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Multifactorial experimental design and the transitivity of ratios with spotted DNA microarrays.

Townsend JP - BMC Genomics (2003)

Bottom Line: Multifactorial experimental designs using DNA microarrays are becoming increasingly common, but the extent of the transitivity of cDNA microarray expression measurements across multiple samples has yet to be explored.A strong correlation between direct and transitive inference for significantly differentially expressed genes is demonstrated, using subsets of a dye-swap loop design.In experimental design, opportunities for transitive inference should be exploited, while always ensuring that comparisons of greatest interest comprise direct hybridizations.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Plant and Microbial Biology, 321 Koshland Hall, University of California, Berkeley, CA 94720, USA. townsend@nature.berkeley.edu

ABSTRACT

Background: Multifactorial experimental designs using DNA microarrays are becoming increasingly common, but the extent of the transitivity of cDNA microarray expression measurements across multiple samples has yet to be explored.

Results: A strong correlation between direct and transitive inference for significantly differentially expressed genes is demonstrated, using subsets of a dye-swap loop design.

Conclusions: In experimental design, opportunities for transitive inference should be exploited, while always ensuring that comparisons of greatest interest comprise direct hybridizations.

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Related in: MedlinePlus

Effect of SAGE mRNA count on coefficient of variation, average absolute log ratio, and signal intensity. SAGE yields data on the absolute expression level, in counts of mRNA per cell, of genes. A) The coefficient of variation estimated for a given gene does not generally appear to depend upon the absolute expression level. B) The average absolute log ratio does not generally appear to depend upon the absolute expression level. C) Intensity of a microarray spot does depend upon absolute expression level, at least for highly abundant genes. Error bars are +/- 2 standard errors of the mean. Larger error bars at higher SAGE levels are due to smaller numbers of genes at those levels and do not reflect a greater standard deviation of the measurements.
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Figure 5: Effect of SAGE mRNA count on coefficient of variation, average absolute log ratio, and signal intensity. SAGE yields data on the absolute expression level, in counts of mRNA per cell, of genes. A) The coefficient of variation estimated for a given gene does not generally appear to depend upon the absolute expression level. B) The average absolute log ratio does not generally appear to depend upon the absolute expression level. C) Intensity of a microarray spot does depend upon absolute expression level, at least for highly abundant genes. Error bars are +/- 2 standard errors of the mean. Larger error bars at higher SAGE levels are due to smaller numbers of genes at those levels and do not reflect a greater standard deviation of the measurements.

Mentions: Figure 5 relates absolute expression level (from SAGE data [20]) to the coefficient of variation, the average absolute log ratio, and the intensity of microarray spots. One might be concerned that genes with low gene expression level yield especially noisy data. However, the coefficient of variation for measurements of relative gene expression level does not vary across absolute gene expression levels (Figure 5A).


Multifactorial experimental design and the transitivity of ratios with spotted DNA microarrays.

Townsend JP - BMC Genomics (2003)

Effect of SAGE mRNA count on coefficient of variation, average absolute log ratio, and signal intensity. SAGE yields data on the absolute expression level, in counts of mRNA per cell, of genes. A) The coefficient of variation estimated for a given gene does not generally appear to depend upon the absolute expression level. B) The average absolute log ratio does not generally appear to depend upon the absolute expression level. C) Intensity of a microarray spot does depend upon absolute expression level, at least for highly abundant genes. Error bars are +/- 2 standard errors of the mean. Larger error bars at higher SAGE levels are due to smaller numbers of genes at those levels and do not reflect a greater standard deviation of the measurements.
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Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC239860&req=5

Figure 5: Effect of SAGE mRNA count on coefficient of variation, average absolute log ratio, and signal intensity. SAGE yields data on the absolute expression level, in counts of mRNA per cell, of genes. A) The coefficient of variation estimated for a given gene does not generally appear to depend upon the absolute expression level. B) The average absolute log ratio does not generally appear to depend upon the absolute expression level. C) Intensity of a microarray spot does depend upon absolute expression level, at least for highly abundant genes. Error bars are +/- 2 standard errors of the mean. Larger error bars at higher SAGE levels are due to smaller numbers of genes at those levels and do not reflect a greater standard deviation of the measurements.
Mentions: Figure 5 relates absolute expression level (from SAGE data [20]) to the coefficient of variation, the average absolute log ratio, and the intensity of microarray spots. One might be concerned that genes with low gene expression level yield especially noisy data. However, the coefficient of variation for measurements of relative gene expression level does not vary across absolute gene expression levels (Figure 5A).

Bottom Line: Multifactorial experimental designs using DNA microarrays are becoming increasingly common, but the extent of the transitivity of cDNA microarray expression measurements across multiple samples has yet to be explored.A strong correlation between direct and transitive inference for significantly differentially expressed genes is demonstrated, using subsets of a dye-swap loop design.In experimental design, opportunities for transitive inference should be exploited, while always ensuring that comparisons of greatest interest comprise direct hybridizations.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Plant and Microbial Biology, 321 Koshland Hall, University of California, Berkeley, CA 94720, USA. townsend@nature.berkeley.edu

ABSTRACT

Background: Multifactorial experimental designs using DNA microarrays are becoming increasingly common, but the extent of the transitivity of cDNA microarray expression measurements across multiple samples has yet to be explored.

Results: A strong correlation between direct and transitive inference for significantly differentially expressed genes is demonstrated, using subsets of a dye-swap loop design.

Conclusions: In experimental design, opportunities for transitive inference should be exploited, while always ensuring that comparisons of greatest interest comprise direct hybridizations.

Show MeSH
Related in: MedlinePlus