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:// Users can also download demo datasets from the same website. Results and Discussion: By integrating selected functions from existing statistical and data visualization packages with extensive customization, RBioplot features both statistical analysis and data visualization functionalities. Key properties of RBioplot include: - 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.

Additional Metadata
Keywords Bioinformatics, Biostatistics, Histogram, Joint-point curve, R package
Persistent URL
Journal PeerJ
Zhang, J. (Jing), & Storey, K. (2016). RBioplot: An easy-to-use R pipeline for automated statistical analysis and data visualization in molecular biology and biochemistry. PeerJ, 2016(9). doi:10.7717/peerj.2436