Normalization of miRNA qPCR high-throughput data: a comparison of methods

Low-density quantitative real-time PCR (qPCR) arrays are often used to profile expression patterns of microRNAs in various biological milieus. To achieve accurate analysis of expression of miRNAs, non-biological sources of variation in data should be removed through precise normalization of data. We have systematically compared the performance of 19 normalization methods on different subsets of a real miRNA qPCR dataset that covers 40 human tissues. After robustly modeling the mean squared error (MSE) in normalized data, we demonstrate lower variability between replicates is achieved using various methods not applied to high-throughput miRNA qPCR data yet. Normalization methods that use splines or wavelets smoothing to estimate and remove Cq dependent non-linearity between pairs of samples best reduced the MSE of differences in Cq values of replicate samples. These methods also retained between-group variability in different subsets of the dataset.     

Analysis of gene expression using high-density and IFN-gamma-specific low-density cDNA arrays.

The combination of high and low density cDNA filter array technology potentially permits both the identification of subsets of induced genes and convenient and rapid multisample expression profiling of such subsets under a variety of conditions. The JAK/STAT1 pathway for IFN-gamma signaling in human cells has been well characterized, but the extent and importance of additional pathways remain to be established. Here, using high-density filter arrays of the RZPD UniGene set, we identified 18 novel IFN-gamma-inducible genes. Expression profiling was carried out using low-density arrays representing both novel and known IFN-gamma-inducible genes. Initial experiments failed to detect evidence for any novel non-JAK-dependent pathways in cells expressing a kinase-dead JAK2. The data, however, validated the potential of the combined methods in establishing rapid and convenient expression profiling of several hundred genes in response to any ligand of choice.     

Microarray standard data set and figures of merit for comparing data processing methods and experiment designs

MOTIVATION: There is a very large and growing level of effort toward improving the platforms, experiment designs, and data analysis methods for microarray expression profiling. Along with a growing richness in the approaches there is a growing confusion among most scientists as to how to make objective comparisons and choices between them for different applications. There is a need for a standard framework for the microarray community to compare and improve analytical and statistical methods. RESULTS: We report on a microarray data set comprising 204 in-situ synthesized oligonucleotide arrays, each hybridized with two-color cDNA samples derived from 20 different human tissues and cell lines. Design of the approximately 24 000 60mer oligonucleotides that report approximately 2500 known genes on the arrays, and design of the hybridization experiments, were carried out in a way that supports the performance assessment of alternative data processing approaches and of alternative experiment and array designs. We also propose standard figures of merit for success in detecting individual differential expression changes or expression levels, and for detecting similarities and differences in expression patterns across genes and experiments. We expect this data set and the proposed figures of merit will provide a standard framework for much of the microarray community to compare and improve many analytical and statistical methods relevant to microarray data analysis, including image processing, normalization, error modeling, combining of multiple reporters per gene, use of replicate experiments, and sample referencing schemes in measurements based on expression change. AVAILABILITY/SUPPLEMENTARY INFORMATION: Expression data and supplementary information are available at http://www.rii.com/publications/2003/HE_SDS.htm     

qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data

Although quantitative PCR (qPCR) is becoming the method of choice for expression profiling of selected genes, accurate and straightforward processing of the raw measurements remains a major hurdle. Here we outline advanced and universally applicable models for relative quantification and inter-run calibration with proper error propagation along the entire calculation track. These models and algorithms are implemented in qBase, a free program for the management and automated analysis of qPCR data.     

Data variance and statistical significance in 2D-gel electrophoresis and DIGE experiments: comparison of the effects of normalization methods

Identifying changes in the relative abundance of proteins between different biological samples is often confounded by technical noise. In this work, we compared eight normalization methods commonly used in two-dimensional gel electrophoresis and difference gel electrophoresis (DIGE) experiments for their ability to reduce noise and for their influence on the list of proteins whose difference in abundance between two samples is determined to be statistically significant. With respect to reducing noise we find that, while all methods improve upon unnormalized data, cyclic linear normalization is the least well suited to gel-based proteomics and the performances of the other methods are similar. We also find in DIGE data that the choice of normalization method has less of an impact on the noise than does the decision to use an internal reference in the experimental design and that both normalization and standardization using the internal reference are required to maximally reduce variance. Despite the similar noise reduction achieved by most normalization methods, the list of proteins whose abundance was determined to differ significantly between biological groups differed depending on the choice of normalization method. This work provides a direct comparison of the impact of normalization methods in the context of common experimental designs.     

Gene expression studies of reference genes for quantitative real-time PCR: an overview in insects

Whenever gene expression is being examined, it is essential that a normalization process is carried out to eliminate non-biological variations. The use of reference genes, such as glyceraldehyde-3-phosphate dehydrogenase, actin, and ribosomal protein genes, is the usual method of choice for normalizing gene expression. Although reference genes are used to normalize target gene expression, a major problem is that the stability of these genes differs among tissues, developmental stages, species, and responses to abiotic factors. Therefore, the use and validation of multiple reference genes are required. This review discusses the reasons that why RT-qPCR has become the preferred method for validating results of gene expression profiles, the use of specific and non-specific dyes and the importance of use of primers and probes for qPCR as well as to discuss several statistical algorithms developed to help the validation of potential reference genes. The conflicts arising in the use of classical reference genes in gene normalization and their replacement with novel references are also discussed by citing the high stability and low stability of classical and novel reference genes under various biotic and abiotic experimental conditions by employing various methods applied for the reference genes amplification.     

Importance of randomization in microarray experimental designs with Illumina platforms

Measurements of gene expression from microarray experiments are highly dependent on experimental design. Systematic noise can be introduced into the data at numerous steps. On Illumina BeadChips, multiple samples are assayed in an ordered series of arrays. Two experiments were performed using the same samples but different hybridization designs. An experiment confounding genotype with BeadChip and treatment with array position was compared to another experiment in which these factors were randomized to BeadChip and array position. An ordinal effect of array position on intensity values was observed in both experiments. We demonstrate that there is increased rate of false-positive results in the confounded design and that attempts to correct for confounded effects by statistical modeling reduce power of detection for true differential expression. Simple analysis models without post hoc corrections provide the best results possible for a given experimental design. Normalization improved differential expression testing in both experiments but randomization was the most important factor for establishing accurate results. We conclude that lack of randomization cannot be corrected by normalization or by analytical methods. Proper randomization is essential for successful microarray experiments.     

Detection of stable reference genes for real-time PCR analysis in schizophrenia and bipolar disorder

Gene expression studies using postmortem human brain tissue are a common tool for studying the etiology of psychiatric disorders. Quantitative real-time PCR (qPCR) is an accurate and sensitive technique used for gene expression analysis in which the expression level is quantified by normalization to one or more reference genes. Therefore, accurate data normalization is critical for validating results obtained by qPCR. This study aimed to identify genes that may serve as reference in postmortem dorsolateral-prefrontal cortices (Brodmann’s area 46) of schizophrenics, bipolar disorder (BPD) patients, and control subjects. In the exploratory stage of the analysis, samples of four BPD patients, two schizophrenics, and two controls were quantified using the TaqMan Low Density Array endogenous control panel, containing assays for 16 commonly used reference genes. In the next stage, six of these genes (TFRC, RPLP0, ACTB, POLR2a, B2M, and GAPDH) were quantified by qPCR in 12 samples of each clinical group. Expressional stability of the genes was determined by GeNorm and NormFinder. TFRC and RPLP0 were the most stably expressed genes, whereas the commonly used 18S, POLR2a, and GAPDH were the least stable. This report stresses the importance of examining expressional stability of candidate reference genes in the specific sample collection to be analyzed.     

Identification of stably expressed reference small non‐coding RNAs for microRNA quantification in high‐grade serous ovarian carcinoma tissues

MicroRNAs (miRNAs) belong to a family of small non‐coding RNAs (sncRNAs) playing important roles in human carcinogenesis. Multiple investigations reported miRNAs aberrantly expressed in several cancers, including high‐grade serous ovarian carcinoma (HGS‐OvCa). Quantitative PCR is widely used in studies investigating miRNA expression and the identification of reliable endogenous controls is crucial for proper data normalization. In this study, we aimed to experimentally identify the most stable reference sncRNAs for normalization of miRNA qPCR expression data in HGS‐OvCa. Eleven putative reference sncRNAs for normalization (U6, SNORD48, miR‐92a‐3p, let‐7a‐5p, SNORD61, SNORD72, SNORD68, miR‐103a‐3p, miR‐423‐3p, miR‐191‐5p, miR‐16‐5p) were analysed on a total of 75 HGS‐OvCa and 30 normal tissues, using a highly specific qPCR. Both the normal tissues considered to initiate HGS‐OvCa malignant transformation, namely ovary and fallopian tube epithelia, were included in our study. Stability of candidate endogenous controls was evaluated using an equivalence test and validated by geNorm and NormFinder algorithms. Combining results from the three different statistical approaches, SNORD48 emerged as stably and equivalently expressed between malignant and normal tissues. Among malignant samples, considering groups based on residual tumour, miR‐191‐5p was identified as the most equivalent sncRNA. On the basis of our results, we support the use of SNORD48 as best reference sncRNA for relative quantification in miRNA expression studies between HGS‐OvCa and normal controls, including the first time both the normal tissues supposed to be HGS‐OvCa progenitors. In addition, we recommend miR‐191‐5p as best reference sncRNA in miRNA expression studies with prognostic intent on HGS‐OvCa tissues.     

Quality Assessment and Data Analysis for microRNA Expression Arrays

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.     

Approach for defining endogenous reference genes in gene expression experiments

The quantification of gene expression by real-time polymerase chain reaction (PCR) has revolutionized the field of gene expression analysis. Due to its sensitivity and flexibility it is becoming the method of choice for many investigators. However, good normalization protocols still have to be implemented to facilitate data exchange and comparison. We have designed primers for 10 unrelated genes and developed a simple protocol to detect genes with stable expression that are suitable for use as endogenous reference genes for further use in the normalization of gene expression data obtained by real-time PCR. Using this protocol, we were able to identify human proteosome subunit Y as a reliable endogenous reference gene for human umbilical vein endothelial cells treated for up to 18 h with TNFalpha, IL-4, or IFNgamma and for B cells isolated from healthy controls and patients suffering from IgA nephropathy. Other optional endogenous reference genes that can be considered are phosphomannomutase (PPMM) and actin for endothelial cells and glyceraldehyde-3-phosphate dehydrogenase and PPMM for B cells.