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三种 microRNA 分析平台的系统评估:微阵列、珠子阵列和实时定量 PCR 阵列。

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

机构信息

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

出版信息

PLoS One. 2011 Feb 11;6(2):e17167. doi: 10.1371/journal.pone.0017167.

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.

摘要

背景

许多基因分析方法已被应用于 microRNA 研究。平台和分析方法的多样性使得跨平台 microRNA 分析数据的比较和整合具有挑战性。在这项研究中,我们系统地分析了三种代表性的 microRNA 分析平台:锁核酸 (LNA) 微阵列、珠子阵列和 TaqMan 定量实时 PCR 低密度阵列 (TLDA)。

方法/主要发现:通过 LNA 阵列、珠子阵列和 TLDA 生成了 40 个人骨肉瘤异种移植样本的 microRNA 图谱。结果表明,在同一平台内,每个平台的内平台重现性或数据重现性都表现相似,而 LNA 阵列和 TLDA 具有最佳的跨平台重现性或跨平台数据重现性。观察到每个平台中包含的内源性对照/探针在不同处理/环境下的稳定性;TLDA 中包含的探针表现最好,变异系数最小。重要的是,我们发现正确选择归一化方法对于提高跨平台重现性至关重要,这可以通过应用两种非线性归一化方法(局部加权回归和分位数)来证明,这大大提高了统计数据评估的灵敏度和特异性。

结论

就其自身的 microRNA 分析内重现性而言,每个平台都是相对稳定的;然而,不同平台之间的平台间重现性较低。对于跨平台 microRNA 微阵列数据的集成和比较,需要更多的 microRNA 特异性归一化方法,这将提高平台之间的重现性和一致性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f927/3037970/fb83b25932cc/pone.0017167.g001.jpg

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