yi-zhang/STHD
STHD: probabilistic cell typing of Single spots in whole Transcriptome spatial data with High Definition
This tool helps scientists precisely identify cell types in high-resolution spatial transcriptomics data, like VisiumHD. It takes your VisiumHD gene expression data and a single-cell RNA sequencing (scRNA-seq) reference dataset, then outputs detailed cell type labels and their probabilities for each 2-micrometer spot. This is ideal for biologists and pathologists studying tissue architecture and cellular composition at a very fine scale.
No commits in the last 6 months. Available on PyPI.
Use this if you need to accurately map specific cell types within complex tissue samples from VisiumHD data to understand disease mechanisms or tissue development.
Not ideal if you are working with lower-resolution spatial transcriptomics data or do not have a well-annotated scRNA-seq reference for your cell types of interest.
Stars
37
Forks
8
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Apr 30, 2025
Commits (30d)
0
Dependencies
11
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/yi-zhang/STHD"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
scverse/scvi-tools
Deep probabilistic analysis of single-cell and spatial omics data
scverse/scanpy
Single-cell analysis in Python. Scales to >100M cells.
Teichlab/celltypist
A tool for semi-automatic cell type classification
theislab/scarches
Reference mapping for single-cell genomics
Lotfollahi-lab/nichecompass
End-to-end analysis of spatial multi-omics data