bioRxivBioinformaticsPreprint
starmapVR: immersive visualisation of single cell spatial omic data
Andrian YangiD, Yao YuiD, Xiunan Fang, Jianfu LiiD, Yongyan XiaiD, Crystal S.M. Kwok, Michelle C. K. LoiD, Dickson M. D. SiuiD, Kevin K. TsiaiD, Joshua W. K. HoiD
- Citations
- 3
Abstract
MotivationAdvances in high throughput single-cell and spatial omic technologies have enabled the profiling of molecular expression and phenotypic properties of hundreds of thousands of individual cells in the context of their two dimensional (2D) or three dimensional (3D) spatial endogenous arrangement. However, current visualisation techniques do not allow for effective display and exploration of the single cell data in their spatial context. With the widespread availability of low-cost virtual reality (VR) gadgets, such as Google Cardboard, we propose that an immersive visualisation strategy is useful. ResultsWe present starmapVR, a light-weight, cross-platform, web-based tool for visualising single-cell and spatial omic data. starmapVR supports a number of interaction methods, such as keyboard, mouse, wireless controller and voice control. The tool visualises single cells in a 3D space and each cell can be represented by a star plot (for molecular expression, phenotypic properties) or image (for single cell imaging). For spatial transcriptomic data, the 2D single cell expression data can be visualised alongside the histological image in a 2.5D format. The application of starmapVR is demonstrated through a series of case studies. Its scalability has been carefully evaluated across different platforms. Availability and implementationstarmapVR is freely accessible at https://holab-hku.github.io/starmapVR, with the corresponding source code available at https://github.com/holab-hku/starmapVR under the open source MIT license. Supplementary InformationSupplementary data are available at Bioinformatics online.
- assay: spatial transcriptomics
- assay: scRNA-seq
- research focus: dimensionality reduction
- research focus: database / resource
- research focus: software / tool
- research focus: imputation
- approach: experimental assay
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