scholarly journals MendelianRandomization v0.5.0: updates to an R package for performing Mendelian randomization analyses using summarized data

2020 ◽  
Vol 5 ◽  
pp. 252
Author(s):  
Jim R. Broadbent ◽  
Christopher N. Foley ◽  
Andrew J. Grant ◽  
Amy M. Mason ◽  
James R. Staley ◽  
...  
Keyword(s):  
Data Visualization ◽  
Software Package ◽  
Mendelian Randomization ◽  
R Package ◽  
Robust Methods ◽  
Software Environment ◽  
R Software ◽  
Graphical Output ◽  
Web Resource ◽  
Randomization Analysis

The MendelianRandomization package is a software package written for the R software environment that implements methods for Mendelian randomization based on summarized data. In this manuscript, we describe functions that have been added to the package or updated in recent years. These features can be divided into four categories: robust methods for Mendelian randomization, methods for multivariable Mendelian randomization, functions for data visualization, and the ability to load data into the package seamlessly from the PhenoScanner web-resource. We provide examples of the graphical output produced by the data visualization commands, as well as syntax for obtaining suitable data and performing a Mendelian randomization analysis in a single line of code.

scholarly journals MendelianRandomization v0.5.0: updates to an R package for performing Mendelian randomization analyses using summarized data

2020 ◽  
Vol 5 ◽  
pp. 252
Author(s):  
Jim R. Broadbent ◽  
Christopher N. Foley ◽  
Andrew J. Grant ◽  
Amy M. Mason ◽  
James R. Staley ◽  
...  
Keyword(s):  
Data Visualization ◽  
Software Package ◽  
Mendelian Randomization ◽  
R Package ◽  
Robust Methods ◽  
Software Environment ◽  
R Software ◽  
Graphical Output ◽  
Web Resource ◽  
Randomization Analysis

The MendelianRandomization package is a software package written for the R software environment that implements methods for Mendelian randomization based on summarized data. In this manuscript, we describe functions that have been added to the package or updated in recent years. These features can be divided into four categories: robust methods for Mendelian randomization, methods for multivariable Mendelian randomization, functions for data visualization, and the ability to load data into the package seamlessly from the PhenoScanner web-resource. We provide examples of the graphical output produced by the data visualization commands, as well as syntax for obtaining suitable data and performing a Mendelian randomization analysis in a single line of code.

scholarly journals Identifying home locations in human mobility data: an open-source R package for comparison and reproducibility

2021 ◽  
Author(s):  
Qingqing Chen ◽  
Ate Poorthuis
Keyword(s):  
Software Package ◽  
Ad Hoc ◽  
Human Mobility ◽  
Building Blocks ◽  
R Package ◽  
Location Based Services ◽  
R Software ◽  
Mobility Data ◽  
Residential Population ◽  
Research Goal

Identifying meaningful locations, such as home or work, from human mobility data has become an increasingly common prerequisite for geographic research. Although location-based services (LBS) and other mobile technology have rapidly grown in recent years, it can be challenging to infer meaningful places from such data, which - compared to conventional datasets – can be devoid of context. Existing approaches are often developed ad-hoc and can lack transparency and reproducibility. To address this, we introduce an R software package for inferring home locations from LBS data. The package implements pre-existing algorithms and provides building blocks to make writing algorithmic ‘recipes’ more convenient. We evaluate this approach by analyzing a de-identified LBS dataset from Singapore that aims to balance ethics and privacy with the research goal of identifying meaningful locations. We show that ensemble approaches, combining multiple algorithms, can be especially valuable in this regard as the resulting patterns of inferred home locations closely correlate with the distribution of residential population. We hope this package, and others like it, will contribute to an increase in use and sharing of comparable algorithms, research code and data. This will increase transparency and reproducibility in mobility analyses and further the ongoing discourse around ethical big data research.

scholarly journals easyFulcrum: An R package to process and analyze ecological sampling data generated using the Fulcrum mobile application

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0254293 ◽  
Author(s):  
Matteo Di Bernardo ◽  
Timothy A. Crombie ◽  
Daniel E. Cook ◽  
Erik C. Andersen
Keyword(s):  
Mobile Application ◽  
Large Scale ◽  
Data Entry ◽  
R Package ◽  
Environmental Data ◽  
Software Environment ◽  
R Software ◽  
Natural Sampling ◽  
Disparate Data ◽  
Ecological Sampling

Large-scale ecological sampling can be difficult and costly, especially for organisms that are too small to be easily identified in a natural environment by eye. Typically, these microscopic floral and fauna are sampled by collecting substrates from nature and then separating organisms from substrates in the laboratory. In many cases, diverse organisms can be identified to the species-level using molecular barcodes. To facilitate large-scale ecological sampling of microscopic organisms, we used a geographic data-collection platform for mobile devices called Fulcrum that streamlines the organization of geospatial sampling data, substrate photographs, and environmental data at natural sampling sites. These sampling data are then linked to organism isolation data from the laboratory. Here, we describe the easyFulcrum R package, which can be used to clean, process, and visualize ecological field sampling and isolation data exported from the Fulcrum mobile application. We developed this package for wild nematode sampling, but it can be used with other organisms. The advantages of using Fulcrum combined with easyFulcrum are (1) the elimination of transcription errors by replacing manual data entry and/or spreadsheets with a mobile application, (2) the ability to clean, process, and visualize sampling data using a standardized set of functions in the R software environment, and (3) the ability to join disparate data to each other, including environmental data from the field and the molecularly defined identities of individual specimens isolated from samples.

scholarly journals OmicCircos: A Simple-to-Use R Package for the Circular Visualization of Multidimensional Omics Data

2014 ◽  
Vol 13 ◽  
pp. CIN.S13495 ◽  
Author(s):  
Ying Hu ◽  
Chunhua Yan ◽  
Chih-Hao Hsu ◽  
Qing-Rong Chen ◽  
Kelvin Niu ◽  
...  
Keyword(s):  
Copy Number ◽  
Software Package ◽  
Expression Patterns ◽  
R Package ◽  
Copy Number Variations ◽  
High Quality ◽  
R Software ◽  
Genomic Features ◽  
Genomic Variations ◽  
Methylation Patterns

Summary OmicCircos is an R software package used to generate high-quality circular plots for visualizing genomic variations, including mutation patterns, copy number variations (CNVs), expression patterns, and methylation patterns. Such variations can be displayed as scatterplot, line, or text-label figures. Relationships among genomic features in different chromosome positions can be represented in the forms of polygons or curves. Utilizing the statistical and graphic functions in an R/Bioconductor environment, OmicCircos performs statistical analyses and displays results using cluster, boxplot, histogram, and heatmap formats. In addition, OmicCircos offers a number of unique capabilities, including independent track drawing for easy modification and integration, zoom functions, link-polygons, and position-independent heatmaps supporting detailed visualization. Availability and Implementation OmicCircos is available through Bioconductor at http://www.bioconductor.org/packages/devel/bioc/html/OmicCircos.html . An extensive vignette in the package describes installation, data formatting, and workflow procedures. The software is open source under the Artistic—2.0 license.

scholarly journals New Fast ApEn and SampEn Entropy Algorithms Implementation and Their Application to Supercomputer Power Consumption

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 863 ◽  
Author(s):  
Jiří Tomčala
Keyword(s):  
Time Series ◽  
Power Consumption ◽  
Real World ◽  
Software Package ◽  
World System ◽  
Approximate Entropy ◽  
R Package ◽  
Sample Entropy ◽  
R Software

Approximate Entropy and especially Sample Entropy are recently frequently used algorithms for calculating the measure of complexity of a time series. A lesser known fact is that there are also accelerated modifications of these two algorithms, namely Fast Approximate Entropy and Fast Sample Entropy. All these algorithms are effectively implemented in the R software package TSEntropies. This paper contains not only an explanation of all these algorithms, but also the principle of their acceleration. Furthermore, the paper contains a description of the functions of this software package and their parameters, as well as simple examples of using this software package to calculate these measures of complexity of an artificial time series and the time series of a complex real-world system represented by the course of supercomputer infrastructure power consumption. These time series were also used to test the speed of this package and to compare its speed with another R package pracma. The results show that TSEntropies is up to 100 times faster than pracma and another important result is that the computational times of the new Fast Approximate Entropy and Fast Sample Entropy algorithms are up to 500 times lower than the computational times of their original versions. At the very end of this paper, the possible use of this software package TSEntropies is proposed.

scholarly journals easyFulcrum: An R package to process and analyze ecological sampling data generated using the Fulcrum mobile application

2021 ◽  
Author(s):  
Timothy A Crombie ◽  
Matteo Di Bernardo ◽  
Daniel E Cook ◽  
Erik Andersen
Keyword(s):  
Mobile Application ◽  
Large Scale ◽  
Data Entry ◽  
R Package ◽  
Environmental Data ◽  
Software Environment ◽  
R Software ◽  
Natural Sampling ◽  
Disparate Data ◽  
Ecological Sampling

Large-scale ecological sampling can be difficult and costly, especially for organisms that are too small to be easily identified in a natural environment by eye. Typically, these microscopic floral and fauna are sampled by collecting substrates from nature and then separating organisms from substrates in the laboratory. In many cases, diverse organisms can be identified to the species-level using molecular barcodes. To facilitate large-scale ecological sampling of microscopic organisms, we used a geographic data-collection platform for mobile devices called Fulcrum that streamlines the organization of geospatial sampling data, substrate photographs, and environmental data at natural sampling sites. These sampling data are then linked to organism isolation data from the laboratory. Here, we describe the easyFulcrum R package, which can be used to clean, process, and visualize ecological field sampling and isolation data exported from the Fulcrum mobile application. We developed this package for wild nematode sampling, but it is extensible to other organisms. The advantages of using Fulcrum combined with easyFulcrum are (1) the elimination of transcription errors by replacing manual data entry and/or spreadsheets with a mobile application, (2) the ability to clean, process, and visualize sampling data using a standardized set of functions in the R software environment, and (3) the ability to join disparate data to each other, including environmental data from the field and the molecularly defined identities of individual specimens isolated from samples.

scholarly journals Association of serum 25-hydroxyvitamin D with metabolic syndrome and type 2 diabetes: a one sample Mendelian randomization study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jing Xiao ◽  
Jingyi Lv ◽  
Shiyu Wang ◽  
Yang Zhou ◽  
Lunwen Chen ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Metabolic Syndrome ◽  
Vitamin D ◽  
Vitamin D Deficiency ◽  
Mendelian Randomization ◽  
Middle Aged ◽  
Hydroxyvitamin D ◽  
25 Hydroxyvitamin D ◽  
Randomization Analysis

Abstract Background Vitamin D deficiency has been associated with type 2 diabetes (T2D) and metabolic syndrome (MS) and its components. However, it is unclear whether a low concentration of vitamin D is the cause or consequence of these health conditions. Thus, this study aimed to evaluate the association of vitamin D concentrations and its genetic risk scores (GRSs) with MS and its component diseases, such as T2D, in middle-aged and elderly participants from rural eastern China. Methods A subset of 2393 middle-aged and elderly individuals were selected from 70,458 participants of the Nantong Chronic Diseases Study of 2017–2018 in China. We used two 25-hydroxyvitamin D (25[OH]D) synthesis single-nucleotide polymorphisms (SNPs) (DHCR7-rs12785878 and CYP2R1-rs10741657) and two 25(OH) D metabolism SNPs (GC-rs2282679 and CYP24A1-rs6013897) for creating GRSs, which were used as instrumental variables to assess the effect of genetically lowered 25(OH) D concentrations on MS and T2D based on the Wald ratio. F statistics were used to validate that the four SNPs genetically determined 25(OH) D concentrations. Results Compared to vitamin D sufficient individuals, individuals with vitamin D insufficiency had an odds ratio (OR [95% confidence interval {CI}]) of MS of 1.30 (1.06–1.61) and of T2D of 1.32 (1.08–1.64), individuals with vitamin D deficiency had an ORs (95% CI) of MS of 1.50 (1.24–1.79) and of T2D of 1.47 (1.12–1.80), and those with vitamin D severe deficiency had an ORs (95% CI) of MS of 1.52 (1.29–1.85) and of T2D of 1.54 (1.27–1.85). Mendelian randomization analysis showed a 25-nmol/L decrease in genetically instrumented serum 25(OH) D concentrations using the two synthesis SNPs (DHCR7 and CYP2R1 genes) associated with the risk of T2D and abnormal diastolic blood pressure (DBP) with ORs of 1.10 (95%CI: 1.02–1.45) for T2D and 1.14 (95%CI: 1.03–1.43) for DBP. Conclusions This one sample Mendelian randomization analysis shows genetic evidence for a causal role of lower 25(OH) D concentrations in promoting of T2D and abnormal DBP in middle-aged and elderly participants from rural China.

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