Quality Assessment and Data Analysis for microRNA Expression Arrays

Abstract

MicroRNAs are small (∼22 nt) RNAs that regulate gene expression and play important roles in both normal and disease physiology. The use of microarrays for global characterization of microRNA expression is becoming increasingly popular and has the potential to be a widely used and valuable research tool. However, microarray profiling of microRNA expression raises a number of data analytic challenges that must be addressed in order to obtain reliable results. We introduce here a universal reference microRNA reagent set as well as a series of nonhuman spiked-in synthetic microRNA controls, and demonstrate their use for quality control and between-array normalization of microRNA expression data. We also introduce diagnostic plots designed to assess and compare various normalization methods. We anticipate that the reagents and analytic approach presented here will be useful for improving the reliability of microRNA microarray experiments.

Normalization strategies for microRNA profiling experiments: a ‘normal’ way to a hidden layer of complexity?

“…However, in a similar study, Sarkar et al (2009) did not find significant differences between print-tip LOESS and other normalizations. Risso et al (2009) proposed the non-parametric LOESSM normalization which scales the expression data on the global median expression rather than on zero.…”

“…However, these assumptions could fail for miRNA platforms as they are printed with a relatively small number of selected sequences Hua et al 2008). Moreover, the number of expressed miRs in a given sample tends to be small (Sarkar et al 2009). Hence, the proportion of those miRNAs that are differentially expressed (among those expressed at all) is much larger than that observed when profiling global mRNA expression (Sarkar et al 2009).…”

“…Moreover, the number of expressed miRs in a given sample tends to be small (Sarkar et al 2009). Hence, the proportion of those miRNAs that are differentially expressed (among those expressed at all) is much larger than that observed when profiling global mRNA expression (Sarkar et al 2009). Thus, one needs to verify whether these assumptions hold true for the respective datasets.…”

“…Normalization of RT-qPCR miRNA profiling data is needed as in addition to reflecting miRNA levels, signal intensities may also depend on reverse transcription and PCR reaction efficiencies. Normalized miRNA RT-qPCR profiling data is widely used for independent evaluation of the goodness of miRNA microarray normalization strategies (Sarkar et al 2009;Pradervand et al 2009;Sato et al 2009;Hua et al 2008) and validation of microarray or small RNA-seq results (Git et al 2010). RT-qPCR is generally accepted as gold standard for microRNA measurement (Sato et al 2009;Hua et al 2008) though this view has been recently challenged (Git et al 2010).…”

“…Therefore, normalization is a prerequisite for getting true biological changes and allowing biologically meaningful comparisons to be made. Several studies pointed out that selection of the data preprocessing method can have great impact on the resulting data outcome (Lopez-Romero et al 2010;Hua et al 2008;Pradervand et al 2009;Risso et al 2009;Sarkar et al 2009) such as identification of differentially expressed miRNAs and data interpretation. Thus, inappropriate normalization of the data can lead to incorrect conclusions.…”