MetaVision3D

©Nov 31, 2023 University of Florida Research Foundation, Inc. All Rights Reserved.

Shield:

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

We introduce MetaVision3D, a novel pipeline driven by computer vision techniques for the transformation of serial 2D MALDI mass spectrometry imaging sections into a high-resolution 3D spatial metabolome. Our framework employs advanced algorithms for image registration, normalization, and interpolation to enable the integration of serial 2D tissue sections, thereby generating a comprehensive 3D model of unique diverse metabolites across host tissues at mesoscale.

Source:

Paper: MetaVision3D: Automated Framework for the Generation of Spatial Metabolome Atlas in 3D
Code:
Datasets: - Processed .CSV data: - Raw .imzML data: https://sunlabresources.rc.ufl.edu/

Implement

MetaVision3D can be run through two different ways:

1. Using Docker (Recommended):

We have pre-configured the environment for you using Docker, which ensures a consistent and reliable environment and make it easy to get started.

Steps:

• Clone MetaVision3D from Github Repository: bash git clone https://github.com/XinBiostats/MetaVision3D
• Download dataset from Zenodo and put it in "./MetaVision3D/data/".
• Download Docker desktop from Docker website, and install it on your machine.
• Open Docker Tesktop first, then open the Terminal or PowerShell(Windows), run below command with your MetaVision3D path: ```bash docker run -it --rm --user root -e GRANTSUDO=yes -p 8888:8888 -v "YOURMetaVision3D_PATH:/home/jovyan/work" xinbiostats/metavision3d:latest

example: docker run -it --rm --user root -e GRANT_SUDO=yes -p 8888:8888 -v "/Users/xin.ma/Desktop/MetaVision3D:/home/jovyan/work" xinbiostats/metavision3d:latest ```

• Find the highlighted link in your terminal and copy it to your browser. The link will not be the exactly same, but will show up at same place.
• All set! You can play with our Demo now. (demo.ipynb)

2. Using Conda:

Create your own environment for MetaVision3D.(Due to potential incompatibility issues caused by different operating systems and versions, it is recommended to use Docker.）

Steps:

• Clone MetaVision3D from Github Repository: bash git clone https://github.com/XinBiostats/MetaVision3D

• Download dataset from Zenodo and put it in "./MetaVision3D/data/".

• Open the Terminal or PowerShell(Windows), then install requirements: bash conda env create -f environment.yml

• Activate SAMI environment, find your R installation's home directory. bash conda activate metavision3d

• We created a demo (demo.ipynb) to demonstrate how to use MetaVision3D. The results will be displayed inline or saved by users.