<![CDATA[In searching for new biomarkers, high throughput technique has been widely used by researchers, including for gene expression study. However, the reliability and accuracy of results from high throughput study critically depends on appropriate data management, including normalization methods. Data driven normalization has been introduced as a normalization method for high throughput gene expression study. Thus, this study was conducted to evaluate the performance of various data driven and reference genes normalization methods using a high throughput circulating microRNA expression dataset. A quantification cycle (Cq) dataset generated from a high throughput circulating microRNA study was used to test the normalization methods using HTqPCR package in R software. The normalized Cq generated from different methods were compared descriptively using box plot analysis and coefficient of variance. The box plot analysis showed that quantile normalization produced more homogenous Cq distribution, lesser outliers and reduced coefficient of variance as compared to other normalization methods in screening and validation phases. The overview on quantile normalized Cq showed consistency in its level of expression before and after 2-∆∆Cq calculation indicating the reliability of quantile normalized Cq. Quantile normalization is suggested to be used in high throughput miRNA expression study due to its performance in homogenizing the data, reduce outliers and coefficient of variance.]]>
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