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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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Log-ratio distributions. These plots depict the distribution of observed log ratios for various nominal fold changes. In each case, the log ratios are stratified by the ALE values into which the two nominal concentrations fall. For example, HL means that one fell in the high stratum and one fell in the medium stratum. The  distributions' log-ratios are divided into background RNA (Bg-Null) and spike-ins at the same nominal concentration (S-Null), for each bin. The dotted horizontal lines represent the expected or nominal log-ratios: zero for the  distribution and Δ for the other comparisons (Δ =log2 4 for Affymetrix and Δ =log2 3 for Agilent and Illumina).
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Figure 3: Log-ratio distributions. These plots depict the distribution of observed log ratios for various nominal fold changes. In each case, the log ratios are stratified by the ALE values into which the two nominal concentrations fall. For example, HL means that one fell in the high stratum and one fell in the medium stratum. The distributions' log-ratios are divided into background RNA (Bg-Null) and spike-ins at the same nominal concentration (S-Null), for each bin. The dotted horizontal lines represent the expected or nominal log-ratios: zero for the distribution and Δ for the other comparisons (Δ =log2 4 for Affymetrix and Δ =log2 3 for Agilent and Illumina).

Mentions: To complete our comparison, we needed to assess specificity. Because the majority of microarray studies rely on relative measures (e.g. fold change) as opposed to absolute ones, we focused on the precision of the basic unit of relative expression: log-ratios. We adapted the precision assessment of Cope et al. (10) that focused on the variability of log-ratios generated by comparisons expected to produce log-ratios of 0. Our set of comparisons was created by making all possible comparisons between spiked-in transcripts across arrays in which they had the same nominal concentration and from all possible comparisons within the background RNA. We referred to this group of comparisons as the Null set. The SD of these log-ratios served as a basic assessment of precision and has a useful interpretation: it is the expected range of observed log-ratios for genes that are not differentially expressed. Table 3 and Figure 3 show results for the three platforms.Table 3.


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

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

Log-ratio distributions. These plots depict the distribution of observed log ratios for various nominal fold changes. In each case, the log ratios are stratified by the ALE values into which the two nominal concentrations fall. For example, HL means that one fell in the high stratum and one fell in the medium stratum. The  distributions' log-ratios are divided into background RNA (Bg-Null) and spike-ins at the same nominal concentration (S-Null), for each bin. The dotted horizontal lines represent the expected or nominal log-ratios: zero for the  distribution and Δ for the other comparisons (Δ =log2 4 for Affymetrix and Δ =log2 3 for Agilent and Illumina).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 3: Log-ratio distributions. These plots depict the distribution of observed log ratios for various nominal fold changes. In each case, the log ratios are stratified by the ALE values into which the two nominal concentrations fall. For example, HL means that one fell in the high stratum and one fell in the medium stratum. The distributions' log-ratios are divided into background RNA (Bg-Null) and spike-ins at the same nominal concentration (S-Null), for each bin. The dotted horizontal lines represent the expected or nominal log-ratios: zero for the distribution and Δ for the other comparisons (Δ =log2 4 for Affymetrix and Δ =log2 3 for Agilent and Illumina).
Mentions: To complete our comparison, we needed to assess specificity. Because the majority of microarray studies rely on relative measures (e.g. fold change) as opposed to absolute ones, we focused on the precision of the basic unit of relative expression: log-ratios. We adapted the precision assessment of Cope et al. (10) that focused on the variability of log-ratios generated by comparisons expected to produce log-ratios of 0. Our set of comparisons was created by making all possible comparisons between spiked-in transcripts across arrays in which they had the same nominal concentration and from all possible comparisons within the background RNA. We referred to this group of comparisons as the Null set. The SD of these log-ratios served as a basic assessment of precision and has a useful interpretation: it is the expected range of observed log-ratios for genes that are not differentially expressed. Table 3 and Figure 3 show results for the three platforms.Table 3.

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