PIVOT: platform for interactive analysis and visualization of transcriptomics data

Abstract

BackgroundMany 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.ResultsHere 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.ConclusionsPIVOT will allow researchers with broad background to easily access sophisticated transcriptome analysis tools and interactively explore transcriptome datasets.Electronic supplementary materialThe online version of this article (10.1186/s12859-017-1994-0) contains supplementary material, which is available to authorized users.

MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets

“…Most are essentially interfaces to R packages (18-20, 129), providing a "shiny" (17) web-app inter- face to a limited number of R packages for data normalization, differential expression analysis, PCA analysis (among samples) and gene enrichment analysis. Because they are written in R (18)(19)(20), these tools must rely on R's somewhat limited capabilities for interactive applications. In contrast to R, Java, MOG's platform, has been used to develop numerous software with interfaces that are interactive and userfriendly (102,(130)(131)(132)(133), and MOG provides the researcher with specialized GUIs and methods for exploratory data analysis.…”

“…Such tools are based upon rigorous statistical frameworks and produce accurate results when the model assumptions hold. Some tools avoid the need to code by providing "shiny" interfaces (17) to various subsets of R's functionalities (10,(18)(19)(20). They can only apply selected R packages to the data, and have the general limitations that they are not well suited for very large datasets, and have very limited interactivity.…”

“…MOG performance benchmarks. MOG was benchmarked using the entire the cancer dataset(18,212 genes over 7142 samples) and using smaller-sized datasets derived by partitioning the full cancer dataset into tumor and non-tumor samples from various tissues. (A) Comparison of MOG to R-based (PIVOT).…”

“…(A) Comparison of MOG to R-based (PIVOT). Dataset size was limited to the amount of data that could be loaded in PIVOT i.e., liver tumor and non-tumor samples(18,212 genes over 410 samples). The % processor time (% CPU utilization) for PIVOT and MOG was calculated over 30 minutes.…”

ideal: an R/Bioconductor package for Interactive Differential Expression Analysis

“…A growing number of software packages (Younesy et al, 2015;Nelson et al, 2016;Su et al, 2017;Harshbarger et al, 2017;Gardeux et al, 2017;Lim et al, 2017;Li and Andrade, 2017;Zhu et al, 2018;Ge et al, 2018;Monier et al, 2018;McDermaid et al, 2018;Schultheis et al, 2018;Kucukural et al, 2019;Choi and Ratner, 2019;Price et al, 2019;Tintori et al, 2020;Su et al, 2019) have been developed to operate on tabular-like summarized expression data, or on formats which might derive from their results (see Additional file 1: Table S1 for a comprehensive list of details on their functionality and characteristics).…”