmeester D, Kemmeren P, van Hooff SR, Holstege FC - Mol. Syst. Biol. (2009)

Bottom Line: A correction method (Gene- And Slide-Specific Correction, GASSCO) is presented, whereby sequence-specific corrections are modulated by the overall bias of individual hybridizations.GASSCO outperforms earlier methods and works well on a variety of publically available datasets covering a range of platforms, organisms and applications, including ChIP on chip.Software implementing the method is publicly available.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiological Chemistry, University Medical Center Utrecht, Universiteitsweg, Utrecht, The Netherlands.

ABSTRACT
DNA microarray technology is a powerful tool for monitoring gene expression or for finding the location of DNA-bound proteins. DNA microarrays can suffer from gene-specific dye bias (GSDB), causing some probes to be affected more by the dye than by the sample. This results in large measurement errors, which vary considerably for different probes and also across different hybridizations. GSDB is not corrected by conventional normalization and has been difficult to address systematically because of its variance. We show that GSDB is influenced by label incorporation efficiency, explaining the variation of GSDB across different hybridizations. A correction method (Gene- And Slide-Specific Correction, GASSCO) is presented, whereby sequence-specific corrections are modulated by the overall bias of individual hybridizations. GASSCO outperforms earlier methods and works well on a variety of publically available datasets covering a range of platforms, organisms and applications, including ChIP on chip. A sequence-based model is also presented, which predicts which probes will suffer most from GSDB, useful for microarray probe design and correction of individual hybridizations. Software implementing the method is publicly available.

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Related in: MedlinePlus

Correcting GSDB in previously published studies. Examples of GSDB determined from earlier studies that included dye swaps or self versus self hybridizations. Graphs as in Figure 1; left: before correction; right: after correction. In the left panel, the relevant publication, the organism and the platform. In the right panel, the number of hybridizations corrected and the achieved average and maximum performance.
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f3: Correcting GSDB in previously published studies. Examples of GSDB determined from earlier studies that included dye swaps or self versus self hybridizations. Graphs as in Figure 1; left: before correction; right: after correction. In the left panel, the relevant publication, the organism and the platform. In the right panel, the number of hybridizations corrected and the achieved average and maximum performance.

Mentions: We compared the performance of GASSCO with the latest available one, VERA (Kelley et al, 2008), as well as with simply averaging dye swaps (Figure 1K and L), using the data presented in Figure 1C. As both VERA and averaging result in a single merged dataset, we also averaged our individually GSDB-corrected hybridizations for the sake of comparison. The results show clearly that GASSCO outperforms both methods. Importantly, as GASSCO corrects hybridizations individually (Figure 1A–J), it has the additional advantage of conserving statistical power, which is lost by methods that merge. The new method is also computationally less challenging than VERA (500-fold faster in our tests). As the degree of GSDB is associated with labelling efficiency (Figure 1E), both simple merging of dye swaps and methods that do not take into account the slide dependency of dye bias will only work well if label incorporation is identical between dye-swap replicates. In this study, ‘control' of sample labelling efficiency (Figure 1E) was only achieved by performing a number of reactions under varying conditions, matching labelled samples that happened to have the same incorporation. In practice, it is too challenging to control the degree of label incorporation precisely enough, especially for prolonged projects, which is one reason why the degree of GSDB varies (see Figure 3 for other examples of this variation from independent studies).


Adaptable gene-specific dye bias correction for two-channel DNA microarrays.

Margaritis T, Lijnzaad P, van Leenen D, Bouwmeester D, Kemmeren P, van Hooff SR, Holstege FC - Mol. Syst. Biol. (2009)

Correcting GSDB in previously published studies. Examples of GSDB determined from earlier studies that included dye swaps or self versus self hybridizations. Graphs as in Figure 1; left: before correction; right: after correction. In the left panel, the relevant publication, the organism and the platform. In the right panel, the number of hybridizations corrected and the achieved average and maximum performance.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2683724&req=5

f3: Correcting GSDB in previously published studies. Examples of GSDB determined from earlier studies that included dye swaps or self versus self hybridizations. Graphs as in Figure 1; left: before correction; right: after correction. In the left panel, the relevant publication, the organism and the platform. In the right panel, the number of hybridizations corrected and the achieved average and maximum performance.
Mentions: We compared the performance of GASSCO with the latest available one, VERA (Kelley et al, 2008), as well as with simply averaging dye swaps (Figure 1K and L), using the data presented in Figure 1C. As both VERA and averaging result in a single merged dataset, we also averaged our individually GSDB-corrected hybridizations for the sake of comparison. The results show clearly that GASSCO outperforms both methods. Importantly, as GASSCO corrects hybridizations individually (Figure 1A–J), it has the additional advantage of conserving statistical power, which is lost by methods that merge. The new method is also computationally less challenging than VERA (500-fold faster in our tests). As the degree of GSDB is associated with labelling efficiency (Figure 1E), both simple merging of dye swaps and methods that do not take into account the slide dependency of dye bias will only work well if label incorporation is identical between dye-swap replicates. In this study, ‘control' of sample labelling efficiency (Figure 1E) was only achieved by performing a number of reactions under varying conditions, matching labelled samples that happened to have the same incorporation. In practice, it is too challenging to control the degree of label incorporation precisely enough, especially for prolonged projects, which is one reason why the degree of GSDB varies (see Figure 3 for other examples of this variation from independent studies).

Bottom Line: A correction method (Gene- And Slide-Specific Correction, GASSCO) is presented, whereby sequence-specific corrections are modulated by the overall bias of individual hybridizations.GASSCO outperforms earlier methods and works well on a variety of publically available datasets covering a range of platforms, organisms and applications, including ChIP on chip.Software implementing the method is publicly available.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiological Chemistry, University Medical Center Utrecht, Universiteitsweg, Utrecht, The Netherlands.

ABSTRACT
DNA microarray technology is a powerful tool for monitoring gene expression or for finding the location of DNA-bound proteins. DNA microarrays can suffer from gene-specific dye bias (GSDB), causing some probes to be affected more by the dye than by the sample. This results in large measurement errors, which vary considerably for different probes and also across different hybridizations. GSDB is not corrected by conventional normalization and has been difficult to address systematically because of its variance. We show that GSDB is influenced by label incorporation efficiency, explaining the variation of GSDB across different hybridizations. A correction method (Gene- And Slide-Specific Correction, GASSCO) is presented, whereby sequence-specific corrections are modulated by the overall bias of individual hybridizations. GASSCO outperforms earlier methods and works well on a variety of publically available datasets covering a range of platforms, organisms and applications, including ChIP on chip. A sequence-based model is also presented, which predicts which probes will suffer most from GSDB, useful for microarray probe design and correction of individual hybridizations. Software implementing the method is publicly available.

Show MeSH
Related in: MedlinePlus