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Systematic evaluation of three microRNA profiling platforms: microarray, beads array, and quantitative real-time PCR array.

Wang B, Howel P, Bruheim S, Ju J, Owen LB, Fodstad O, Xi Y - PLoS ONE (2011)

Bottom Line: Results show that each of the three platforms perform similarly regarding intra-platform reproducibility or reproducibility of data within one platform while LNA array and TLDA had the best inter-platform reproducibility or reproducibility of data across platforms.Each platform is relatively stable in terms of its own microRNA profiling intra-reproducibility; however, the inter-platform reproducibility among different platforms is low.More microRNA specific normalization methods are in demand for cross-platform microRNA microarray data integration and comparison, which will improve the reproducibility and consistency between platforms.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Statistics, University of South Alabama College of Arts and Sciences, Mobile, Alabama, United States of America.

ABSTRACT

Background: A number of gene-profiling methodologies have been applied to microRNA research. The diversity of the platforms and analytical methods makes the comparison and integration of cross-platform microRNA profiling data challenging. In this study, we systematically analyze three representative microRNA profiling platforms: Locked Nucleic Acid (LNA) microarray, beads array, and TaqMan quantitative real-time PCR Low Density Array (TLDA).

Methodology/principal findings: The microRNA profiles of 40 human osteosarcoma xenograft samples were generated by LNA array, beads array, and TLDA. Results show that each of the three platforms perform similarly regarding intra-platform reproducibility or reproducibility of data within one platform while LNA array and TLDA had the best inter-platform reproducibility or reproducibility of data across platforms. The endogenous controls/probes contained in each platform have been observed for their stability under different treatments/environments; those included in TLDA have the best performance with minimal coefficients of variation. Importantly, we identify that the proper selection of normalization methods is critical for improving the inter-platform reproducibility, which is evidenced by the application of two non-linear normalization methods (loess and quantile) that substantially elevated the sensitivity and specificity of the statistical data assessment.

Conclusions: Each platform is relatively stable in terms of its own microRNA profiling intra-reproducibility; however, the inter-platform reproducibility among different platforms is low. More microRNA specific normalization methods are in demand for cross-platform microRNA microarray data integration and comparison, which will improve the reproducibility and consistency between platforms.

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Related in: MedlinePlus

Inter-platform reproducibility measured by sensitivity and specificity.Sensitivities were compared to evaluate the consistency among the three platforms. The three plots in the first column compare the consistency between beads array and TLDA, the three plots in the second column compare the consistency between LNA array and TLDA, and the plots in the third column compare the consistency between beads array and LNA array. The results in the first, second and third rows are based on the profiles normalized using scaling by specific controls/probes, quantile normalization, and the cyclic loess method, respectively.
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pone-0017167-g006: Inter-platform reproducibility measured by sensitivity and specificity.Sensitivities were compared to evaluate the consistency among the three platforms. The three plots in the first column compare the consistency between beads array and TLDA, the three plots in the second column compare the consistency between LNA array and TLDA, and the plots in the third column compare the consistency between beads array and LNA array. The results in the first, second and third rows are based on the profiles normalized using scaling by specific controls/probes, quantile normalization, and the cyclic loess method, respectively.

Mentions: In this study, we also utilized sensitivity and specificity, two statistical measures of the performance of a binary classification, as test statistics of each platform to measure the inter-platform reproducibility. Our foremost assumption regarding the use of raw miRNA data for such a study is that a given miRNA from a single patient after a single treatment should maintain a consistent trend of up- or down-regulation across each platform, and an effective miRNA expression profiling platform should be capable of assessing the true direction of regulation. Thus, we compared the sensitivity and specificity of beads array (Figure 6; left panel) and LNA array (right panel), using the TLDA results as a reference, under different normalization methods with various fold-change cutoffs (x-axis). As shown in the left plot in the first row, the specificity is high when normalizing the profiling data of beads array by the four normalizers; however, the sensitivity is low. The loess method can improve the sensitivity and lower the specificity as a trade-off for the beads array data, while quantile normalization and the scaling method have similar effects on the beads data. By comparing the results of beads array with that of LNA array, we see that the consistency of LNA array in predicting the regulation trends (up- or down-regulated) is better than beads array, consistent with the results of inter-platform reproducibility as assessed by correlation coefficients. Additionally, quantile and loess normalization can improve the consistency of beads array and LNA array with TLDA, although the choice of optimal cutoff value could be slightly different.


Systematic evaluation of three microRNA profiling platforms: microarray, beads array, and quantitative real-time PCR array.

Wang B, Howel P, Bruheim S, Ju J, Owen LB, Fodstad O, Xi Y - PLoS ONE (2011)

Inter-platform reproducibility measured by sensitivity and specificity.Sensitivities were compared to evaluate the consistency among the three platforms. The three plots in the first column compare the consistency between beads array and TLDA, the three plots in the second column compare the consistency between LNA array and TLDA, and the plots in the third column compare the consistency between beads array and LNA array. The results in the first, second and third rows are based on the profiles normalized using scaling by specific controls/probes, quantile normalization, and the cyclic loess method, respectively.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3037970&req=5

pone-0017167-g006: Inter-platform reproducibility measured by sensitivity and specificity.Sensitivities were compared to evaluate the consistency among the three platforms. The three plots in the first column compare the consistency between beads array and TLDA, the three plots in the second column compare the consistency between LNA array and TLDA, and the plots in the third column compare the consistency between beads array and LNA array. The results in the first, second and third rows are based on the profiles normalized using scaling by specific controls/probes, quantile normalization, and the cyclic loess method, respectively.
Mentions: In this study, we also utilized sensitivity and specificity, two statistical measures of the performance of a binary classification, as test statistics of each platform to measure the inter-platform reproducibility. Our foremost assumption regarding the use of raw miRNA data for such a study is that a given miRNA from a single patient after a single treatment should maintain a consistent trend of up- or down-regulation across each platform, and an effective miRNA expression profiling platform should be capable of assessing the true direction of regulation. Thus, we compared the sensitivity and specificity of beads array (Figure 6; left panel) and LNA array (right panel), using the TLDA results as a reference, under different normalization methods with various fold-change cutoffs (x-axis). As shown in the left plot in the first row, the specificity is high when normalizing the profiling data of beads array by the four normalizers; however, the sensitivity is low. The loess method can improve the sensitivity and lower the specificity as a trade-off for the beads array data, while quantile normalization and the scaling method have similar effects on the beads data. By comparing the results of beads array with that of LNA array, we see that the consistency of LNA array in predicting the regulation trends (up- or down-regulated) is better than beads array, consistent with the results of inter-platform reproducibility as assessed by correlation coefficients. Additionally, quantile and loess normalization can improve the consistency of beads array and LNA array with TLDA, although the choice of optimal cutoff value could be slightly different.

Bottom Line: Results show that each of the three platforms perform similarly regarding intra-platform reproducibility or reproducibility of data within one platform while LNA array and TLDA had the best inter-platform reproducibility or reproducibility of data across platforms.Each platform is relatively stable in terms of its own microRNA profiling intra-reproducibility; however, the inter-platform reproducibility among different platforms is low.More microRNA specific normalization methods are in demand for cross-platform microRNA microarray data integration and comparison, which will improve the reproducibility and consistency between platforms.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Statistics, University of South Alabama College of Arts and Sciences, Mobile, Alabama, United States of America.

ABSTRACT

Background: A number of gene-profiling methodologies have been applied to microRNA research. The diversity of the platforms and analytical methods makes the comparison and integration of cross-platform microRNA profiling data challenging. In this study, we systematically analyze three representative microRNA profiling platforms: Locked Nucleic Acid (LNA) microarray, beads array, and TaqMan quantitative real-time PCR Low Density Array (TLDA).

Methodology/principal findings: The microRNA profiles of 40 human osteosarcoma xenograft samples were generated by LNA array, beads array, and TLDA. Results show that each of the three platforms perform similarly regarding intra-platform reproducibility or reproducibility of data within one platform while LNA array and TLDA had the best inter-platform reproducibility or reproducibility of data across platforms. The endogenous controls/probes contained in each platform have been observed for their stability under different treatments/environments; those included in TLDA have the best performance with minimal coefficients of variation. Importantly, we identify that the proper selection of normalization methods is critical for improving the inter-platform reproducibility, which is evidenced by the application of two non-linear normalization methods (loess and quantile) that substantially elevated the sensitivity and specificity of the statistical data assessment.

Conclusions: Each platform is relatively stable in terms of its own microRNA profiling intra-reproducibility; however, the inter-platform reproducibility among different platforms is low. More microRNA specific normalization methods are in demand for cross-platform microRNA microarray data integration and comparison, which will improve the reproducibility and consistency between platforms.

Show MeSH
Related in: MedlinePlus