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Consolidated strategy for the analysis of microarray spike-in data.

McCall MN, Irizarry RA - Nucleic Acids Res. (2008)

Bottom Line: Spike-in experiments have been successfully used for internal technology assessments by microarray manufacturers and for comparisons of competing data analysis approaches.Furthermore, cross-platform comparisons have proven difficult because reported concentrations are not comparable.We demonstrated the utility of our tools by presenting the first spike-in-based comparison of the three major platforms--Affymetrix, Agilent and Illumina.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Johns Hopkins University, 615 N. Wolfe Street, Baltimore, MD 21205, USA.

ABSTRACT
As the number of users of microarray technology continues to grow, so does the importance of platform assessments and comparisons. Spike-in experiments have been successfully used for internal technology assessments by microarray manufacturers and for comparisons of competing data analysis approaches. The microarray literature is saturated with statistical assessments based on spike-in experiment data. Unfortunately, the statistical assessments vary widely and are applicable only in specific cases. This has introduced confusion into the debate over best practices with regards to which platform, protocols and data analysis tools are best. Furthermore, cross-platform comparisons have proven difficult because reported concentrations are not comparable. In this article, we introduce two new spike-in experiments, present a novel statistical solution that enables cross-platform comparisons, and propose a comprehensive procedure for assessments based on spike-in experiments. The ideas are implemented in a user friendly Bioconductor package: spkTools. We demonstrated the utility of our tools by presenting the first spike-in-based comparison of the three major platforms--Affymetrix, Agilent and Illumina.

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Observed versus nominal values. For each of the three platforms, expression values are plotted against the log (base 2) of the reported nominal concentration. The regression slope obtained utilizing all the data and the regression slopes obtain within each ALE value strata are shown. The slope of each line is reported in the legend. The vertical lines divide the ALE strata.
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Figure 2: Observed versus nominal values. For each of the three platforms, expression values are plotted against the log (base 2) of the reported nominal concentration. The regression slope obtained utilizing all the data and the regression slopes obtain within each ALE value strata are shown. The slope of each line is reported in the legend. The vertical lines divide the ALE strata.

Mentions: With the ALE values in place, we were ready to adapt some of the existing statistical assessments to cross-platform comparisons. We started with a basic assessment of accuracy: the signal detection slope (10). Microarrays are designed to measure the abundance of sample RNA. In principle, we expect a doubling of nominal concentration to result in a doubling of observed intensity. In other words, on the log2 scale, the slope from the regression of expression on nominal concentration can be interpreted as the expected observed difference when the true difference is a fold change of 2. Thus, an optimal result is a slope of one, and values higher and lower than one are associated with over and under estimation, respectively (Figure 2).Figure 2.


Consolidated strategy for the analysis of microarray spike-in data.

McCall MN, Irizarry RA - Nucleic Acids Res. (2008)

Observed versus nominal values. For each of the three platforms, expression values are plotted against the log (base 2) of the reported nominal concentration. The regression slope obtained utilizing all the data and the regression slopes obtain within each ALE value strata are shown. The slope of each line is reported in the legend. The vertical lines divide the ALE strata.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2553586&req=5

Figure 2: Observed versus nominal values. For each of the three platforms, expression values are plotted against the log (base 2) of the reported nominal concentration. The regression slope obtained utilizing all the data and the regression slopes obtain within each ALE value strata are shown. The slope of each line is reported in the legend. The vertical lines divide the ALE strata.
Mentions: With the ALE values in place, we were ready to adapt some of the existing statistical assessments to cross-platform comparisons. We started with a basic assessment of accuracy: the signal detection slope (10). Microarrays are designed to measure the abundance of sample RNA. In principle, we expect a doubling of nominal concentration to result in a doubling of observed intensity. In other words, on the log2 scale, the slope from the regression of expression on nominal concentration can be interpreted as the expected observed difference when the true difference is a fold change of 2. Thus, an optimal result is a slope of one, and values higher and lower than one are associated with over and under estimation, respectively (Figure 2).Figure 2.

Bottom Line: Spike-in experiments have been successfully used for internal technology assessments by microarray manufacturers and for comparisons of competing data analysis approaches.Furthermore, cross-platform comparisons have proven difficult because reported concentrations are not comparable.We demonstrated the utility of our tools by presenting the first spike-in-based comparison of the three major platforms--Affymetrix, Agilent and Illumina.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Johns Hopkins University, 615 N. Wolfe Street, Baltimore, MD 21205, USA.

ABSTRACT
As the number of users of microarray technology continues to grow, so does the importance of platform assessments and comparisons. Spike-in experiments have been successfully used for internal technology assessments by microarray manufacturers and for comparisons of competing data analysis approaches. The microarray literature is saturated with statistical assessments based on spike-in experiment data. Unfortunately, the statistical assessments vary widely and are applicable only in specific cases. This has introduced confusion into the debate over best practices with regards to which platform, protocols and data analysis tools are best. Furthermore, cross-platform comparisons have proven difficult because reported concentrations are not comparable. In this article, we introduce two new spike-in experiments, present a novel statistical solution that enables cross-platform comparisons, and propose a comprehensive procedure for assessments based on spike-in experiments. The ideas are implemented in a user friendly Bioconductor package: spkTools. We demonstrated the utility of our tools by presenting the first spike-in-based comparison of the three major platforms--Affymetrix, Agilent and Illumina.

Show MeSH
Related in: MedlinePlus