Browse
Submit Data
Databases
API
Help

Dataset Information

28 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Smplot: An R Package for Easy and Elegant Data Visualization.

ABSTRACT: R, a programming language, is an attractive tool for data visualization because it is free and open source. However, learning R can be intimidating and cumbersome for many. In this report, we introduce an R package called "smplot" for easy and elegant data visualization. The R package "smplot" generates graphs with defaults that are visually pleasing and informative. Although it requires basic knowledge of R and ggplot2, it significantly simplifies the process of plotting a bar graph, a violin plot, a correlation plot, a slope chart, a Bland-Altman plot and a raincloud plot. The aesthetics of the plots generated from the package are elegant, highly customisable and adhere to important practices of data visualization. The functions from smplot can be used in a modular fashion, thereby allowing the user to further customise the aesthetics. The smplot package is open source under the MIT license and available on Github (https://github.com/smin95/smplot), where updates will be posted. All the example figures in this report are reproducible and the codes and data are provided for the reader in a separate online guide (https://smin95.github.io/dataviz/).

SUBMITTER: Min SH

PROVIDER: S-EPMC8714909 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

EMA - A R package for Easy Microarray data analysis.

Project description:BACKGROUND: The increasing number of methodologies and tools currently available to analyse gene expression microarray data can be confusing for non specialist users. FINDINGS: Based on the experience of biostatisticians of Institut Curie, we propose both a clear analysis strategy and a selection of tools to investigate microarray gene expression data. The most usual and relevant existing R functions were discussed, validated and gathered in an easy-to-use R package (EMA) devoted to gene expression microarray analysis. These functions were improved for ease of use, enhanced visualisation and better interpretation of results. CONCLUSIONS: Strategy and tools proposed in the EMA R package could provide a useful starting point for many microarrays users. EMA is part of Comprehensive R Archive Network and is freely available at http://bioinfo.curie.fr/projects/ema/.

| S-EPMC2987873 | biostudies-literature

ceas: an R package for Seahorse data analysis and visualization.

Project description:SummaryMeasuring cellular energetics is essential to understanding a matrix's (e.g. cell, tissue, or biofluid) metabolic state. The Agilent Seahorse machine is a common method to measure real-time cellular energetics, but existing analysis tools are highly manual or lack functionality. The Cellular Energetics Analysis Software (ceas) R package fills this analytical gap by providing modular and automated Seahorse data analysis and visualization.Availability and implementationceas is available on CRAN (https://cran.r-project.org/package=ceas). Source code and installable tarballs are freely available for download at https://github.com/jamespeapen/ceas/releases/ under the MIT license. Package documentation may be found at https://jamespeapen.github.io/ceas/. ceas is implemented in R and is supported on macOS, Windows and Linux.

| S-EPMC11349193 | biostudies-literature

qPCRtools: An R package for qPCR data processing and visualization.

Project description:In biological research, qPCR is a technique that is frequently used to measure gene expression levels. The calculation of gene amplification efficiency is a critical step in the processing of qPCR data since it helps to decide which method to employ to compute gene expression levels. Here, we introduce the R package qPCRtools, which enables users to analyze the efficiency of gene amplification. Additionally, this software can determine gene expression levels using one of three approaches: the conventional curve-based method, the 2-ΔΔCt method, and the SATQPCR method. The qPCRtools package produces a table with the statistical data of each method as well as a figure with a box or bar plot illustrating the results. The R package qPCRtools is freely available at CRAN (https://CRAN.R-project.org/package=qPCRtools) or GitHub (https://github.com/lixiang117423/qPCRtools/tree/main/CRAN/qPCRtools).

| S-EPMC9513427 | biostudies-literature

cerebroViz: an R package for anatomical visualization of spatiotemporal brain data.

Project description:SummarySpatiotemporal transcriptomic profiling has provided valuable insight into the patterning of gene expression throughout the human brain from early fetal development to adulthood. When combined with prior knowledge of a disease's age at onset and region-specificity, these expression profiles have provided the necessary context to both strengthen putative gene-disease associations and infer new associations. While a wealth of spatiotemporal expression data exists, there are currently no tools available to visualize this data within the anatomical context of the brain, thus limiting the intuitive interpretation of many such findings. We present cerebroViz, an R package to map spatiotemporal brain data to vector graphic diagrams of the human brain. Our tool allows rapid generation of publication-quality figures that highlight spatiotemporal trends in the input data, while striking a balance between usability and customization. cerebroViz is generalizable to any data quantifiable at a brain-regional resolution and currently supports visualization of up to thirty regions of the brain found in databases such as BrainSpan, GTEx and Roadmap Epigenomics.Availability and implementationcerebroViz is freely available through GitHub ( https://github.com/ethanbahl/cerebroViz ). The tutorial is available at http://ethanbahl.github.io/cerebroViz/.Contactsethan-bahl@uiowa.edu or jacob-michaelson@uiowa.edu.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC5870797 | biostudies-literature

bigPint: A Bioconductor visualization package that makes big data pint-sized.

Project description:Interactive data visualization is imperative in the biological sciences. The development of independent layers of interactivity has been in pursuit in the visualization community. We developed bigPint, a data visualization package available on Bioconductor under the GPL-3 license (https://bioconductor.org/packages/release/bioc/html/bigPint.html). Our software introduces new visualization technology that enables independent layers of interactivity using Plotly in R, which aids in the exploration of large biological datasets. The bigPint package presents modernized versions of scatterplot matrices, volcano plots, and litre plots through the implementation of layered interactivity. These graphics have detected normalization issues, differential expression designation problems, and common analysis errors in public RNA-sequencing datasets. Researchers can apply bigPint graphics to their data by following recommended pipelines written in reproducible code in the user manual. In this paper, we explain how we achieved the independent layers of interactivity that are behind bigPint graphics. Pseudocode and source code are provided. Computational scientists can leverage our open-source code to expand upon our layered interactive technology and/or apply it in new ways toward other computational biology tasks.

| S-EPMC7347224 | biostudies-literature

BTW-Bioinformatics Through Windows: an easy-to-install package to analyze marker gene data.

Project description:Recent advances in Next-Generation Sequencing (NGS) make comparative analyses of the composition and diversity of whole microbial communities possible at a far greater depth than ever before. This brings new challenges, such as an increased dependence on computation to process these huge datasets. The demand on system resources usually requires migrating from Windows to Linux-based operating systems and prior familiarity with command-line interfaces. To overcome this barrier, we developed a fully automated and easy-to-install package as well as a complete, easy-to-follow pipeline for microbial metataxonomic analysis operating in the Windows Subsystem for Linux (WSL)-Bioinformatics Through Windows (BTW). BTW combines several open-access tools for processing marker gene data, including 16S rRNA, bringing the user from raw sequencing reads to diversity-related conclusions. It includes data quality filtering, clustering, taxonomic assignment and further statistical analyses, directly in WSL, avoiding the prior need of migrating from Windows to Linux. BTW is expected to boost the use of NGS amplicon data by facilitating rapid access to a set of bioinformatics tools for Windows users. Moreover, several Linux command line tools became more reachable, which will enhance bioinformatics accessibility to a wider range of researchers and practitioners in the life sciences and medicine. BTW is available in GitHub (https://github.com/vpylro/BTW). The package is freely available for noncommercial users.

| S-EPMC6074753 | biostudies-literature

PyCellBase, an efficient python package for easy retrieval of biological data from heterogeneous sources.

Project description:BackgroundBiological databases and repositories are incrementing in diversity and complexity over the years. This rapid expansion of current and new sources of biological knowledge raises serious problems of data accessibility and integration. To handle the growing necessity of unification, CellBase was created as an integrative solution. CellBase provides a centralized NoSQL database containing biological information from different and heterogeneous sources. Access to this information is done through a RESTful web service API, which provides an efficient interface to the data.ResultsIn this work we present PyCellBase, a Python package that provides programmatic access to the rich RESTful web service API offered by CellBase. This package offers a fast and user-friendly access to biological information without the need of installing any local database. In addition, a series of command-line tools are provided to perform common bioinformatic tasks, such as variant annotation. CellBase data is always available by a high-availability cluster and queries have been tuned to ensure a real-time performance.ConclusionPyCellBase is an open-source Python package that provides an efficient access to heterogeneous biological information. It allows to perform tasks that require a comprehensive set of knowledge resources, as for example variant annotation. Queries can be easily fine-tuned to retrieve the desired information of particular biological features. PyCellBase offers the convenience of an object-oriented scripting language and provides the ability to integrate the obtained results into other Python applications and pipelines.

| S-EPMC6438028 | biostudies-literature

Statistical Package for Growth Rates Made Easy.

Project description:Growth rates are an important tool in microbiology because they provide high throughput fitness measurements. The release of GrowthRates, a program that uses the output of plate reader files to automatically calculate growth rates, has facilitated experimental procedures in many areas. However, many sources of variation within replicate growth rate data exist and can decrease data reliability. We have developed a new statistical package, CompareGrowthRates (CGR), to enhance the program GrowthRates and accurately measure variation in growth rate data sets. We define a metric, Variability-score (V-score), that can help determine if variation within a data set might result in false interpretations. CGR also uses the bootstrap method to determine the fraction of bootstrap replicates in which a strain will grow the fastest. We illustrate the usage of CGR with growth rate data sets similar to those in Mira, Meza, et al. (Adaptive landscapes of resistance genes change as antibiotic concentrations change. Mol Biol Evol. 32(10): 2707-2715). These statistical methods are compatible with the analytic methods described in Growth Rates Made Easy and can be used with any set of growth rate output from GrowthRates.

| S-EPMC5850790 | biostudies-literature

Pathview: an R/Bioconductor package for pathway-based data integration and visualization.

Project description:SummaryPathview is a novel tool set for pathway-based data integration and visualization. It maps and renders user data on relevant pathway graphs. Users only need to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps and integrates user data onto the pathway and renders pathway graphs with the mapped data. Although built as a stand-alone program, Pathview may seamlessly integrate with pathway and functional analysis tools for large-scale and fully automated analysis pipelines.AvailabilityThe package is freely available under the GPLv3 license through Bioconductor and R-Forge. It is available at http://bioconductor.org/packages/release/bioc/html/pathview.html and at http://Pathview.r-forge.r-project.org/.Contactluo_weijun@yahoo.comSupplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC3702256 | biostudies-literature

MStractor: R Workflow Package for Enhancing Metabolomics Data Pre-Processing and Visualization.

Project description:Untargeted metabolomics experiments for characterizing complex biological samples, conducted with chromatography/mass spectrometry technology, generate large datasets containing very complex and highly variable information. Many data-processing options are available, however, both commercial and open-source solutions for data processing have limitations, such as vendor platform exclusivity and/or requiring familiarity with diverse programming languages. Data processing of untargeted metabolite data is a particular problem for laboratories that specialize in non-routine mass spectrometry analysis of diverse sample types across humans, animals, plants, fungi, and microorganisms. Here, we present MStractor, an R workflow package developed to streamline and enhance pre-processing of metabolomics mass spectrometry data and visualization. MStractor combines functions for molecular feature extraction with user-friendly dedicated GUIs for chromatographic and mass spectromerty (MS) parameter input, graphical quality-control outputs, and descriptive statistics. MStractor performance was evaluated through a detailed comparison with XCMS Online. The MStractor package is freely available on GitHub at the MetabolomicsSA repository.

| S-EPMC8398219 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data