STHD: probabilistic cell typing of Single spots in whole Transcriptome spatial data with High Definition

Quick start: notebooks/tutorial.ipynb
Generates single-spot (2um) cell type labels and probabilities for VisiumHD data using a machine learning model.
Input: VisiumHD data and reference scRNA-seq dataset with cell type annotation.
Output: cell type labels and probabilities at 2um spot level.
Visualization - STHDviewer: interactive, scalable, and fast spatial plot of spot cell type labels, in a HTML.

Author: Yi Zhang, PhD, yi.zhang@duke.edu
Website: Yi Zhang Lab at Duke
STHDviewer of VisiumHD colon cancer sample with near 9 million spots: STHDviewer_colon_cancer_HD
We provided test data. Download this folder and put as ./testdata/

python version requirement: >=3.8.3
How to use
- create new python venv python3.8 -m venv sthd_env
- activate the venv source sthd_env/bin/activate
- download repo: git clone git@github.com:yi-zhang/STHD.git
- install dependencies: pip install -r STHD/requirements.txt
- making sure ./STHD is in python path, e.g adding via sys.path.append('./STHD')
- then in script: from STHD import {the module you need}
Beta version - pip package coming soon; also finalizing details of comprehensive tutorials!

STHD Quickstart using a colon cancer VisiumHD patch:

10X Genomics colon cancer sample can be downloaded from: https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-human-crc
Required input includes 2um level spatial expression: square_002um , which usually contains filtered_feature_bc_matrix.h5 and spatial/tissue_positions.csv . It is often from the downloaded folder "Binned outputs (all bin levels)". tissue positions in .parquet format can be converted using STHD/hdpp.py.
Required input also includes full-resolution H&E image: Visium_HD_Human_Colon_Cancer_tissue_image.btf. It is often from the "Microscope image".