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Statistical methodology for the analysis of dye-switch microarray experiments.

Mary-Huard T, Aubert J, Mansouri-Attia N, Sandra O, Daudin JJ - BMC Bioinformatics (2008)

Bottom Line: These procedures are compared with the usual ML and REML mixed model procedures proposed in most statistical toolboxes, on both simulated and real data.The UP procedure we propose as an alternative to usual mixed model procedures is more efficient and significantly faster to compute.This result provides some useful guidelines for the analysis of complex designs.

View Article: PubMed Central - HTML - PubMed

Affiliation: UMR AgroParisTech/INRA 518, 16, rue Claude Bernard 75231 Paris CEDEX 05, France. maryhuar@agroparistech.fr

ABSTRACT

Background: In individually dye-balanced microarray designs, each biological sample is hybridized on two different slides, once with Cy3 and once with Cy5. While this strategy ensures an automatic correction of the gene-specific labelling bias, it also induces dependencies between log-ratio measurements that must be taken into account in the statistical analysis.

Results: We present two original statistical procedures for the statistical analysis of individually balanced designs. These procedures are compared with the usual ML and REML mixed model procedures proposed in most statistical toolboxes, on both simulated and real data.

Conclusion: The UP procedure we propose as an alternative to usual mixed model procedures is more efficient and significantly faster to compute. This result provides some useful guidelines for the analysis of complex designs.

Show MeSH
Comparison of the standard errors obtained with ML, REML and UP for the REML-DE genes of the embriogenomics experiment. Left: REML estimates (y-axis) versus UP estimates (x-axis) of the standard error. Center: REML estimates versus ML estimates. Right: UP estimates versus ML estimates.
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Figure 2: Comparison of the standard errors obtained with ML, REML and UP for the REML-DE genes of the embriogenomics experiment. Left: REML estimates (y-axis) versus UP estimates (x-axis) of the standard error. Center: REML estimates versus ML estimates. Right: UP estimates versus ML estimates.

Mentions: The Venn diagram of Figure 1 shows the number of genes declared differentially expressed (DE) by 4 methods using the Bonferroni method with a 5% level. The UU method gives the least number of DE genes (4) and is not presented in the diagram. REML (which did not converge for 3 genes) gives the greater number of DE genes (93), among which 23 are also found by the other methods, and 70 are specifically found by REML (70 REML specific genes). 70 genes are found DE by ML (22 ML specific genes), and 58 by the naive method (9 Naive specific). Finally 33 genes are declared DE by UP, and all of them are also found by one, two or all of its competitors. Therefore UP provides the less discordant results. The higher number of DE genes obtained with the naive and the ML methods was expected, since it is known from the theory and the simulation study that these methods yield more false positives than the nominal risk. Figure 2 (right) shows that the ML and UP estimates of the standard error are coherent but that the ML estimate are lower than the ones obtained by the UP method. This point is in keeping with the statistical theory which assesses that the UP estimate of the variance is unbiased while the ML estimate has a negative bias.


Statistical methodology for the analysis of dye-switch microarray experiments.

Mary-Huard T, Aubert J, Mansouri-Attia N, Sandra O, Daudin JJ - BMC Bioinformatics (2008)

Comparison of the standard errors obtained with ML, REML and UP for the REML-DE genes of the embriogenomics experiment. Left: REML estimates (y-axis) versus UP estimates (x-axis) of the standard error. Center: REML estimates versus ML estimates. Right: UP estimates versus ML estimates.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2277403&req=5

Figure 2: Comparison of the standard errors obtained with ML, REML and UP for the REML-DE genes of the embriogenomics experiment. Left: REML estimates (y-axis) versus UP estimates (x-axis) of the standard error. Center: REML estimates versus ML estimates. Right: UP estimates versus ML estimates.
Mentions: The Venn diagram of Figure 1 shows the number of genes declared differentially expressed (DE) by 4 methods using the Bonferroni method with a 5% level. The UU method gives the least number of DE genes (4) and is not presented in the diagram. REML (which did not converge for 3 genes) gives the greater number of DE genes (93), among which 23 are also found by the other methods, and 70 are specifically found by REML (70 REML specific genes). 70 genes are found DE by ML (22 ML specific genes), and 58 by the naive method (9 Naive specific). Finally 33 genes are declared DE by UP, and all of them are also found by one, two or all of its competitors. Therefore UP provides the less discordant results. The higher number of DE genes obtained with the naive and the ML methods was expected, since it is known from the theory and the simulation study that these methods yield more false positives than the nominal risk. Figure 2 (right) shows that the ML and UP estimates of the standard error are coherent but that the ML estimate are lower than the ones obtained by the UP method. This point is in keeping with the statistical theory which assesses that the UP estimate of the variance is unbiased while the ML estimate has a negative bias.

Bottom Line: These procedures are compared with the usual ML and REML mixed model procedures proposed in most statistical toolboxes, on both simulated and real data.The UP procedure we propose as an alternative to usual mixed model procedures is more efficient and significantly faster to compute.This result provides some useful guidelines for the analysis of complex designs.

View Article: PubMed Central - HTML - PubMed

Affiliation: UMR AgroParisTech/INRA 518, 16, rue Claude Bernard 75231 Paris CEDEX 05, France. maryhuar@agroparistech.fr

ABSTRACT

Background: In individually dye-balanced microarray designs, each biological sample is hybridized on two different slides, once with Cy3 and once with Cy5. While this strategy ensures an automatic correction of the gene-specific labelling bias, it also induces dependencies between log-ratio measurements that must be taken into account in the statistical analysis.

Results: We present two original statistical procedures for the statistical analysis of individually balanced designs. These procedures are compared with the usual ML and REML mixed model procedures proposed in most statistical toolboxes, on both simulated and real data.

Conclusion: The UP procedure we propose as an alternative to usual mixed model procedures is more efficient and significantly faster to compute. This result provides some useful guidelines for the analysis of complex designs.

Show MeSH