HiVis 1.0.3

A user friendly tool for working with VisiumHD data

HiVis is associated with the following manuscript: Subcellular mRNA localization patterns across tissues resolved with spatial transcriptomics

HD Integrated Visium Interactive Suite (HiVis)

• HiVis is a user-friendly Python tool for analyzing
  10X VisiumHD data,
  supporting both H&E and immunofluorescence experiments.
• HiVis is built on top of AnnData, integrating seamlessly with other spatial transcriptomics tools such as Scanpy and Squidpy.
• HiVis provides extensive data visualization with fully customizable plots. Plots are created using matplotlib, allowing stacking and further customization.
• HiVis works with QuPath to enable easy manual annotation, pixel classification, and single-cell segmentation using Stardist,
  Cellpose and Instaseg. It links bins and single-cell objects, facilitating seamless information exchange across levels.

Getting started

Installation

Installation video tutorial.

To avoid dependency conflicts, we recommend the use of a dedicated conda environment. In a terminal run the command:

conda create -n HiVis python=3.12
conda activate HiVis

We recommend two options to then install HiVis in your virtual environment.

Use the package manager pip to install HiVis. Takes up to few minutes. In a terminal run the command:

pip install HiVis

Or clone the project's Github repository and install it manually with the following commands:

git clone [e-mail]:roynov01/HiVis.git
cd HiVis
pip install .

To use Qupath features, such as manual annotations and pixel classifiers, download and install QuPath (>= 0.5.1).

To perform cell segmentation in QuPath, download and install Stardist and/or Cellpose extensions for QuPath.

Usage and Documentation

For video tutorial - please refer to the tutorial playlist.

Please refer to the demo notebooks.

For QuPath, please refer to the tutorial.

Function docstrings are available on ReadTheDocs.

Code used for data analysis in the paper is available at the repo.

Contact

Bug report/feature request via the GitHub issue tracker.

Citation

Novoselsky R*, Golani O*, Barkai T, Kedmi M, Goliand I, Fine M, Kent I, Nachmany I, Itzkovitz S. Subcellular mRNA localization patterns across tissues resolved with spatial transcriptomics. Nature Communications, 2026.