Standardisation of data from real-time quantitative PCR methods – evaluation of outliers and comparison of calibration curves

Background  

As real-time quantitative PCR (RT-QPCR) is increasingly being relied upon for the enforcement of legislation and regulations dependent upon the trace detection of DNA, focus has increased on the quality issues related to the technique. Recent work has focused on the identification of factors that contribute towards significant measurement uncertainty in the real-time quantitative PCR technique, through investigation of the experimental design and operating procedure. However, measurement uncertainty contributions made during the data analysis procedure have not been studied in detail. This paper presents two additional approaches for standardising data analysis through the novel application of statistical methods to RT-QPCR, in order to minimise potential uncertainty in results.     

Relative quantification of mRNA: comparison of methods currently used for real-time PCR data analysis

Background  

Fluorescent data obtained from real-time PCR must be processed by some method of data analysis to obtain the relative quantity of target mRNA. The method chosen for data analysis can strongly influence results of the quantification.     

Highly accurate sigmoidal fitting of real-time PCR data by introducing a parameter for asymmetry

Background  

Fitting four-parameter sigmoidal models is one of the methods established in the analysis of quantitative real-time PCR (qPCR) data. We had observed that these models are not optimal in the fitting outcome due to the inherent constraint of symmetry around the point of inflection. Thus, we found it necessary to employ a mathematical algorithm that circumvents this problem and which utilizes an additional parameter for accommodating asymmetrical structures in sigmoidal qPCR data.     

Shape based kinetic outlier detection in real-time PCR

Background  

Real-time PCR has recently become the technique of choice for absolute and relative nucleic acid quantification. The gold standard quantification method in real-time PCR assumes that the compared samples have similar PCR efficiency. However, many factors present in biological samples affect PCR kinetic, confounding quantification analysis. In this work we propose a new strategy to detect outlier samples, called SOD.     

FCI: an R-based algorithm for evaluating uncertainty of absolute real-time PCR quantification

Background  

FCI is an R code for analyzing data from real-time PCR experiments. This algorithm estimates standard curve features as well as nucleic acid concentrations and confidence intervals according to Fieller's theorem.     

GeneSrF and varSelRF: a web-based tool and R package for gene selection and classification using random forest

Background  

Microarray data are often used for patient classification and gene selection. An appropriate tool for end users and biomedical researchers should combine user friendliness with statistical rigor, including carefully avoiding selection biases and allowing analysis of multiple solutions, together with access to additional functional information of selected genes. Methodologically, such a tool would be of greater use if it incorporates state-of-the-art computational approaches and makes source code available.     

Mixture models for analysis of melting temperature data

Background  

In addition to their use in detecting undesired real-time PCR products, melting temperatures are useful for detecting variations in the desired target sequences. Methodological improvements in recent years allow the generation of high-resolution melting-temperature (T_m) data. However, there is currently no convention on how to statistically analyze such high-resolution T_m data.     

Stability of gene contributions and identification of outliers in multivariate analysis of microarray data

Background  

Multivariate ordination methods are powerful tools for the exploration of complex data structures present in microarray data. These methods have several advantages compared to common gene-by-gene approaches. However, due to their exploratory nature, multivariate ordination methods do not allow direct statistical testing of the stability of genes.     

Real-Time Relative qPCR without Reference to Control Samples and Estimation of Run-Specific PCR Parameters from Run-Internal Mini-Standard Curves

Background

Real-Time quantitative PCR is an important tool in research and clinical settings. Here, we describe two new approaches that broaden the scope of real-time quantitative PCR; namely, run-internal mini standard curves (RIMS) and direct real-time relative quantitative PCR (drqPCR). RIMS are an efficient alternative to traditional standard curves and provide both run-specific and target-specific estimates of PCR parameters. The drqPCR enables direct estimation of target ratios without reference to conventional control samples.

Methodology/Principal Findings

In this study, we compared RIMS-based drqPCR with classical quantifications based on external standard curves and the “comparative Ct method”. Specifically, we used a raw real-time PCR dataset as the basis for more than two-and-a-half million simulated quantifications with various user-defined conditions. Compared with classical approaches, we found that RIMS-based drqPCR provided superior precision and comparable accuracy.

Conclusions/Significance

The obviation of referencing to control samples is attractive whenever unpaired samples are quantified. This may be in clinical and research settings; for instance, studies on chimerism, TREC quantifications, copy number variations etc. Also, lab-to-lab comparability can be greatly simplified.     

1,2-propanediol-trehalose mixture as a potent quantitative real-time PCR enhancer

Background  

Quantitative real-time PCR (qPCR) is becoming increasingly important for DNA genotyping and gene expression analysis. For continuous monitoring of the production of PCR amplicons DNA-intercalating dyes are widely used. Recently, we have introduced a new qPCR mix which showed improved amplification of medium-size genomic DNA fragments in the presence of DNA dye SYBR green I (SGI). In this study we tested whether the new PCR mix is also suitable for other DNA dyes used for qPCR and whether it can be applied for amplification of DNA fragments which are difficult to amplify.     

Evaluation of absolute quantitation by nonlinear regression in probe-based real-time PCR

Background  

In real-time PCR data analysis, the cycle threshold (CT) method is currently the gold standard. This method is based on an assumption of equal PCR efficiency in all reactions, and precision may suffer if this condition is not met. Nonlinear regression analysis (NLR) or curve fitting has therefore been suggested as an alternative to the cycle threshold method for absolute quantitation. The advantages of NLR are that the individual sample efficiency is simulated by the model and that absolute quantitation is possible without a standard curve, releasing reaction wells for unknown samples. However, the calculation method has not been evaluated systematically and has not previously been applied to a TaqMan platform. Aim: To develop and evaluate an automated NLR algorithm capable of generating batch production regression analysis.     

Asymmetric microarray data produces gene lists highly predictive of research literature on multiple cancer types

Background  

Much of the public access cancer microarray data is asymmetric, belonging to datasets containing no samples from normal tissue. Asymmetric data cannot be used in standard meta-analysis approaches (such as the inverse variance method) to obtain large sample sizes for statistical power enrichment. Noting that plenty of normal tissue microarray samples exist in studies not involving cancer, we investigated the viability and accuracy of an integrated microarray analysis approach based on significance analysis of microarrays (merged SAM) using a collection of data from separate diseased and normal samples.     

Instance-based concept learning from multiclass DNA microarray data

Background  

Various statistical and machine learning methods have been successfully applied to the classification of DNA microarray data. Simple instance-based classifiers such as nearest neighbor (NN) approaches perform remarkably well in comparison to more complex models, and are currently experiencing a renaissance in the analysis of data sets from biology and biotechnology. While binary classification of microarray data has been extensively investigated, studies involving multiclass data are rare. The question remains open whether there exists a significant difference in performance between NN approaches and more complex multiclass methods. Comparative studies in this field commonly assess different models based on their classification accuracy only; however, this approach lacks the rigor needed to draw reliable conclusions and is inadequate for testing the null hypothesis of equal performance. Comparing novel classification models to existing approaches requires focusing on the significance of differences in performance.     

The MetabolomeExpress Project: enabling web-based processing,analysis and transparent dissemination of GC/MS metabolomics datasets

Background  

Standardization of analytical approaches and reporting methods via community-wide collaboration can work synergistically with web-tool development to result in rapid community-driven expansion of online data repositories suitable for data mining and meta-analysis. In metabolomics, the inter-laboratory reproducibility of gas-chromatography/mass-spectrometry (GC/MS) makes it an obvious target for such development. While a number of web-tools offer access to datasets and/or tools for raw data processing and statistical analysis, none of these systems are currently set up to act as a public repository by easily accepting, processing and presenting publicly submitted GC/MS metabolomics datasets for public re-analysis.     

A general modular framework for gene set enrichment analysis

Background  

Analysis of microarray and other high-throughput data on the basis of gene sets, rather than individual genes, is becoming more important in genomic studies. Correspondingly, a large number of statistical approaches for detecting gene set enrichment have been proposed, but both the interrelations and the relative performance of the various methods are still very much unclear.     

Technical considerations for genotyping multi-allelic copy number variation (CNV), in regions of segmental duplication

Background

Intrachromosomal segmental duplications provide the substrate for non-allelic homologous recombination, facilitating extensive copy number variation in the human genome. Many multi-copy gene families are embedded within genomic regions with high levels of sequence identity (>95%) and therefore pose considerable analytical challenges. In some cases, the complexity involved in analyzing such regions is largely underestimated. Rapid, cost effective analysis of multi-copy gene regions have typically implemented quantitative approaches, however quantitative data are not an absolute means of certainty. Therefore any technique prone to degrees of measurement error can produce ambiguous results that may lead to spurious associations with complex disease.

Results

In this study we have focused on testing the accuracy and reproducibility of quantitative analysis techniques. With reference to the C-C Chemokine Ligand-3-like-1 (CCL3L1) gene, we performed analysis using real-time Quantitative PCR (QPCR), Multiplex Ligation-dependent Probe Amplification (MLPA) and Paralogue Ratio Test (PRT). After controlling for potential outside variables on assay performance, including DNA concentration, quality, preparation and storage conditions, we find that real-time QPCR produces data that does not cluster tightly around copy number integer values, with variation substantially greater than that of the MLPA or PRT systems. We find that the method of rounding real-time QPCR measurements can potentially lead to mis-scoring of copy number genotypes and suggest caution should be exercised in interpreting QPCR data.

Conclusions

We conclude that real-time QPCR is inherently prone to measurement error, even under conditions that would seem favorable for association studies. Our results indicate that potential variability in the physicochemical properties of the DNA samples cannot solely explain the poor performance exhibited by the real-time QPCR systems. We recommend that more robust approaches such as PRT or MLPA should be used to genotype multi-allelic copy number variation in disease association studies and suggest several approaches which can be implemented to ensure the quality of the copy number typing using quantitative methods.     

A Bayesian method for calculating real-time quantitative PCR calibration curves using absolute plasmid DNA standards

Background  

In real-time quantitative PCR studies using absolute plasmid DNA standards, a calibration curve is developed to estimate an unknown DNA concentration. However, potential differences in the amplification performance of plasmid DNA compared to genomic DNA standards are often ignored in calibration calculations and in some cases impossible to characterize. A flexible statistical method that can account for uncertainty between plasmid and genomic DNA targets, replicate testing, and experiment-to-experiment variability is needed to estimate calibration curve parameters such as intercept and slope. Here we report the use of a Bayesian approach to generate calibration curves for the enumeration of target DNA from genomic DNA samples using absolute plasmid DNA standards.