Marmap: A package for importing, plotting and analyzing bathymetric and topographic data in R.
Ontology highlight
ABSTRACT:
SUBMITTER: Pante E
PROVIDER: S-EPMC3760912 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Given the growing interest in path sampling methods for extending the time scales of molecular dynamics (MD) simulations, there has been great interest in software tools that streamline the generation of plots for monitoring the progress of large-scale simulations. Here, we present the WEDAP Python package for simplifying the analysis of data generated from either conventional MD simulations or the weighted ensemble (WE) path sampling method, as implemented in the widely used WESTPA software package. WEDAP facilitates (i) the parsing of WE simulation data stored in highly compressed, hierarchical HDF5 files and (ii) incorporates trajectory weights from WE simulations into all generated plots. Our Python package consists of multiple user-friendly interfaces: a command-line interface, a graphical user interface, and a Python application programming interface. We demonstrate the plotting features of WEDAP through a series of examples using data from WE and conventional MD simulations that focus on the HIV-1 capsid protein's C-terminal domain dimer as a showcase system. The source code for WEDAP is freely available on GitHub at https://github.com/chonglab-pitt/wedap.
| S-EPMC11323263 | biostudies-literature
Project description:OncoPrint, the plot to visualize an overview of genetic variants in sequencing data, has been widely used in the field of cancer genomics. However, still, there have been no Python libraries capable to generate OncoPrint yet, a big hassle to plot OncoPrints within Python-based genetic variants analysis pipelines. This paper introduces a new Python package PyOncoPrint, which can be easily used to plot OncoPrints in Python. The package is based on the existing widely used scientific plotting library Matplotlib, the resulting plots are easy to be adjusted for various needs.
| S-EPMC10085746 | biostudies-literature
Project description:BackgroundIn medical informatics, psychology, market research and many other fields, researchers often need to analyze and model ranking data. However, there is no statistical software that provides tools for the comprehensive analysis of ranking data. Here, we present pmr, an R package for analyzing and modeling ranking data with a bundle of tools. The pmr package enables descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce model, distance-based model, and rank-ordered logit model), and the visualization of ranking data with multidimensional preference analysis.ResultsExamples of the use of package pmr are given using a real ranking dataset from medical informatics, in which 566 Hong Kong physicians ranked the top five incentives (1: competitive pressures; 2: increased savings; 3: government regulation; 4: improved efficiency; 5: improved quality care; 6: patient demand; 7: financial incentives) to the computerization of clinical practice. The mean rank showed that item 4 is the most preferred item and item 3 is the least preferred item, and significance difference was found between physicians' preferences with respect to their monthly income. A multidimensional preference analysis identified two dimensions that explain 42% of the total variance. The first can be interpreted as the overall preference of the seven items (labeled as "internal/external"), and the second dimension can be interpreted as their overall variance of (labeled as "push/pull factors"). Various statistical models were fitted, and the best were found to be weighted distance-based models with Spearman's footrule distance.ConclusionsIn this paper, we presented the R package pmr, the first package for analyzing and modeling ranking data. The package provides insight to users through descriptive statistics of ranking data. Users can also visualize ranking data by applying a thought multidimensional preference analysis. Various probability models for ranking data are also included, allowing users to choose that which is most suitable to their specific situations.
| S-EPMC3665468 | biostudies-literature
Project description:High-throughput analyses of single-cell microscopy data are a critical tool within the field of bacterial cell biology. Several programs have been developed to specifically segment bacterial cells from phase-contrast images. Together with spot and object detection algorithms, these programs offer powerful approaches to quantify observations from microscopy data, ranging from cell-to-cell genealogy to localization and movement of proteins. Most segmentation programs contain specific post-processing and plotting options, but these options vary between programs and possibilities to optimize or alter the outputs are often limited. Therefore, we developed BactMAP (Bacterial toolbox for Microscopy Analysis & Plotting), a command-line based R package that allows researchers to transform cell segmentation and spot detection data generated by different programs into various plots. Furthermore, BactMAP makes it possible to perform custom analyses and change the layout of the output. Because BactMAP works independently of segmentation and detection programs, inputs from different sources can be compared within the same analysis pipeline. BactMAP complies with standard practice in R which enables the use of advanced statistical analysis tools, and its graphic output is compatible with ggplot2, enabling adjustable plot graphics in every operating system. User feedback will be used to create a fully automated Graphical User Interface version of BactMAP in the future. Using BactMAP, we visualize key cell cycle parameters in Bacillus subtilis and Staphylococcus aureus, and demonstrate that the DNA replication forks in Streptococcus pneumoniae dissociate and associate before splitting of the cell, after the Z-ring is formed at the new quarter positions. BactMAP is available from https://veeninglab.com/bactmap.
| S-EPMC7027861 | biostudies-literature
Project description:In many biomechanical analyses, the forces acting on a body during dynamic and static activities are often simplified as point loads. However, it is usually more accurate to characterize these forces as distributed loads, varying in magnitude and direction, over a given contact area. Evaluating these pressure distributions while they are applied to different parts of the body can provide effective insights for clinicians and researchers when studying health and disease conditions, for example when investigating the biomechanical factors that may lead to plantar ulceration in diabetic foot disease. At present, most processing and analysis for pressure data is performed using proprietary software, limiting reproducibility, transparency, and consistency across different studies. This paper describes an open-source software package, 'pressuRe', which is built in the freely available R statistical computing environment and is designed to process, analyze, and visualize pressure data collected on a range of different hardware systems in a standardized manner. We demonstrate the use of the package on pressure dataset from patients with diabetic foot disease, comparing pressure variables between those with longer and shorter durations of the disease. The results matched closely with those from commercially available software, and individuals with longer duration of diabetes were found to have higher forefoot pressures than those with shorter duration. By utilizing R's powerful and openly available tools for statistical analysis and user customization, this package may be a useful tool for researchers and clinicians studying plantar pressures and other pressure sensor array based biomechanical measurements. With regular updates intended, this package allows for continued improvement and we welcome feedback and future contributions to extend its scope. In this article, we detail the package's features and functionality.
| S-EPMC10556014 | biostudies-literature
Project description:Dipolar EPR spectroscopy (DEER and other techniques) enables the structural characterization of macromolecular and biological systems by measurement of distance distributions between unpaired electrons on a nanometer scale. The inference of these distributions from the measured signals is challenging due to the ill-posed nature of the inverse problem. Existing analysis tools are scattered over several applications with specialized graphical user interfaces. This renders comparison, reproducibility, and method development difficult. To remedy this situation, we present DeerLab, an open-source software package for analyzing dipolar EPR data that is modular and implements a wide range of methods. We show that DeerLab can perform one-step analysis based on separable non-linear least squares, fit dipolar multi-pathway models to multi-pulse DEER data, run global analysis with non-parametric distributions, and use a bootstrapping approach to fully quantify the uncertainty in the analysis.
| S-EPMC8462493 | biostudies-literature
Project description: Not available
| S-EPMC10908268 | biostudies-literature
Project description:WEGO (Web Gene Ontology Annotation Plot), created in 2006, is a simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results. Owing largely to the rapid development of high-throughput sequencing and the increasing acceptance of GO, WEGO has benefitted from outstanding performance regarding the number of users and citations in recent years, which motivated us to update to version 2.0. WEGO uses the GO annotation results as input. Based on GO's standardized DAG (Directed Acyclic Graph) structured vocabulary system, the number of genes corresponding to each GO ID is calculated and shown in a graphical format. WEGO 2.0 updates have targeted four aspects, aiming to provide a more efficient and up-to-date approach for comparative genomic analyses. First, the number of input files, previously limited to three, is now unlimited, allowing WEGO to analyze multiple datasets. Also added in this version are the reference datasets of nine model species that can be adopted as baselines in genomic comparative analyses. Furthermore, in the analyzing processes each Chi-square test is carried out for multiple datasets instead of every two samples. At last, WEGO 2.0 provides an additional output graph along with the traditional WEGO histogram, displaying the sorted P-values of GO terms and indicating their significant differences. At the same time, WEGO 2.0 features an entirely new user interface. WEGO is available for free at http://wego.genomics.org.cn.
| S-EPMC6030983 | biostudies-literature
Project description:BackgroundRecent deep sequencing technologies have proven to be valuable resources to gain insights into the expression profiles of diverse tRNAs. However, despite these technologies, the association of tRNAs with diverse diseases has not been explored in depth because analytical tools are lacking.ResultsWe developed a user-friendly tool, tRNA Expression Analysis Software Utilizing R for Easy use (tReasure), to analyze differentially expressed tRNAs (DEtRNAs) from deep sequencing data of small RNAs using R packages. tReasure can quantify individual mature tRNAs, isodecoders, and isoacceptors. By adopting stringent mapping strategies, tReasure supports the precise measurement of mature tRNA read counts. The whole analysis workflow for determining DEtRNAs (uploading FASTQ files, removing adapter sequences and poor-quality reads, mapping and quantifying tRNAs, filtering out low count tRNAs, determining DEtRNAs, and visualizing statistical analysis) can be performed with the tReasure package.ConclusionstReasure is an open-source software available for download at https://treasure.pmrc.re.kr and will be indispensable for users who have little experience with command-line software to explore the biological implication of tRNA expression.
| S-EPMC9063265 | biostudies-literature
Project description:Social scientists have frequently attempted to assess the relative contribution of age, period, and cohort variables to the overall trend in an outcome. We develop an R package APCI (and Stata command apci) to implement the age-period-cohort-interaction (APC-I) model for estimating and testing age, period, and cohort patterns in various types of outcomes for pooled cross-sectional data and multi-cohort panel data. Package APCI also provides a set of functions for visualizing the data and modeling results. We demonstrate the usage of package APCI with empirical data from the Current Population Survey. We show that package APCI provides useful visualization and analytical tools for understanding age, period, and cohort trends in various types of outcomes.
| S-EPMC10237519 | biostudies-literature