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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.

SUBMITTER: Pradervand S

PROVIDER: S-EPMC2657010 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature

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Impact of normalization on miRNA microarray expression profiling.

Pradervand Sylvain S Weber Johann J Thomas Jérôme J Bueno Manuel M Wirapati Pratyaksha P Lefort Karine K Dotto G Paolo GP Harshman Keith K

RNA (New York, N.Y.) 20090128 3

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 o ...[more]

PMID: 19176604

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Project description: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. Here, we developed a method to select non-changing miRNAs (“invariants”) and used them to compute linear regression normalization coefficients or Variance Stabilizing Normalization (VSN) parameters. We compared the invariant normalizations 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 few miRNAs are affected (p53 overexpression in SCC13 cells versus GFP vector control transfected cells). All normalization methods performed better than no normalization. Normalizations 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 datasets from one color miRNA microarray platforms, focused gene expression arrays and gene expression analysis using quantitative PCR. Keywords: miRNA profiling To assay technical reproducibility, 3 technical replicates from brain and heart RNA (Stratagene) were hybridized on Agilent human miRNA microarrays. To determine sensitivity and specificity, heart and brain RNA were mixed in the following ratios: 50% heart 50% brain, 25% heart 75% brain and 5% heart 95% brain. Each of the dilutions was hybridized in a technical duplicate on Agilent human miRNA arrays. To assay the effect of p53 expression on miRNA levels in the human SCC13 cell line, RNA from 3 biological replicates of p53-expressing or control cells were hybridized in technical duplicates on the microarrays, resulting in a total of 12 hybridizations.

Project description:MicroRNAs (miRNAs) have critical functions in various biological processes. MiRNA profiling is an important tool for the identification of differentially expressed miRNAs in normal cellular and disease processes. A technical challenge remains for high-throughput miRNA expression analysis as the number of miRNAs continues to increase with in silico prediction and experimental verification. Our study critically evaluated the performance of a novel miRNA expression profiling approach, quantitative RT-PCR array (qPCR-array), compared to miRNA detection with oligonucleotide microchip (microarray).High reproducibility with qPCR-array was demonstrated by comparing replicate results from the same RNA sample. Pre-amplification of the miRNA cDNA improved sensitivity of the qPCR-array and increased the number of detectable miRNAs. Furthermore, the relative expression levels of miRNAs were maintained after pre-amplification. When the performance of qPCR-array and microarrays were compared using different aliquots of the same RNA, a low correlation between the two methods (r=-0.443) indicated considerable variability between the two assay platforms. Higher variation between replicates was observed in miRNAs with low expression in both assays. Finally, a higher false positive rate of differential miRNA expression was observed using the microarray compared to the qPCR-array.Our studies demonstrated high reproducibility of TaqMan qPCR-array. Comparison between different reverse transcription reactions and qPCR-arrays performed on different days indicated that reverse transcription reactions did not introduce significant variation in the results. The use of cDNA pre-amplification increased the sensitivity of miRNA detection. Although there was variability associated with pre-amplification in low abundance miRNAs, the latter did not involve any systemic bias in the estimation of miRNA expression. Comparison between microarray and qPCR-array indicated superior sensitivity and specificity of qPCR-array.

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Impact of normalization on miRNA microarray expression profiling.

Publications

Impact of normalization on miRNA microarray expression profiling.

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