Here, we summarise available data and source code regarding the publication "Spatially Resolved Transcriptomics Mining in 3D and Virtual Reality Environments with VR-Omics".

Abstract
Spatially resolved transcriptomics (SRT) technologies produce complex, multi-dimensional data sets of gene expression information that can be obtained at subcellular spatial resolution. While several computational tools are available to process and analyse SRT data, no platforms facilitate the visualisation and interaction with SRT data in an immersive manner. Here we present VR-Omics, a computational platform that supports the analysis, visualisation, exploration, and interpretation SRT data compatible with any SRT technology. VR-Omics is the first tool capable of analysing and visualising data generated by multiple SRT platforms in both 2D desktop and virtual reality environments. It incorporates an in-built workflow to automatically pre-process and spatially mine the data within a user-friendly graphical user interface. Benchmarking VR-Omics against other comparable software demonstrates its seamless end-to-end analysis of SRT data, hence making SRT data processing and mining universally accessible.

VR-Omics is an open-source software freely available at: https://ramialison-lab.github.io/pages/vromics.html or below.

For development of VR-Omics publicly available data was used.

The Visium data from 10XGenomics is available at the 10X Genomics website: https://www.10xgenomics.com/resources/datasets. The 10X Genomics Xenium dataset is available under: https://www.10xgenomics.com/products/xenium-in-situ/preview-dataset-human-breast. The STOmics database is available at: https://db.cngb.org/stomics. The Vizgen MERFISH data release program can be accessed via: https://vizgen.com/data-release-program/. The Tomo-seq data is available via their publication https://doi.org/10.1016/j.cell.2014.09.038 which also contains the MATLAB code for the 3D data reconstruction.

The Visium demo was adapted from Asp et al. and can be accessed via the related publication https://doi.org/10.1016/j.cell.2019.11.025 or at https://data.mendeley.com/datasets/zkzvyprd5z/1.

The demo datasets generated for VR-Omics can be found at: https://doi.org/10.26180/22207579.v1 or below for download.

The 3D Visium data set of the human developing heart adapted from Asp et al. can be found within the application and can be accessed from the main menu following the Visium, Demo context menu.

The complete standalone version of VR-Omics (containing Python AW and Visualiser) can be downloaded at https://ramialison-lab.github.io/pages/vromics.html or at https://doi.org/10.26180/20220312.v1 or below for download.

Alternatively, the code is available at GitHub (https://github.com/Ramialison-Lab/VR-Omics). To use the GitHub version an installation of Unity Gaming Engine (version 2021.3.11f1) is required. This version does not include the Python AW.

The Python AW can be accessed at: https://doi.org/10.26180/22207903.v1. More information of run VR-Omics via Unity can be found in the full documentation accessible at https://ramialison-lab.github.io/pages/vromics.html.