- Introduction
- Prerequisites
- Installing
- Citing
- Building
- Tutorial
Our work of QuST-Copilot can be found here.
Welcome to the QuST extension for QuPath!
We introduce QuST, a QuPath extension designed to bridge the gap between H&E WSI and ST analyzing tasks. Recently, various technologies have been introduced into digital pathology, including artificial intelligence (AI) driven methods, in both areas of pathological whole slide image (WSI) analysis and spatial transcriptomics (ST) analysis. AI-driven WSI analysis utilizes the power of deep learning (DL), expands the field of view for histopathological image analysis. On the other hand, ST bridges the gap between tissue spatial analysis and biological signals, offering the possibility to understand the spatial biology. However, a major bottleneck in DL-based WSI analysis is the preparation of training patterns, as hematoxylin & eosin (H&E) staining does not provide direct biological evidence, such as gene expression, for determining the category of a biological component. On the other hand, as of now, the resolution in ST is far beyond that of WSI, resulting the challenge of further spatial analysis. Although various WSI analysis tools, including QuPath, have cited the use of WSI analysis tools in the context of ST analysis, its usage is primarily focused on initial image analysis, with other tools being utilized for more detailed transcriptomic analysis. As a result, the information hidden beneath WSI has not yet been fully utilized to support ST analysis.
The current version is written for QuPath v0.5.1
See what's new in the changelog;
QuST workflow includes: (a) users begin by importing ST data into QuPath using QuST. This step may require additional spatial alignment data which can be obtained via FIJI, if the user is working on 10x Xenium dataset (see text). (b) once the ST data is successfully loaded, users can perform analysis and visualization via QuPath and QuST. (c) given the biological evidences provided by ST, users can generate the training set for image based cell classification and region segmentation based on H&E. Finally, the result generated using the DL module can be further analyzed using the functions described in (b).
- QuST includes a user interface for StarDist for convenience. The user who wishes to use StarDist should download and install it separatly.
- For cell-cell interaction, an additional file, human_lr_pair.csv, is required. The latest version of the file can be obtained from CellChatDB. An older version can be found here. Please cite below publications if you are using their works.
- If you are using the QuST deep learning module, Linux environment with GPUs is strongly recommended for the use case.
Better extension support in QuPath v0.5! See readthedocs for details.
To install the QuST extension, download the latest qupath-extension-qust-[version].jar file from releases and drag it onto the main QuPath window.
If you haven't installed any extensions before, you'll be prompted to select a QuPath user directory. The extension will then be copied to a location inside that directory.
You might then need to restart QuPath (but not your computer).
If you use this extension, you should cite the original QuST publication
- Chao-Hui Huang, Sara Lichtarge, Diane Fernandez.
QuST: QuPath Extension for Integrative Whole Slide Image and Spatial Transcriptomics Analysis.
You should also cite the QuPath publication, as described here.
You can build the QuPath QuST extension from source with
cd qupath-extension-qust
gradlew clean buildThe output will be under build/libs.
cleanremoves anything oldbuildbuilds the QuPath extension as a .jar file and adds it tolibs
For a detailed tutorial on how to use QuST, please refer to the Tutorial.