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Preparations

STANDS is a powerful documentation framework to detect, align and subtyping anomalous tissue domains across multiple samples. In the subsequent sections, we will introduce the preparatory tasks before utilizing STANDS for your research, including the installation of Python packages, downloading of datasets, and other related procedures.

Installation

STANDS is developed as a Python package. You will need to install Python, and the recommended version is Python 3.9.5.

You can download the package from GitHub and install it locally:

git clone https://github.com/Catchxu/STANDS.git
cd STANDS/
python3 setup.py install

Datasets

All experimental datasets involved in this paper are available from their respective original sources: the 10x-Visium datasets of healthy human breast tissues (10x-hNB datasets) are available at CELLxGENE; The 10x-Visium datasets of human breast cancer tissues (10x-hBC datasets) are available at github; The 10x-Visium datasets of human primary sclerosing cholangitis tissue (10x-hPSC datasets) and human liver caudate lobe tissue are available at CELLxGENE; The 10x-Visium datasets of human renal cell cancer tissue (10x-hRCC datasets) are available at Gene Expression Omnibus; The scRNA-seq dataset of human pancreatic ductal (sc-hPD) and 10x-Visium datasets of the human pancreatic ductal adenocarcinomas (10x-hPDAC) are available at Gene Expression Omnibus; The slide-seqV2 datasets of mouse embryo tissues (ssq-mEmb datasets) are available at CELLxGENE; The Stereo-seq datasets of mouse embryo tissue (Stereo-mEmb datasets) are available at STOmicsDB. We also provide organized and processed small datasets to demo the our code and tutorials. You can download the demo datasets from Google Drive.

Getting help

Please see the tutorial for more complete documentation of all the functions of STANDS. For any questions or comments, please use the GitHub issues or directly contact Kaichen Xu at the email: kaichenxu358@gmail.com.

Tested environment

Environment 1

  • CPU: Intel(R) Xeon(R) Platinum 8255C CPU @ 2.50GHz
  • Memory: 256 GB
  • System: Ubuntu 20.04.5 LTS
  • Python: 3.9.15

Environment 2

  • CPU: Intel(R) Xeon(R) Gold 6240R CPU @ 2.40GHz
  • Memory: 256 GB
  • System: Ubuntu 22.04.3 LTS
  • Python: 3.9.18

Dependencies

  • anndata>=0.10.7
  • dgl>=2.1.0
  • networkx>=3.2.1
  • numpy>=1.22.4
  • pandas>=1.5.1
  • Pillow>=9.4.0
  • PuLP>=2.7.0
  • pyemd>=1.0.0
  • rpy2>=3.5.13
  • scanpy>=1.10.1
  • scikit_learn>=1.2.0
  • scipy>=1.11.4
  • torch>=2.0.0
  • torchvision>=0.15.1
  • tqdm>=4.64.1