German Conference on Bioinformatics (GCB) 2020

14 - 17 September 2020,
Virtual Conference

GCB 2020-Logo

WS5: iSEE: Interactive visualization of SummarizedExperiment objects


Federico Marini, University of Mainz
Charlotte Soneson, Friedrich Miescher Institute for Biomedical Research (FMI), Basel


Data exploration is crucial to the comprehension of large biological datasets, generated by high-throughput assays such as high throughput sequencing, with interactivity as a key functionality to generate insightful outputs. Most existing tools for intuitive and interactive visualization are limited to specific assays or analyses, and lack support for reproducible analysis.

Sparked from a Bioconductor community-driven effort, we have built a general-purpose tool, the iSEE package (“interactive SummarizedExperiment Explorer” -,

iSEE is designed for the interactive exploration of any experimental data which can be stored in a SummarizedExperiment object. iSEE is an R package implemented using the Shiny framework, and is fully compatible with many existing R/Bioconductor packages for high-throughput biological data.

This workshop demonstrates the use of the iSEE package to create and configure interactive applications for the exploration of various types of genomics data sets (e.g., bulk and single-cell RNA-seq, CyTOF, gene expression microarray).

This workshop will be presented as a lab session that combines an instructor-led live demo, followed by hands-on experimentation guided by completely worked examples and stand-alone notes that participants may continue to use after the workshop.

The instructor-led live demo comprises three parts:

  1. Brief lecture on the package concept and functionality
  2. Overview of the graphical user interface
  3. Instructions to preconfigure iSEE apps

The hands-on lab comprises three part:

  1. Inspection of single-cell RNA-seq data at various steps of a typical computational workflow, including quality control and dimensionality reduction
  2. Addition of custom panels to the user interface for advanced visualization.
  3. Additional questions from the participants, including individual use cases and suggestions for future developments

Participants are encouraged to ask questions at any time during the workshop.


  • Basic knowledge of R syntax and the use of data-frames
  • Familiarity with the SummarizedExperiment and SingleCellExperiment classes
  • Familiarity with the shiny CRAN package
  • Familiarity with the scRNAseq package and vignette

Additional background reading about the programming environment, relevant packages, and example use cases:

  • Shiny from RStudio:
  • SummarizedExperiment paper: (Figure 2)
  • iSEE manuscript:
  • “Orchestrating single-cell analysis with Bioconductor”, 0654-x, Nat Methods 17, 137–145 (2020), as an online companion to the manuscript above
  • for extending the functionality of iSEE
  •, a gallery-repository to store the commands to reproduce entire analyses for iSEE instances on public/own data

Workshop Participation:

Students will participate by following along an R markdown document, and asking questions throughout the workshop.
There is also scope for participants to apply iSEE to their own data sets, and fuel the discussion with more questions about specific use cases.

Workshop goals and objectives

Learning goals

  • Recognize the benefits of integrative data containers such as SummarizedExperiment and SingleCellExperiment for downstream analyses and visualization
  • Outline the unique features of iSEE built upon the RStudio Shiny framework
  • Identify biological data that may be combined into insightful graphical outputs
  • Utilize interactive GUI components and layouts to efficiently extract information from biological data sets
  • Describe how to construct interactive apps and custom panels

Learning objectives

  • Memorize the key information available in SummarizedExperiment and SingleCellExperiment objects
  • Set up a local environment for running iSEE apps
  • Interact with components of the iSEE user interface to visually inspect and discuss various data sets
  • Identify and locate configurable aspects of iSEE apps
  • Practice interactive visualization over a single-cell RNA-sequencing workflow
  • Design custom iSEE panels for advanced use cases
  • Imagine use cases and future developments for interactive visualization as part of computational workflows


Supported by