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About this workshop

Federico Marini1, Kevin Rue-Albrecht2, Charlotte Soneson3, Aaron Lun4, Najla Abassi5

Source: vignettes/d00_about_this_workshop.Rmd
d00_about_this_workshop.Rmd

Description

The iSEE Bioconductor package (Rue-Albrecht et al. 2018) is a flexible, powerful and extensible application to explore any dataset stored in a SummarizedExperiment object, including single cell and spatially resolved data, but also bulk data (such as transcriptomics and proteomics) and many types of non-biological data.

iSEE enables a multitude of panels to gain in-depth insight in your data; it does so combining interactivity and reproducibility, and provides an ideal companion to other tools for deeper dives into data.

This workshop provides an overview of the functionality of the iSEE package. The material is equally suitable for use in a “masterclass-style” workshop as for self-study.

Pre-requisites

Most of the examples in this workshop will center around bulk and single-cell RNA sequencing data. For this reason, a certain background knowledge about these types of data and the questions that are typically addressed with them can be helpful to get the most out of the content. More precisely, the following pre-requisities are useful:

  • Familiarity with bulk or single-cell sequencing data (ideally scRNA-seq).
  • Familiarity with the SummarizedExperiment/SingleCellExperiment class, or alternatively the Seurat or AnnData frameworks - basically, any of the widely used ecosystem for working with/storing/processing single-cell data.
  • Familiarity with single-cell data visualizations and representations.

Participation

Participants will be encouraged to follow along in the live coding parts of the workshop - this is very much intended as a hands-on learning resource.

R / Bioconductor packages used

Outline of the workshop

The following material is available in the vignettes folder:

Workshop goals and objectives

Learning goals

  • Outline the features of iSEE, built upon the R/Shiny framework.
  • Utilize interactive GUI components and layouts to efficiently extract information from biological data sets.
  • Describe how to construct interactive apps and custom panels.
  • Demonstrate how iSEE and related packages can smoothly be integrated into and enhance existing workflows for the datasets at hand.

Learning objectives

  • Set up a local environment for running iSEE apps.
  • Interact with components of the iSEE user interface to visually inspect and discuss various datasets.
  • Identify and locate configurable aspects of iSEE apps.
  • Practice interactive visualization over a single-cell RNA-sequencing workflow.
  • Imagine use cases and future developments for interactive visualization as part of computational workflows.
  • Design and create iSEE instances for collections of datasets.

Additional resources

A lot of materials have been developed, from the first use of iSEE to contributions of new panel classes in separate downstream packages.

References

Rue-Albrecht, Kevin, Federico Marini, Charlotte Soneson, and Aaron T L Lun. 2018. “iSEE: Interactive SummarizedExperiment Explorer.” F1000Res. 7: 741.