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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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Empirical densities. These plots depict the empirical density of the average (across arrays) expression values for the background RNA. The tick marks on the x-axis show the average expression at each nominal concentration. The dotted lines represent the cut points for low, medium and high ALE values (defined in text).
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Figure 1: Empirical densities. These plots depict the empirical density of the average (across arrays) expression values for the background RNA. The tick marks on the x-axis show the average expression at each nominal concentration. The dotted lines represent the cut points for low, medium and high ALE values (defined in text).

Mentions: An important fact that has been overlooked by previous assessments is that microarray performance largely depends on concentration levels (11). Assessments based on experiments for which spike-in concentrations lead to unusually high expression measurements have resulted in misleading conclusions (12). For this reason, it is essential that the distribution of observed expression for the spike-in transcripts reflects the distributions seen in typical experiments. Figure 1 shows the typical distribution of expression values for the background RNA for the three studied data sets. The tick marks on the x-axis represent the average expression at each reported spike-in level. This figure illustrates that the spike-in transcripts resulted in higher expression measurements, on average, than the background RNA transcripts. Furthermore, we see that, relative to their respective background RNA distributions, the Agilent and Illumina spike-ins have higher observed expression than those in the Affymetrix experiment. Previous work (11) suggests that comparing platform performance without correcting for this leaves Affymetrix at a disadvantage.Figure 1.


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

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

Empirical densities. These plots depict the empirical density of the average (across arrays) expression values for the background RNA. The tick marks on the x-axis show the average expression at each nominal concentration. The dotted lines represent the cut points for low, medium and high ALE values (defined in text).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 1: Empirical densities. These plots depict the empirical density of the average (across arrays) expression values for the background RNA. The tick marks on the x-axis show the average expression at each nominal concentration. The dotted lines represent the cut points for low, medium and high ALE values (defined in text).
Mentions: An important fact that has been overlooked by previous assessments is that microarray performance largely depends on concentration levels (11). Assessments based on experiments for which spike-in concentrations lead to unusually high expression measurements have resulted in misleading conclusions (12). For this reason, it is essential that the distribution of observed expression for the spike-in transcripts reflects the distributions seen in typical experiments. Figure 1 shows the typical distribution of expression values for the background RNA for the three studied data sets. The tick marks on the x-axis represent the average expression at each reported spike-in level. This figure illustrates that the spike-in transcripts resulted in higher expression measurements, on average, than the background RNA transcripts. Furthermore, we see that, relative to their respective background RNA distributions, the Agilent and Illumina spike-ins have higher observed expression than those in the Affymetrix experiment. Previous work (11) suggests that comparing platform performance without correcting for this leaves Affymetrix at a disadvantage.Figure 1.

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