1,720,999 research outputs found
Digital PCR threshold robustness analysis and optimization using dipcensR
No full textDigital polymerase chain reaction (dPCR) is a best-in-class molecular biology technique for the accurate and precise quantification of nucleic acids. The recent maturation of dPCR technology allows the quantification of up to thousands of targeted nucleic acids per instrument per day. A key step in the dPCR data analysis workflow is the classification of partitions into two classes based on their partition intensities: partitions either containing or lacking target nucleic acids of interest. Much effort has been invested in the design and tailoring of automated dPCR partition classification procedures, and such procedures will be increasingly important as the technology ventures into high-throughput applications. However, automated partition classification is not fail-safe, and evaluation of its accuracy is highly advised. This accuracy evaluation is a manual endeavor and is becoming a bottleneck for high-throughput dPCR applications. Here, we introduce dipcensR, the first data-analysis procedure that automates the assessment of any linear partition classifier’s partition classification accuracy, offering potentially substantial efficiency gains. dipcensR is based on a robustness evaluation of said partition classification and flags classifications with low robustness as needing review. Additionally, dipcensR’s robustness analysis underpins (optional) automatic optimization of partition classification to achieve maximal robustness. A freely available R implementation supports dipcensR’s use
Benchmarking digital PCR partition classification methods with empirical and simulated duplex data
No full textDigital PCR (dPCR) is a highly accurate technique for the quantification of target nucleic acid(s). It has shown great potential in clinical applications, like tumor liquid biopsy and validation of biomarkers. Accurate classification of partitions based on end-point fluorescence intensities is crucial to avoid biased estimators of the concentration of the target molecules. We have evaluated many clustering methods, from general-purpose methods to specific methods for dPCR and flowcytometry, on both simulated and real-life data. Clustering method performance was evaluated by simulating various scenarios. Based on our extensive comparison of clustering methods, we describe the limits of these methods, and formulate guidelines for choosing an appropriate method. In addition, we have developed a novel method for simulating realistic dPCR data. The method is based on a mixture distribution of a Poisson point process and a skew- distribution, which enables the generation of irregularities of cluster shapes and randomness of partitions between clusters ('rain') as commonly observed in dPCR data. Users can fine-tune the model parameters and generate labeled datasets, using their own data as a template. Besides, the database of experimental dPCR data augmented with the labeled simulated data can serve as training and testing data for new clustering methods. The simulation method is available as an R Shiny app
Polytect: an automatic clustering and labeling method for multicolor digital PCR data
No full textDigital polymerase chain reaction (dPCR) is a state-of-the-art targeted quantification method of nucleic acids. The technology is based on massive partitioning of a reaction mixture into individual PCR reactions. The resulting partition-level end-point fluorescence intensities are used to classify partitions as positive or negative, i.e. containing or not containing the target nucleic acid(s). Many automatic dPCR partition classification methods have been proposed, but they are limited to the analysis of single- or dual-color dPCR data. While general-purpose or flow cytometry clustering methods can be directly applied to multicolor dPCR data, these methods do not exploit the approximate prior knowledge on cluster center locations available in dPCR data. We present Polytect, a method that relies on crude cluster results from flowPeaks, previously shown to offer good partition classification performance, and subsequently refines flowPeaks' results by automatic cluster merging and cluster labeling, exploiting the prior knowledge on cluster center locations. Comparative analyses with established methods such as flowPeaks, dpcp, and ddPCRclust reveal that Polytect often surpasses established methods, both on empirical and simulated data. Polytect manages to merge excess clusters, while also successfully identifying empty clusters when fewer than the maximally observable number of clusters are present. On par with recent developments in instruments, Polytect extends beyond two-color data. The method is available as an R package and R Shiny app (https://digpcr.shinyapps.io/Polytect/).This work is funded in part by Bijzonder Onderzoeksfonds UGent (BOF, grant 01IO0420), and Agentschap voor Innoveren en Onderneme (VLAIO, grant HBC_2022.0673)
Flexible methods for uncertainty estimation of digital PCR data
No full textDigital PCR (dPCR) is an accurate technique for quantifying nucleic acids, but variance estimation remains a challenge due to violations of the assumptions underlying many existing methods. To address this, we propose two generic approaches, NonPVar and BinomVar, for calculating variance in dPCR data. These methods are evaluated using simulated and empirical data, incorporating common sources of variability. Unlike classical methods, our approaches are flexible and applicable to complex functions of partition counts like copy number variation (CNV), fractional abundance, and DNA integrity. An R Shiny app is provided to facilitate method selection and implementation. Our findings demonstrate that these methods improve accuracy and adaptability, offering robust tools for uncertainty estimation in dPCR experiments
Some key considerations regarding the design and evaluation of high-throughput sequencing-based biallelic chimerism assays
No full textGoing Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
- …
