Statistical analysis of repeated microRNA high-throughput data with application to human heart failure: a review of methodology
Shesh N Rai1, Herman E Ray2, Xiaobin Yuan1, Jianmin Pan1, Tariq Hamid3,4, Sumanth D Prabhu3,4
1Biostatistics Shared Facility, JG Brown Cancer Center and Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA; 2Department of Mathematics and Statistics, Kennesaw State University, Kennesaw, GA, USA; 3Division of Cardiovascular Medicine, University of Louisville, Louisville, KY, USA; 4Division of Cardiovascular Disease, University of Alabama – Birmingham and Birmingham VAMC, Birmingham, AL, USA
Abstract: Complex experimental designs present unique challenges in the analysis of microRNA (miRNA) cycle to threshold (Ct) values. In this paper, we discuss various statistical techniques and their application in an analysis performed at the JG Brown Cancer Center. We consider data quality evaluation, data normalization, and statistical hypothesis procedures in the context of maintaining patients prior to heart transplantation. The research involved repeated sampling over time, and the intra-subject correlation created by the repeated sampling should be incorporated into the analysis resulting in additional significant miRNAs. The statistical techniques leveraged to analyze miRNA Ct values resulting from qPCR should incorporate key features of the experimental design. When an experiment collects multiple samples from the same individuals over time this may cause issues with the commonly used methodologies – these issues are discussed.
Keywords: miRNA, repeated measurements, normalization, hypothesis testing
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