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RBioplot: an easy-to-use R pipeline for automated statistical analysis and data visualization in molecular biology and biochemistry

BACKGROUND: Statistical analysis and data visualization are two crucial aspects in molecular biology and biology. For analyses that compare one dependent variable between standard (e.g., control) and one or multiple independent variables, a comprehensive yet highly streamlined solution is valuable....

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Autores principales: Zhang, Jing, Storey, Kenneth B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5045883/
https://www.ncbi.nlm.nih.gov/pubmed/27703842
http://dx.doi.org/10.7717/peerj.2436
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author Zhang, Jing
Storey, Kenneth B.
author_facet Zhang, Jing
Storey, Kenneth B.
author_sort Zhang, Jing
collection PubMed
description BACKGROUND: Statistical analysis and data visualization are two crucial aspects in molecular biology and biology. For analyses that compare one dependent variable between standard (e.g., control) and one or multiple independent variables, a comprehensive yet highly streamlined solution is valuable. The computer programming language R is a popular platform for researchers to develop tools that are tailored specifically for their research needs. Here we present an R package RBioplot that takes raw input data for automated statistical analysis and plotting, highly compatible with various molecular biology and biochemistry lab techniques, such as, but not limited to, western blotting, PCR, and enzyme activity assays. METHOD: The package is built based on workflows operating on a simple raw data layout, with minimum user input or data manipulation required. The package is distributed through GitHub, which can be easily installed through one single-line R command. A detailed installation guide is available at http://kenstoreylab.com/?page_id=2448. Users can also download demo datasets from the same website. RESULTS AND DISCUSSION: -. Fully automated and comprehensive statistical analysis, including normality test, equal variance test, Student’s t-test and ANOVA (with post-hoc tests); -. Fully automated histogram, heatmap and joint-point curve plotting modules; -. Detailed output files for statistical analysis, data manipulation and high quality graphs; -. Axis range finding and user customizable tick settings; -. High user-customizability.
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spelling pubmed-50458832016-10-04 RBioplot: an easy-to-use R pipeline for automated statistical analysis and data visualization in molecular biology and biochemistry Zhang, Jing Storey, Kenneth B. PeerJ Biochemistry BACKGROUND: Statistical analysis and data visualization are two crucial aspects in molecular biology and biology. For analyses that compare one dependent variable between standard (e.g., control) and one or multiple independent variables, a comprehensive yet highly streamlined solution is valuable. The computer programming language R is a popular platform for researchers to develop tools that are tailored specifically for their research needs. Here we present an R package RBioplot that takes raw input data for automated statistical analysis and plotting, highly compatible with various molecular biology and biochemistry lab techniques, such as, but not limited to, western blotting, PCR, and enzyme activity assays. METHOD: The package is built based on workflows operating on a simple raw data layout, with minimum user input or data manipulation required. The package is distributed through GitHub, which can be easily installed through one single-line R command. A detailed installation guide is available at http://kenstoreylab.com/?page_id=2448. Users can also download demo datasets from the same website. RESULTS AND DISCUSSION: -. Fully automated and comprehensive statistical analysis, including normality test, equal variance test, Student’s t-test and ANOVA (with post-hoc tests); -. Fully automated histogram, heatmap and joint-point curve plotting modules; -. Detailed output files for statistical analysis, data manipulation and high quality graphs; -. Axis range finding and user customizable tick settings; -. High user-customizability. PeerJ Inc. 2016-09-28 /pmc/articles/PMC5045883/ /pubmed/27703842 http://dx.doi.org/10.7717/peerj.2436 Text en © 2016 Zhang and Storey http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Biochemistry
Zhang, Jing
Storey, Kenneth B.
RBioplot: an easy-to-use R pipeline for automated statistical analysis and data visualization in molecular biology and biochemistry
title RBioplot: an easy-to-use R pipeline for automated statistical analysis and data visualization in molecular biology and biochemistry
title_full RBioplot: an easy-to-use R pipeline for automated statistical analysis and data visualization in molecular biology and biochemistry
title_fullStr RBioplot: an easy-to-use R pipeline for automated statistical analysis and data visualization in molecular biology and biochemistry
title_full_unstemmed RBioplot: an easy-to-use R pipeline for automated statistical analysis and data visualization in molecular biology and biochemistry
title_short RBioplot: an easy-to-use R pipeline for automated statistical analysis and data visualization in molecular biology and biochemistry
title_sort rbioplot: an easy-to-use r pipeline for automated statistical analysis and data visualization in molecular biology and biochemistry
topic Biochemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5045883/
https://www.ncbi.nlm.nih.gov/pubmed/27703842
http://dx.doi.org/10.7717/peerj.2436
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