Application of a Blood-based Dynamic Genome Signature: How MAS5/RMA/PLIER Normalization Batches Affect Measured Gene Expression Profiling Stability in Clinical Diagnosis
Received Date: Dec 18, 2017 / Accepted Date: Jan 08, 2018 / Published Date: Jan 10, 2018
Abstract
Objective: A number of studies compare the microarray normalization methods MAS5 (Microarray Suite Version 5), RMA (Robust Multi-array Average) and PLIER (Probe Logarithmic Error Intensity Estimate) with respect to the rate at which genes of interest are identified. Here we evaluate and compare the stability of the measured gene expression when identical or technical replicate arrays are analyzed in batches of differing sizes and composition. These variations in measured gene expression have implications for clinical applications, which have requirements that differ significantly from those of research applications.
Methods: We evaluated the samples from data set E-MTAB-1532, available on ArrayExpress, a public repository of microarray data using the MAS5, RMA, and PLIER methods. We then evaluated a sample run as triplicate arrays and compared results among the different normalization methods.
Results and conclusion: Our study found that for some applications MAS5 is superior to the other methods, although the MAQC (Micro Array Quality Control) project, which extensively evaluated the performance of the platforms, reached a different conclusion.
Keywords: Microarray normalization methods; Microarray suite version; Robust multi-array average; Probe logarithmic error intensity estimate; Microarray; Gene expression; Microarray quality control project
Citation: Chao J, Chaban G, Cheng C, Piarar S, Liew CC, et al. (2018) Application of a Blood-based Dynamic Genome Signature: How MAS5/RMA/PLIER Normalization Batches Affect Measured Gene Expression Profiling Stability in Clinical Diagnosis. J Clin Exp Pathol 8: 333. Doi: 10.4172/2161-0681.1000333
Copyright: ©2017 Chao J, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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