A survey of tools for the analysis of quantitative PCR (qPCR) data <s> Data handling, statistical analysis, and result presentation <s> Real-time PCR is presently the gold standard of gene expression quantification. Configuration of real-time PCR instruments with 384-well reaction blocks, enables the instrument to be used essentially as a low-density array. While PCR will never rival the throughput of microchip arrays, in situations where one is interested in assaying several hundreds of genes, high throughput, real-time PCR is an excellent alternative to microchip arrays. By combining SYBR green detection and 5 μL reaction volume, the associated costs of high-throughput real-time PCR are comparable to microarrays. Described here is a complete protocol to perform real-time PCR in a 384-well configuration. Examples are provided to access numerous PCR primer sequences that may be used for high-throughput real-time PCR. Methods of analysis are described to present real-time PCR data as heat maps and clustered similar to the presentation of cDNA microarray data. An example is provided to profile the expression of over 200 microRNA precursors using high-throughput <s> BIB001 </s> A survey of tools for the analysis of quantitative PCR (qPCR) data <s> Data handling, statistical analysis, and result presentation <s> Two recent letters to the editor of The Plant Cell ([Gutierrez et al., 2008][1]; [Udvardi et al., 2008][2]) highlighted the importance of following correct experimental protocol in quantitative RT-PCR (qRT-PCR). In these letters, the authors outlined measures to allow precise estimation of gene <s> BIB002 </s> A survey of tools for the analysis of quantitative PCR (qPCR) data <s> Data handling, statistical analysis, and result presentation <s> BackgroundReal-time reverse transcription PCR (qRT-PCR) is frequently used for gene expression quantification due to its methodological reproducibility and sensitivity. The gene expression is quantified by normalization to one or more reference genes which are presumed stably expressed throughout a given experiment. The aim of this study was to validate a standardized experimental setup to identifying reference genes for normalization of qRT-PCR in the metastatic and non-metastatic colon cancer.MethodsIn this study, expression of 16 commonly used reference genes was quantified in tumour tissue and individual-matched normal mucosa in 18 non-metastatic colon cancer patients and 20 colon cancer patients with distant metastases using TaqMan Low Density Array (TLDA). The expression stability was determined and compared by means of geNorm and NormFinder.ResultsTwo pairs of genes, HPRT1/PPIA and IPO8/PPIA, were identified to be suitable to normalize gene expression data in metastatic and non-metastatic colon cancer patients, according to geNorm and NormFinder respectively.ConclusionWe propose a standardized approach of finding the most suitable reference gene(s) in every qRT-PCR experiment using TLDA. <s> BIB003 </s> A survey of tools for the analysis of quantitative PCR (qPCR) data <s> Data handling, statistical analysis, and result presentation <s> The polymerase chain reaction (PCR) has matured from a labour- and time-intensive, low throughput qualitative gel-based technique to an easily automated, rapid, high throughput quantitative technology. Real-time quantitative PCR (qPCR) has become the benchmark technology for the detection and quantification of nucleic acids in a research, diagnostic, forensic and biotechnology setting. However, ill-assorted pre-assay conditions, poor assay design and inappropriate data analysis methodologies have resulted in the recurrent publication of data that are at best inconsistent and at worst irrelevant and even misleading. Furthermore, there is a lamentable lack of transparency of reporting, with the "Materials and Methods" sections of many publications, especially those with high impact factors, not fit for the purpose of evaluating the quality of any reported qPCR data. This poses a challenge to the integrity of the scientific literature, with serious consequences not just for basic research, but potentially calamitous implications for drug development and disease monitoring. These issues are being addressed by a set of guidelines that propose a minimum standard for the provision of information for qPCR experiments ("MIQE"). MIQE aims to restructure to-day's free-for-all qPCR methods into a more consistent format that will encourage detailed auditing of experimental detail, data analysis and reporting principles. General implementation of these guidelines is an important requisite for the maturing of qPCR into a robust, accurate and reliable nucleic acid quantification technology. <s> BIB004 </s> A survey of tools for the analysis of quantitative PCR (qPCR) data <s> Data handling, statistical analysis, and result presentation <s> The analysis of different biomolecules is of prime importance for life science research and medical diagnostics. Due to the discovery of new molecules and new emerging bioanalytical problems, there is an ongoing demand for a technology platform that provides a broad range of assays with a user-friendly flexibility and rapid adaptability to new applications. Here we describe a highly versatile microscopy platform, VideoScan, for the rapid and simultaneous analysis of various assay formats based on fluorescence microscopic detection. The technological design is equally suitable for assays in solution, microbead-based assays and cell pattern recognition. The multiplex real-time capability for tracking of changes under dynamic heating conditions makes it a useful tool for PCR applications and nucleic acid hybridization, enabling kinetic data acquisition impossible to obtain by other technologies using endpoint detection. The paper discusses the technological principle of the platform regarding data acquisition and processing. Microbead-based and solution applications for the detection of diverse biomolecules, including antigens, antibodies, peptides, oligonucleotides and amplicons in small reaction volumes, are presented together with a high-content detection of autoimmune antibodies using a HEp-2 cell assay. Its adaptiveness and versatility gives VideoScan a competitive edge over other bioanalytical technologies. <s> BIB005 </s> A survey of tools for the analysis of quantitative PCR (qPCR) data <s> Data handling, statistical analysis, and result presentation <s> Analysis of gene quantities measured by quantitative real-time PCR (qPCR) can be complicated by observations that are below the limit of quantification (LOQ) of the assay. A hierarchical model estimated using MCMC methods was developed to analyze qPCR data of genes with observations that fall below the LOQ (censored observations). Simulated datasets with moderate to very high levels of censoring were used to assess the performance of the model; model results were compared to approaches that replace censored observations with a value on the log scale approximating zero or with values ranging from one to the LOQ of ten gene copies. The model was also compared to a Tobit regression model. Finally, all approaches for handling censored observations were evaluated with DNA extracted from samples that were spiked with known quantities of the antibiotic resistance gene tetL. For the simulated datasets, the model outperformed substitution of all values from 1-10 under all censoring scenarios in terms of bias, mean square error, and coverage of 95% confidence intervals for regression parameters. The model performed as well or better than substitution of a value approximating zero under two censoring scenarios (approximately 57% and 79% censored values). The model also performed as well or better than Tobit regression in two of three censoring scenarios (approximately 79% and 93% censored values). Under the levels of censoring present in the three scenarios of this study, substitution of any values greater than 0 produced the least accurate results. When applied to data produced from spiked samples, the model produced the lowest mean square error of the three approaches. This model provides a good alternative for analyzing large amounts of left-censored qPCR data when the goal is estimation of population parameters. The flexibility of this approach can accommodate complex study designs such as longitudinal studies. <s> BIB006 </s> A survey of tools for the analysis of quantitative PCR (qPCR) data <s> Data handling, statistical analysis, and result presentation <s> Motivation: Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. Despite extensive research in qPCR laboratory protocols, normalization, and statistical analysis, little attention has been given to qPCR non-detects – those reactions failing to produce a minimum amount of signal. Results: We show that the common methods of handling qPCR non-detects lead to biased inference. Furthermore, we show that non-detects do not represent data missing completely at random and likely represent missing data occurring not at random. We propose a model of the missing data mechanism and develop a method to directly model non-detects as missing data. Finally, we show that our approach results in a sizeable reduction in bias when estimating both absolute and differential gene expression. Availability: The proposed algorithm is implemented in the R package, nondetects. This package also contains the raw data for the three example data sets used in this manuscript. The package is freely available at http://mnmccall.com/software and as part of the Bioconductor project. <s> BIB007 </s> A survey of tools for the analysis of quantitative PCR (qPCR) data <s> Data handling, statistical analysis, and result presentation <s> To the Editor ::: ::: In biomedical research, it is often necessary to compare multiple data sets with different distributions. The bar plot, or histogram, is typically used to compare data sets on the basis of simple statistical measures, usually the mean with s.d. or s.e.m. However, summary statistics alone may fail to convey underlying differences in the structure of the primary data (Fig. 1a), which may in turn lead to erroneous conclusions. The box plot, also known as the box-and-whisker plot, represents both the summary statistics and the distribution of the primary data. The box plot thus enables visualization of the minimum, lower quartile, median, upper quartile and maximum of any data set (Fig. 1b). The first documented description of a box plot–like graph by Spear1 defined a range bar to show the median and interquartile range (IQR, or middle 50%) of a data set, with whiskers extended to minimum and maximum values. The most common implementation of the box plot, as defined by Tukey2, has a box that represents the IQR, with whiskers that extend 1.5 times the IQR from the box edges; it also allows for identification of outliers in the data set. Whiskers can also be defined to span the 95% central range of the data3. Other variations, including bean plots4 and violin plots, reveal additional details of the data distribution. These latter variants are less statistically informative but allow better visualization of the data distribution, such as bimodality (Fig. 1b), that may be hidden in a standard box plot. ::: ::: ::: ::: Figure 1 ::: ::: Data visualization with box plots ::: ::: ::: ::: Despite the obvious advantages of the box plot for simultaneous representation of data set and statistical parameters, this method is not in common use, in part because few available software tools allow the facile generation of box plots. For example, the standard spreadsheet tool Excel is unable to generate box plots. Here we describe an open-source application, called BoxPlotR, and an associated web portal that allow rapid generation of customized box plots. A user-defined data matrix is uploaded as a file or pasted directly into the application to generate a basic box plot with options for additional features. Sample size may be represented by the width of each box in proportion to the square root of the number of observations5. Whiskers may be defined according to the criteria of Spear1, Tukey2 or Altman3. The underlying data distribution may be visualized as a violin or bean plot or, alternatively, the actual data may be displayed as overlapping or nonoverlapping points. The 95% confidence interval that two medians are different may be illustrated as notches defined as ±(1.58 × IQR/√n) (ref. 5). There is also an op on to plot the sample means and their confidence intervals. More complex statistical comparisons may be required to ascertain significance according to the specific experimental design6. The output plots may be labeled; customized by color, dimensions and orientation; and exported as publication-quality .eps, .pdf or .svg files. To help ensure that generated plots are accurately described in publications, the application generates a description of the plot for incorporation into a figure legend. ::: ::: The interactive web application is written in R (ref. 7) with the R packages shiny, beanplot4, vioplot, beeswarm and RColorBrewer, and it is hosted on a shiny server to allow for interactive data analysis. User data are held only temporarily and discarded as soon as the session terminates. BoxPlotR is available at http://boxplot.tyerslab.com/ and may be downloaded to run locally or as a virtual machine for VMware and VirtualBox. <s> BIB008
To calculate the Cq value from raw fluorescence data, selected software packages and tools perform preprocessing steps. These may include methods for the reduction of noise (e.g., introduced by technical components) curve smoothing, removal of outliers, normalization, curve fitting, and background subtraction  . In contrast to closed source software, open source software platforms provide full control over such tasks. A further important aspect is the handling of missing values (NA). There are in principle two levels to handle NAs. The first level is on the raw fluorescence data of the amplification curves, which may occur randomly in developmental qPCR technologies, such as the VideoScan platform BIB005 , but also in existing data sets. A common approach of handling missing values is by imputation. Methods include for example cubic spline interpolation or imputation of location parameters such as the mean or median  . The second level of NA handling is after initial Cq calculation. It might be the case that certain reactions are missing corresponding values. This might be due to different factors, such as technical difficulties, detector problems, or low quantity genes. Currently, several methods have been presented to handle missing values. First, they can be simply excluded from any downstream analysis. As long as enough technical replicates are available and not too many reactions are marked as excluded this is a legitimate approach. Second, they can be assigned the Cq value of the maximum number of cycles (often 40), which might deviate the mean of the analyzed samples due to outlier creation. Third, they can be imputed by using the Cq values of technical or biological replicates. Methods range from very simple median calculations to more sophisticated statistical models (e.g., hierarchical models) BIB006 BIB007 . These methods generally yield good results, but may lead to inflated stability values for reference genes BIB003 . After performing relative or absolute quantification, usually different groups (e.g., healthy vs. control, time series) are tested for their statistical significant difference. To allow generation of meaningful results, an experiment ideally should encompass at least three independent biological replicates of each treatment  . As budgetary considerations often hamper the use of multiple replicates, more focus should be given to biological replicates than technical replicates BIB004 . Various statistical methods can be used to test for significant differences. The analysis of variance (ANOVA) can be used to compare treatments using the previously calculated quantification results. If only two conditions are compared, a standard t-test can be applied BIB002 . Final results are often presented in a graphical way allowing easier interpretation and understanding of the obtained findings. The data may be presented as a heatmap and clustered similar to the presentation of microarray data BIB001 . Instead of creating the widely popular bar plots for reporting findings, we recommend the use of box plots as they represent both the summary statistics and the distribution of the primary data BIB008 . Bar plots with error bars are difficult to compare and encourages the perception that the mean is related to the height of the bar rather than the position of its top. They are best used for visualizing counts or proportions. Box plots require a sample size of only 5 and provide more information about samples than conventional error bars. Results obtained from very small sample sizes should be displayed using traditional mean-anderror or column scatter plots  .