Impact of normalization on miRNA microarray expression profiling

Abstract

Profiling miRNA levels in cells with miRNA microarrays is becoming a widely used technique. Although normalization methods for mRNA gene expression arrays are well established, miRNA array normalization has so far not been investigated in detail. In this study we investigate the impact of normalization on data generated with the Agilent miRNA array platform. We have developed a method to select nonchanging miRNAs (invariants) and use them to compute linear regression normalization coefficients or variance stabilizing normalization (VSN) parameters. We compared the invariants normalization to normalization by scaling, quantile, and VSN with default parameters as well as to no normalization using samples with strong differential expression of miRNAs (heart-brain comparison) and samples where only a few miRNAs are affected (by p53 overexpression in squamous carcinoma cells versus control). All normalization methods performed better than no normalization. Normalization procedures based on the set of invariants and quantile were the most robust over all experimental conditions tested. Our method of invariant selection and normalization is not limited to Agilent miRNA arrays and can be applied to other data sets including those from one color miRNA microarray platforms, focused gene expression arrays, and gene expression analysis using quantitative PCR.

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

“…The first miRNA microarray experiments were accompanied by either normalization using scaling to the median or even no normalization of miRNA data by applying background signal subtraction only (summarized in Hua et al 2008 andPradervand et al 2009). Only recently, comparative studies on the relative performance of different normalization methods within a miRNA microarray platform have emphasized the need for evaluating and identifying appropriate normalization methods (Rao et al 2008, Pradervand et al 2009, Hua et al 2008).…”

“…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).…”

“…Normalization based on predefined invariant endogenous controls, reference miRNAs (Peltier and Latham 2008) or other small non-coding RNAs such as small nuclear, small nucleolar RNA (summarized in Table 2) or 5s rRNA, is a commonly used approach in miRNA RT-qPCR profiling data analysis (Benes and Castoldi 2010;Pradervand et al 2009;Mestdagh et al 2009). However, the use of small non-coding RNAs other than miRNAs does not mirror the physicochemical properties of miRNA molecules and it has been argued that it is best to normalize genes with reference genes belonging to the same RNA class (Vandesompele et al 2002).…”

“…Data driven invariant selection of miRNAs whose expression pattern is similar to the global mean expression (Mestdagh et al 2009;Chang et al 2010) has been suggested. Moreover, invariant selection based on a distinguishable low standard deviation and high-mean population as suggested by Pradervand et al (2009) for miRNA microarray preprocessing is generally applicable for RT-qPCR profiling experiments as well. Basically, the use of more than one reference gene increases the accuracy of quantification compared to the use of a single reference gene (Vandesompele et al 2002;Andersen et al 2004).…”

“…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.…”