scholarly journals freqpcr: estimation of population allele frequency using qPCR ΔΔCq measures from bulk samples

2021 ◽  
Author(s):  
Masaaki Sudo ◽  
Masahiro Osakabe
Keyword(s):  
Allele Frequency ◽  
Measurement Errors ◽  
Interval Estimation ◽  
R Package ◽  
Bulk Sample ◽  
Dna Yield ◽  
Rare Mutations ◽  
Population Allele Frequency ◽  
The Individual ◽  
Pcr Techniques

AbstractPCR techniques, both quantitative (qPCR) and non-quantitative, have been used to estimate allele frequency in a population. However, the labor required to sample numerous individuals, and subsequently handle each sample, makes quantification of rare mutations, including pesticide resistance genes at the early stages of resistance development, challenging. Meanwhile, pooling DNA from multiple individuals as a “bulk sample” may reduce handling costs. The qPCR output for a bulk sample, however, contains uncertainty owing to variations in DNA yields from each individual, in addition to measurement errors. In this study, we developed a statistical model for the interval estimation of allele frequency using ΔΔCq-based qPCR analyses of multiple bulk samples collected from a population. We assumed a gamma distribution as the individual DNA yield and developed an R package for parameter estimation, which was verified with real DNA samples from acaricide-resistant spider mites, as well as a numerical simulation. Our model resulted in unbiased point estimates of the allele frequency compared with simple averaging of the ΔΔCq values, while their confidence intervals suggest that collecting and pooling additional samples from individuals may produce higher precision than individual PCR tests with moderate sample sizes.

The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

2019 ◽  
Author(s):  
Shinichi Nakagawa ◽  
Malgorzata Lagisz ◽  
Rose E O'Dea ◽  
Joanna Rutkowska ◽  
Yefeng Yang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Forest Plot ◽  
Point Estimates ◽  
Aggregate Effect ◽  
The Individual ◽  
Meta Analyses ◽  
Heterogeneous Effect ◽  
Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

Time Estimation Performance Before, during and after Physical Activity

1988 ◽  
Vol 32 (15) ◽  
pp. 985-989 ◽  
Author(s):  
T. Mihaly ◽  
P.A. Hancock ◽  
M. Vercruyssen ◽  
M. Rahimi
Keyword(s):  
Interval Estimation ◽  
Time Estimation ◽  
Cycle Ergometer ◽  
Estimation Accuracy ◽  
Physical Work ◽  
Time Interval ◽  
Individual Subject ◽  
Knowledge Of Results ◽  
The Individual ◽  
Experimental Findings

An experiment is reported which evaluated performance on a 10-sec time interval estimation task before, during and after physical work on cycle ergometer at intensities of 30 and 60% VO2max, as scaled to the individual subject. Results from the eleven subjects tested indicate a significant increase in variability of estimates during exercise compared to non-exercise phases. Such a trend was also seen in the mean of estimates, where subjects significantly underestimated the target interval (10 seconds) during exercise. Subjects also performed more accurately with information feedback than without knowledge of results, but they were still not able to overcome the effects of exercise. As suggested by the experimental findings, decreased estimation accuracy and increased variability can be expected during physical work and is part of a body of evidence which indicates that exercise and its severity has a substantive impact on perceptual and cognitive performance.

scholarly journals Power analysis in a SMART design: sample size estimation for determining the best embedded dynamic treatment regime

Biostatistics ◽  
2018 ◽  
Vol 21 (3) ◽  
pp. 432-448 ◽  
Author(s):  
William J Artman ◽  
Inbal Nahum-Shani ◽  
Tianshuang Wu ◽  
James R Mckay ◽  
Ashkan Ertefaie
Keyword(s):  
Power Analysis ◽  
Ad Hoc ◽  
Treatment Effectiveness ◽  
R Package ◽  
Treatment Regime ◽  
Size Estimation ◽  
Dynamic Treatment Regime ◽  
Dynamic Treatment ◽  
The Individual ◽  
Number Of Individuals

Summary Sequential, multiple assignment, randomized trial (SMART) designs have become increasingly popular in the field of precision medicine by providing a means for comparing more than two sequences of treatments tailored to the individual patient, i.e., dynamic treatment regime (DTR). The construction of evidence-based DTRs promises a replacement to ad hoc one-size-fits-all decisions pervasive in patient care. However, there are substantial statistical challenges in sizing SMART designs due to the correlation structure between the DTRs embedded in the design (EDTR). Since a primary goal of SMARTs is the construction of an optimal EDTR, investigators are interested in sizing SMARTs based on the ability to screen out EDTRs inferior to the optimal EDTR by a given amount which cannot be done using existing methods. In this article, we fill this gap by developing a rigorous power analysis framework that leverages the multiple comparisons with the best methodology. Our method employs Monte Carlo simulation to compute the number of individuals to enroll in an arbitrary SMART. We evaluate our method through extensive simulation studies. We illustrate our method by retrospectively computing the power in the Extending Treatment Effectiveness of Naltrexone (EXTEND) trial. An R package implementing our methodology is available to download from the Comprehensive R Archive Network.

scholarly journals Mutation Rate Model Used in the DNA VIEW Program

2020 ◽  
Vol 10 (10) ◽  
pp. 3585 ◽  
Author(s):  
Tomasz Krajka
Keyword(s):  
Computer Simulation ◽  
Allele Frequency ◽  
Mutation Rate ◽  
Allele Frequencies ◽  
Rate Model ◽  
Dna Database ◽  
Mutation Model ◽  
Population Allele Frequency ◽  
Frequency Changes

The first problem considered in this paper is the problem of correctness of a mutation model used in the DNA VIEW program. To this end, we theoretically predict population allele frequency changes in time according to this and similar models (we determine the limit frequencies of alleles—they are uniformly distributed). Furthermore, we evaluate the speed of the above changes using computer simulation applied to our DNA database. Comparing uniformly distributed allele frequencies with these existing in the population (for example, using entropy), we conclude that this mutation model is not correct. The evolution does not follow this direction (direction of uniformly distributed frequencies). The second problem relates to the determination of the extent to which an incorrect mutation model can disturb DNA VIEW program results. We show that in typical computations (simple paternity testing without maternal mutation) this influence is negligible, but in the case of maternal mutation, this should be taken into account. Furthermore, we show that this model is inconsistent from a theoretical viewpoint. Equivalent methods result in different error levels.

scholarly journals Lipid Mini-On: mining and ontology tool for enrichment analysis of lipidomic data

2019 ◽  
Vol 35 (21) ◽  
pp. 4507-4508 ◽  
Author(s):  
Geremy Clair ◽  
Sarah Reehl ◽  
Kelly G Stratton ◽  
Matthew E Monroe ◽  
Malak M Tfaily ◽  
...  
Keyword(s):  
Peat Soil ◽  
Enrichment Analysis ◽  
R Package ◽  
Lipid Classes ◽  
Supplementary Information ◽  
Mass Spec ◽  
Shiny App ◽  
Lung Endothelial Cells ◽  
Lipid Enrichment ◽  
The Individual

Abstract Summary Here we introduce Lipid Mini-On, an open-source tool that performs lipid enrichment analyses and visualizations of lipidomics data. Lipid Mini-On uses a text-mining process to bin individual lipid names into multiple lipid ontology groups based on the classification (e.g. LipidMaps) and other characteristics, such as chain length. Lipid Mini-On provides users with the capability to conduct enrichment analysis of the lipid ontology terms using a Shiny app with options of five statistical approaches. Lipid classes can be added to customize the user’s database and remain updated as new lipid classes are discovered. Visualization of results is available for all classification options (e.g. lipid subclass and individual fatty acid chains). Results are also visualized through an editable network of relationships between the individual lipids and their associated lipid ontology terms. The utility of the tool is demonstrated using biological (e.g. human lung endothelial cells) and environmental (e.g. peat soil) samples. Availability and implementation Rodin (R package: https://github.com/PNNL-Comp-Mass-Spec/Rodin), Lipid Mini-On Shiny app (https://github.com/PNNL-Comp-Mass-Spec/LipidMiniOn) and Lipid Mini-On online tool (https://omicstools.pnnl.gov/shiny/lipid-mini-on/). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Radtools: R utilities for smooth navigation of medical image data

2018 ◽  
Author(s):  
Pamela H Russell ◽  
Debashis Ghosh
Keyword(s):  
Medical Image ◽  
Image Data ◽  
R Package ◽  
Image Series ◽  
Link Type ◽  
File Formats ◽  
Medical Image Data ◽  
Image Metadata ◽  
The Individual ◽  
Radiological Image

AbstractThe radiology community has adopted several widely used standards for medical image files, including the popular DICOM (Digital Imaging and Communication in Medicine) and NIfTI (Neuroimaging Informatics Technology Initiative) standards. These file formats include image intensities as well as potentially extensive metadata. The NIfTI standard specifies a particular set of header fields describing the image and minimal information about the scan. DICOM headers can include any of over 4,000 available metadata attributes spanning a variety of topics. NIfTI files contain all slices for an image series, while DICOM files capture single slices and image series are typically organized into a directory. Each DICOM file contains metadata for the image series as well as the individual image slice.The programming environment R is popular for data analysis due to its free and open code, active ecosystem of tools and users, and excellent system of contributed packages. Currently, many published radiological image analyses are performed with proprietary software or custom unpublished scripts. However, R is increasing in popularity in this area due to several packages for processing and analysis of image files. While these R packages handle image import and processing, no existing package makes image metadata conveniently accessible. Extracting image metadata, combining across slices, and converting to useful formats can be prohibitively cumbersome, especially for DICOM files.We present radtools, an R package for smooth navigation of medical image data. Radtools makes the problem of extracting image metadata trivially simple, providing simple functions to explore and return information in familiar R data structures. Radtools also facilitates extraction of image data and viewing of image slices. The package is freely available under the MIT license at https://github.com/pamelarussell/radtools and is easily installable from the Comprehensive R Archive Network (https://cran.r-project.org/package=radtools).

scholarly journals Getting the most out of family data with the R package fSRM

2016 ◽  
Author(s):  
Lara Stas ◽  
Felix D. Schönbrodt ◽  
Tom Loeys
Keyword(s):  
Social Relations ◽  
R Package ◽  
Family Data ◽  
Individual Level ◽  
The Social ◽  
The Family ◽  
Research Questions ◽  
The Individual ◽  
User Friendly ◽  
Different Levels

Family research aims to explore family processes, but is often limited to the examination of unidirectional processes. As the behavior of one person has consequences that go beyond that one individual, Family functioning should be investigated in its full complexity. The Social Relations Model (SRM; Kenny & La Voie, 1984) is a conceptual and analytical model which can disentangle family data from a round-robin design at three different levels: the individual level (actor and partner effects), the dyadic level (relationship effects) and the family level (family effect). Its statistical complexity may however be a hurdle for family researchers. The user-friendly R package fSRM performs almost automatically those rather complex SRM analyses and introduces new possibilities for assessing differences between SRM-means and between SRM-variances, both within and between groups of families. Using family data on negative processes, different type of research questions are formulated and corresponding analyses with fSRM are presented.

scholarly journals Efficient approaches for large scale GWAS studies with genotype uncertainty

2019 ◽  
Author(s):  
Emil Jørsboe ◽  
Anders Albrechtsen
Keyword(s):  
Population Structure ◽  
Allele Frequency ◽  
Statistical Power ◽  
Large Scale ◽  
Association Studies ◽  
Genetic Data ◽  
Data Sets ◽  
Sequencing Data ◽  
The Individual ◽  
Genotype Probabilities

1AbstractIntroductionAssociation studies using genetic data from SNP-chip based imputation or low depth sequencing data provide a cost efficient design for large scale studies. However, these approaches provide genetic data with uncertainty of the observed genotypes. Here we explore association methods that can be applied to data where the genotype is not directly observed. We investigate how using different priors when estimating genotype probabilities affects the association results in different scenarios such as studies with population structure and varying depth sequencing data. We also suggest a method (ANGSD-asso) that is computational feasible for analysing large scale low depth sequencing data sets, such as can be generated by the non-invasive prenatal testing (NIPT) with low-pass sequencing.MethodsANGSD-asso’s EM model works by modelling the unobserved genotype as a latent variable in a generalised linear model framework. The software is implemented in C/C++ and can be run multi-threaded enabling the analysis of big data sets. ANGSD-asso is based on genotype probabilities, they can be estimated in various ways, such as using the sample allele frequency as a prior, using the individual allele frequencies as a prior or using haplotype frequencies from haplotype imputation. Using simulations of sequencing data we explore how genotype probability based method compares to using genetic dosages in large association studies with genotype uncertainty.Results & DiscussionOur simulations show that in a structured population using the individual allele frequency prior has better power than the sample allele frequency. If there is a correlation between genotype uncertainty and phenotype, then the individual allele frequency prior also helps control the false positive rate. In the absence of population structure the sample allele frequency prior and the individual allele frequency prior perform similarly. In scenarios with sequencing depth and phenotype correlation ANGSD-asso’s EM model has better statistical power and less bias compared to using dosages. Lastly when adding additional covariates to the linear model ANGSD-asso’s EM model has more statistical power and provides less biased effect sizes than other methods that accommodate genotype uncertainly, while also being much faster. This makes it possible to properly account for genotype uncertainty in large scale association studies.

scholarly journals powerEQTL: An R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis

2020 ◽  
Author(s):  
Xianjun Dong ◽  
Xiaoqi Li ◽  
Tzuu-Wang Chang ◽  
Scott T Weiss ◽  
Weiliang Qiu
Keyword(s):  
Gene Expression ◽  
Sample Size ◽  
Single Cell ◽  
Allele Frequency ◽  
R Package ◽  
Power Calculation ◽  
Eqtl Analysis ◽  
Genome Wide Association Studies ◽  
User Friendly ◽  
Bulk Tissue

Genome-wide association studies (GWAS) have revealed thousands of genetic loci for common diseases. One of the main challenges in the post-GWAS era is to understand the causality of the genetic variants. Expression quantitative trait locus (eQTL) analysis has been proven to be an effective way to address this question by examining the relationship between gene expression and genetic variation in a sufficiently powered cohort. However, it is often tricky to determine the sample size at which a variant with a specific allele frequency will be detected to associate with gene expression with sufficient power. This is particularly demanding with single-cell RNAseq studies. Therefore, a user-friendly tool to perform power analysis for eQTL at both bulk tissue and single-cell level will be critical. Here, we presented an R package called powerEQTL with flexible functions to calculate power, minimal sample size, or detectable minor allele frequency in both bulk tissue and single-cell eQTL analysis. A user-friendly, program-free web application is also provided, allowing customers to calculate and visualize the parameters interactively.

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