CellexalVR: A virtual reality platform to visualize and analyze single-cell omics data

Oscar Legetth,Johan Rodhe,Stefan Lang,Parashar Dhapola,Mattias Wallergård,Shamit Soneji

doi:10.1016/j.isci.2021.103251

Abstract

SummarySingle-cell RNAseq is a routinely used method to explore heterogeneity within cell populations. Data from these experiments are often visualized using dimension reduction methods such as UMAP and tSNE, where each cell is projected in two or three dimensional space. Three-dimensional projections can be more informative for larger and complex datasets because they are less prone to merging and flattening similar cell-types/clusters together. However, visualizing and cross-comparing 3D projections using current software on conventional flat-screen displays is far from optimal as they are still essentially 2D, and lack meaningful interaction between the user and the data. Here we present CellexalVR (www.cellexalvr.med.lu.se), a feature-rich, fully interactive virtual reality environment for the visualization and analysis of single-cell experiments that allows researchers to intuitively and collaboratively gain an understanding of their data.

Highlights

The analysis of single-cell RNAseq data is often performed using scripting, primarily using packages for the R/Python languages such as monocle (Trapnell et al, 2014), Seurat (Stuart et al, 2019) and SCANPY (Wolf et al, 2018) among others
Here we present CellexalVR, a free and open-source virtual reality platform for the visualization and analysis of single-cell data
Single-cell data is processed using any method preferred by the user (Seurat/Scanpy for example) after which the resulting object is converted to a set of CellexalVR input files using our accompanying R package called cellexalvrR (Figure 1A)

Summary

Introduction

The analysis of single-cell RNAseq data (scRNAseq) is often performed using scripting, primarily using packages for the R/Python languages such as monocle (Trapnell et al, 2014), Seurat (Stuart et al, 2019) and SCANPY (Wolf et al, 2018) among others. A common step in the process is dimension reduction (DR) where cells are positioned in two/three dimensional space to visualize heterogeneity within the assayed populations. Many tools are available to visualize scRNAseq data (Cakir et al, 2020), and published single-cell datasets are often accompanied by a webtool where 3D DR plots can be explored in a rudimentary fashion (Packer et al, 2019; Nestorowa et al, 2016) highlighting their utility. Only one projection can be loaded in the same window meaning direct comparisons between several 3D reductions cannot be made . Another drawback is these 3D plots are often not interactive, so for example, selecting cells for further analysis is not possible

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Discussion

Conclusion