Filtering maxRatio results with machine learning models increases quantitative PCR accuracy over the fit point method

Marongiu, Luigi; Shain, Eric B.; Shain, K.; Allgayer, Heike

doi:10.1016/j.mimet.2019.105803

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…In order to apply this approach to the amplification and denaturation curves, we characterized the behaviour of curves with several key features such as the maxRatio value for the amplification curve. This feature had already been used for building ML models based on SVM algorithms, returning an accuracy at 1 for high-throughput qPCR analysis classification 13 . The asymmetry (skewness) and the distribution of fluorescence asymmetry (kurtosis) were the most informative features comparing to the Tm and AUC Tm of denaturation curves.…”

Section: Discussionmentioning

confidence: 99%

Machine learning to improve the interpretation of intercalating dye-based quantitative PCR results

Godmer

Bigot

Gianetto

et al. 2022

Sci Rep

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This study aimed to evaluate the contribution of Machine Learning (ML) approach in the interpretation of intercalating dye-based quantitative PCR (IDqPCR) signals applied to the diagnosis of mucormycosis. The ML-based classification approach was applied to 734 results of IDqPCR categorized as positive (n = 74) or negative (n = 660) for mucormycosis after combining “visual reading” of the amplification and denaturation curves with clinical, radiological and microbiological criteria. Fourteen features were calculated to characterize the curves and injected in several pipelines including four ML-algorithms. An initial subset (n = 345) was used for the conception of classifiers. The classifier predictions were combined with majority voting to estimate performances of 48 meta-classifiers on an external dataset (n = 389). The visual reading returned 57 (7.7%), 568 (77.4%) and 109 (14.8%) positive, negative and doubtful results respectively. The Kappa coefficients of all the meta-classifiers were greater than 0.83 for the classification of IDqPCR results on the external dataset. Among these meta-classifiers, 6 exhibited Kappa coefficients at 1. The proposed ML-based approach allows a rigorous interpretation of IDqPCR curves, making the diagnosis of mucormycosis available for non-specialists in molecular diagnosis. A free online application was developed to classify IDqPCR from the raw data of the thermal cycler output (http://gepamy-sat.asso.st/).

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Section: Discussionmentioning

confidence: 99%

Machine learning to improve the interpretation of intercalating dye-based quantitative PCR results

Godmer

Bigot

Gianetto

et al. 2022

Sci Rep

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“…With the availability of machine learning techniques, several groups have started to use these in qPCR experiments, such as Support Vector Machine based classifiers employed in genotyping sequence variants based on high resolution melting curves [3], or to generally improve Cq determination [6,9]. One may ask "Why not use deep learning on amplification curves?"…”

Section: Introductionmentioning

confidence: 99%

PCRedux: A Data Mining and Machine Learning Toolkit for qPCR Experiments

Burdukiewicz

Spiess

Rafacz

et al. 2021

Preprint

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Motivation: Quantitative Real-time PCR (qPCR) is a widely used -omics method for the precise quantification of nucleic acids, in which the result is associated with the presence/absence or quantity of a specific nucleic acid sequence. As the amount of qPCR data increases worldwide, the manual assessment of results becomes challenging and difficult to reproduce. To overcome this, some automatable characteristics of amplification curves have been described in the literature, often with an appropriate "rule of thumb". Results: We developed PCRedux to analyze and calculate 90 numerical qPCR amplification curve descriptors (features) from large datasets of qPCR amplification curves that are aimed for interpretable machine learning and development of decision support systems. In a case study of a diverse dataset with 3181 positive, negative and ambiguous amplification curves, as assessed by three human raters, we demonstrate a sensitivity > 99 % and specificity > 97 % in detecting positive and negative amplification. PCRedux is unique as it goes beyond traditional qPCR analysis to capture curvature properties that improve the characterization and classification of amplification curves. The calculation of the features is reproducible and objective, since R is used as a controllable working environment. PCRedux is not a black box, but open source software following on the principle of mathematically interpretable features. These can be combined with user-defined labels for automatic multi-category classification and regression in machine learning.

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“…An alternative method, maxRatio, was introduced to overcome these issues 9 . It has been reported that maxRatio conferred a marginal increase in assay accuracy over FP 10,11 . FP provides only a quantification cycle (Cq) value, which is then used to calculate VL.…”

Section: Introductionmentioning

confidence: 99%

maxRatio improves the detection of samples with abnormal amplification profiles on QIAgen’s artus HIV-1 qPCR assay

et al. 2021

Self Cite

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Background: Accurate viral load (VL) determination is paramount to determine the efficacy of anti-HIV-1 therapy. The conventional method used, fit-point (FP), assumes an equal efficiency in the polymerase chain reaction (PCR) among samples that might not hold for low-input templates. An alternative approach, maxRatio, was introduced to compensate for inhibition in PCR. Methods: Herein, we assessed whether maxRatio could improve VL quantification using 2,544 QIAgen artus HI virus-1 RT-PCR reactions. The assay’s standard dilutions were used to build external standard curves with either FP or maxRatio that re-calculated the VLs. Results: FP and maxRatio were highly comparable (Pearson’s ρ=0.994, Cohen’s κ=0.885), and the combination of the two methods identified samples (n=41) with aberrant amplification profiles. Conclusions: The combination of maxRatio and FP could improve the predictive value of the assay.

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Filtering maxRatio results with machine learning models increases quantitative PCR accuracy over the fit point method

Cited by 5 publications

References 8 publications

Machine learning to improve the interpretation of intercalating dye-based quantitative PCR results

Machine learning to improve the interpretation of intercalating dye-based quantitative PCR results

PCRedux: A Data Mining and Machine Learning Toolkit for qPCR Experiments

maxRatio improves the detection of samples with abnormal amplification profiles on QIAgen’s artus HIV-1 qPCR assay

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