ISEE: Interactive SummarizedExperiment Explorer.

Kevin Rue-Albrecht,Federico Marini,Charlotte Soneson,Aaron T.L Lun

doi:10.12688/f1000research.14966.1

Abstract

Data exploration is critical to the comprehension of large biological data sets generated by high-throughput assays such as sequencing. However, most existing tools for interactive visualisation are limited to specific assays or analyses. Here, we present the iSEE (Interactive SummarizedExperiment Explorer) software package, which provides a general visual interface for exploring data in a SummarizedExperiment object. iSEE is directly compatible with many existing R/Bioconductor packages for analysing high-throughput biological data, and provides useful features such as simultaneous examination of (meta)data and analysis results, dynamic linking between plots and code tracking for reproducibility. We demonstrate the utility and flexibility of iSEE by applying it to explore a range of real transcriptomics and proteomics data sets.

Highlights

Interactive data exploration is critical to the analysis and comprehension of data generated by high-throughput biological assays, such as those commonly used in genomics
Most existing tools for interactive visualisation of biological data are designed for specific assays and analyses, e.g., pRoloc for proteomics (Gatto et al, 2014), shinyMethyl for methylation (Fortin et al, 2014), HTSvis for high-throughput screens (Scheeder et al, 2017)
We present the iSEE software package for interactive data exploration. iSEE is implemented in R using the Shiny framework (Chang et al, 2017) and exploits data structures from the open-source Bioconductor project (Gentleman et al, 2004), the SummarizedExperiment class. iSEE allows users to simultaneously visualise multiple aspects of a given data set, including experimental data, metadata and analysis results

Summary

Introduction

Interactive data exploration is critical to the analysis and comprehension of data generated by high-throughput biological assays, such as those commonly used in genomics. The code required to reproduce the current state of the interface can be reported This can be used in startup scripts to launch an iSEE instance in any preferred layout, including the panel organisation, variable selection, colouring schemes, links between panels and even individual brushes and lasso selections. Use cases Plate-based single-cell RNA sequencing To demonstrate iSEE’s functionality, we used it to explore a plate-based single-cell RNA sequencing (scRNA-seq) data set involving 379 cells from the mouse visual cortex (Tasic et al, 2016) This demonstration guides the user through the main features of the iSEE interface including the multi-panel layout, colouring and dynamic linking. The interface is flexible and can be dynam-i cally customised by the user; supports exploration of interactions between data aspects through colouring and linking between panels; and provides transparency and reproducibility during the interactive analysis, through code tracking and state reporting. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Is the rationale for developing the new software tool clearly explained?

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