hzar: hybrid zone analysis using an R software package

2013 ◽  
Vol 14 (3) ◽  
pp. 652-663 ◽  
Author(s):  
Elizabeth P. Derryberry ◽  
Graham E. Derryberry ◽  
James M. Maley ◽  
Robb T. Brumfield
Keyword(s):  
Hybrid Zone ◽  
Software Package ◽  
R Software

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.

Utilizing R Software Package for Dose-Response Studies: The Concept and Data Analysis

2007 ◽  
Vol 21 (3) ◽  
pp. 840-848 ◽  
Author(s):  
Stevan Z. Knezevic ◽  
Jens C. Streibig ◽  
Christian Ritz
Keyword(s):  
Data Analysis ◽  
Dose Response ◽  
Software Package ◽  
R Software

scholarly journals Perturbation experiments to investigate the impact of ocean acidification: approaches and software tools

2009 ◽  
Vol 6 (2) ◽  
pp. 4413-4439 ◽  
Author(s):  
J.-P. Gattuso ◽  
H. Lavigne
Keyword(s):  
Ocean Acidification ◽  
Software Package ◽  
Biological Response ◽  
Experimental Manipulation ◽  
Carbonate Chemistry ◽  
R Software ◽  
Seawater Chemistry ◽  
Seawater Carbonate Chemistry ◽  
Gas Bubbling ◽  
The Impact

Abstract. Although future changes in the seawater carbonate chemistry are well constrained, their impact on marine organisms and ecosystems remains poorly known. The biological response to ocean acidification is a recent field of research as most purposeful experiments have only been carried out in the late 1990s. The potentially dire consequences of ocean acidification attract scientists and students with a limited knowledge of the carbonate chemistry and its experimental manipulation. Hence, some guidelines on carbonate chemistry manipulations may be helpful for the growing ocean acidification community to maintain comparability. Perturbation experiments are one of the key approaches used to investigate the biological response to elevated pCO2. They are based on measurements of physiological or metabolic processes in organisms and communities exposed to seawater with normal or altered carbonate chemistry. Seawater chemistry can be manipulated in different ways depending on the facilities available and on the question being addressed. The goal of this paper is (1) to examine the benefits and drawbacks of various manipulation techniques and (2) to describe a new version of the R software package seacarb which includes new functions aimed at assisting the design of ocean acidification perturbation experiments. Three approaches closely mimic the on-going and future changes in the seawater carbonate chemistry: gas bubbling, addition of high-CO2 seawater as well as combined additions of acid and bicarbonate and/or carbonate.

scholarly journals SeedCalc, a new automated R software tool for germination and seedling length data processing

2019 ◽  
Vol 41 (2) ◽  
pp. 250-257 ◽  
Author(s):  
Laércio Junio da Silva ◽  
André Dantas de Medeiros ◽  
Ariadne Morbeck Santos Oliveira
Keyword(s):  
Statistical Analysis ◽  
Data Processing ◽  
Software Package ◽  
Seed Quality ◽  
Software Tool ◽  
R Software ◽  
Seedling Length ◽  
Efficient Data ◽  
Length Data ◽  
Computational Resources

Abstract: The need to optimize seed quality assessment using new, more accessible, and modern computational resources has led to the emergence of new tools. In this paper, we introduce SeedCalc, a new R software package developed to process germination and seedling length data. The functions included in SeedCalc allow fast and efficient data processing, offering greater reliability to the variables generated and facilitating statistical analysis itself since the data are already processed with the appropriate structure to be statistically analyzed in the R software. SeedCalc is available free of charge at https://CRAN.R-project.org/package=SeedCalc.

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 Malaria parasite clearance rate regression: an R software package for a Bayesian hierarchical regression model

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Saeed Sharifi-Malvajerdi ◽  
Feiyu Zhu ◽  
Colin B. Fogarty ◽  
Michael P. Fay ◽  
Rick M. Fairhurst ◽  
...  
Keyword(s):  
Regression Model ◽  
Clearance Rate ◽  
Software Package ◽  
Malaria Parasite ◽  
Parasite Clearance ◽  
Hierarchical Regression ◽  
R Software ◽  
Bayesian Hierarchical ◽  
Hierarchical Regression Model

scholarly journals Classification of Lung Adenocarcinoma Based on Immune Checkpoint and Screening of Related Genes

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ting Zhou ◽  
Ping Yang ◽  
Sanyuan Tang ◽  
Zhongshan Zhu ◽  
Xiaobing Li ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Signaling Pathway ◽  
Risk Score ◽  
Immune Cells ◽  
Immune Checkpoint ◽  
Software Package ◽  
Immune Escape ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
R Software

Aims. Lung adenocarcinoma (LUAD) cells could escape from the monitoring of immune cells and metastasize rapidly through immune escape. Therefore, we aimed to develop a method to predict the prognosis of LUAD patients based on immune checkpoints and their associated genes, thus providing guidance for LUAD treatment. Methods. Gene sequencing data were downloaded from the Cancer Genome Atlas (TCGA) and analyzed by R software and R Bioconductor software package. Based on immune checkpoint genes, kmdist clustering in ConsensusClusterPlus R software package was utilized to classify LUAD. CIBERSORT was used to quantify the abundance of immune cells in LUAD samples. LM22 signature was performed to distinguish 22 phenotypes of human infiltrating immune cells. Gene set variation analysis (GSVA) was performed on immune checkpoint cluster and immune checkpoint score using GSVA R software package. The risk score was calculated by LASSO regression coefficient. Gene Ontology (GO), Hallmark, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed. PROC was performed to generate the ROC curve and calculate the area under the curve (AUC). Results. According to the immune checkpoint, LUAD was classified into clusters 1 and 2. Survival rate, immune infiltration patterns, TMB, and immune score were significantly different between the two clusters. Functional prediction showed that the functions of cluster 1 focused on apoptosis, JAK/STAT signaling pathway, TNF-α/NFκB signaling pathway, and STAT5 signaling pathway. The risk score model was constructed based on nine genes associated with immune checkpoints. Survival analysis and ROC analysis showed that patients with high-risk score had poor prognosis. The risk score was significantly correlated with cancer status (with tumor), male proportion, status, tobacco intake, and cancer stage. With the increase of the risk score, the enrichment of 22 biological functions increased, such as p53 signaling pathway. The signature was verified in IMvigor immunotherapy dataset with excellent diagnostic accuracy. Conclusion. We established a nine-gene signature based on immune checkpoints, which may contribute to the diagnosis, prognosis, and clinical treatment of LUAD.

scholarly journals Disease mechanism, drug-target and biomarker prediction software: Application on prostate cancer and validation

2017 ◽  
Author(s):  
Gokmen Altay ◽  
Elmar Nurmemmedov ◽  
Santosh Kesari ◽  
David E. Neal
Keyword(s):  
Prostate Cancer ◽  
Software Package ◽  
Drug Targets ◽  
Target Prediction ◽  
Potential Drug ◽  
Disease Mechanism ◽  
R Software ◽  
Cancer Dataset ◽  
Link Type ◽  
Potential Drug Targets

AbstractWe present an R software package that performs at genome-wide level differential network analysis and infers only disease-specific molecular interactions between two different cell conditions. This helps revealing the disease mechanism and predicting most influential genes as potential drug targets or biomarkers of the disease condition of interest. As an exemplary analysis, we performed an application of the software over LNCaP datasets and, out of approximately 25000 genes, predicted CXCR7 and CXCR4 together as drug targets of LNCaP prostate cancer dataset. We further successfully validated them with our initial wet-lab experiments. The introduced software can be applied to all the diseases, especially cancer, with gene expression data of two different conditions (e.g. tumor vs normal) and thus has the potential of a global benefit. As a distinct remark, our software provide the causal disease mechanism with multiple potential drug-targets rather than a single independent target prediction.AvailabilityThe introduced R software package for the analysis is available in CRAN at https://cran.r-project.org/web/packages/dc3net and also at https://github.com/altayg/dc3net

Unlocking the potential of eddy covariance data with the R software package openeddy

2020 ◽  
Author(s):  
Ladislav Šigut ◽  
Pavel Sedlák ◽  
Milan Fischer ◽  
Georg Jocher ◽  
Thomas Wutzler ◽  
...  
Keyword(s):  
Quality Control ◽  
Eddy Covariance ◽  
Data Aggregation ◽  
Software Package ◽  
Input Data ◽  
Ease Of Use ◽  
Post Processing ◽  
R Software ◽  
Software Packages ◽  
Processing Steps

<p><span lang="EN-US">The eddy covariance method provides important insights about CO<sub>2</sub>, water and energy exchange-related processes on the ecosystem scale level. Data are collected quasi-continuously with sampling frequency 10 Hz at minimum, often throughout multiple years, producing large datasets. Standard data processing methods are already devised but undergo continuous refinements that should be reflected in the available software. Currently, a suite of software packages is available for computation of half-hourly products from high frequency raw data. However, software packages consolidating the further post-processing computations are not yet that common. The post-processing steps can consist of quality control, footprint modelling, computation of storage fluxes, gap-filling, flux partitioning and data aggregation. Also they can be realized in different programming languages and require various input data formats. Users would therefore often evaluate only certain aspects of the dataset which limits the amount of extractable information from obtained data and they possibly omit the features that could affect data quality or interpretation. Here we present the free R software package openeddy (<a href="https://github.com/lsigut/openeddy">https://github.com/lsigut/openeddy</a>) that provides utilities for input data handling, extended quality control checks, data aggregation and visualization and that includes a workflow (<a href="https://github.com/lsigut/EC_workflow">https://github.com/lsigut/EC_workflow</a>) that attempts to integrate all post-processing steps through incorporation of other free software packages, such as REddyProc (<a href="https://github.com/bgctw/REddyProc/">https://github.com/bgctw/REddyProc/</a>). The framework is designed for the standard set of eddy covariance fluxes, i.e. of momentum, latent and sensible heat as well as CO<sub>2</sub>. Special attention was paid to the visualization of results at different stages of processing and at different time resolutions and aggregation steps. This allows to quickly check that computations were performed as expected and it also helps to notice issues in the dataset itself. Finally, the proposed folder structure with defined post-processing steps allows to organize data in different stages of processing for improved ease of use. Produced workflow files document the whole processing chain and its possible adaptations for a given site. We believe that such a tool can be particularly useful for eddy-covariance novices, groups that cannot or do not contribute their data to regional networks for further processing or users that want to evaluate their data independently. This or similar efforts can also help to save human resources or speed up the development of new methods.</span></p> <p><span lang="EN-US">This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the CzeCOS program, grant number LM2015061, and within Mobility CzechGlobe 2, grant number CZ.02.2.69/0.0/0.0/18_053/0016924.</span></p>

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