PIVOT: platform for interactive analysis and visualization of transcriptomics data

• Main Authors: Qin Zhu, Stephen A. Fisher, Hannah Dueck, Sarah Middleton, Mugdha Khaladkar, Junhyong Kim
• Format: Article
• Language: English
• Published: BMC 2018-01-01
• Series: BMC Bioinformatics
• Subjects: Transcriptomics; Graphical user interface; Interactive visualization; Exploratory data analysis
• Online Access: http://link.springer.com/article/10.1186/s12859-017-1994-0

Abstract Background Many R packages have been developed for transcriptome analysis but their use often requires familiarity with R and integrating results of different packages requires scripts to wrangle the datatypes. Furthermore, exploratory data analyses often generate multiple derived datasets such as data subsets or data transformations, which can be difficult to track. Results Here we present PIVOT, an R-based platform that wraps open source transcriptome analysis packages with a uniform user interface and graphical data management that allows non-programmers to interactively explore transcriptomics data. PIVOT supports more than 40 popular open source packages for transcriptome analysis and provides an extensive set of tools for statistical data manipulations. A graph-based visual interface is used to represent the links between derived datasets, allowing easy tracking of data versions. PIVOT further supports automatic report generation, publication-quality plots, and program/data state saving, such that all analysis can be saved, shared and reproduced. Conclusions PIVOT will allow researchers with broad background to easily access sophisticated transcriptome analysis tools and interactively explore transcriptome datasets.