STitch3D
Construction of a 3D whole organism spatial atlas by joint modeling of multiple slices
We developed STitch3D, a deep learning-based method for 3D reconstruction of tissues or whole organisms. Briefly, STitch3D characterizes complex tissue architectures by borrowing information across multiple 2D tissue slices and integrates them with a paired single-cell RNA-sequencing atlas.
With innovations in model designs, STitch3D enables two critical 3D analyses: First, STitch3D detects 3D spatial tissue regions which are related to biological functions, for example cortex layer structures in brain; Second, STitch3D infers 3D spatial distributions of fine-grained cell types in tissues, substantially improving the spatial resolution of seq-based ST approaches. The output of STitch3D can be further used for various downstream tasks like inference of spatial trajectories, denoising of spatial gene expression patterns, identification of genes enriched in specific biologically meaningful regions and detection of cell type gradients in newly generated virtual slices.
Installation
- STitch3D can be downloaded from GitHub:
git clone https://github.com/YangLabHKUST/STitch3D.git
cd STitch3D
conda config --set channel_priority strict
conda env update --f environment.yml
conda activate stitch3d
Normally the installation time will be less than ten minutes. We have tested our package on Linux (Ubuntu 18.04.5 LTS). Software dependencies are listed in this website.
Tutorials and reproducibility
We provided codes for reproducing the experiments of the paper "Construction of a 3D whole organism spatial atlas by joint modeling of multiple slices", and comprehensive tutorials for using STitch3D. Please check the tutorial website for more details.
Interactive 3D results
Interactive 3D analysis results from STitch3D are available at the website.
Reference
Gefei Wang, Jia Zhao, Yan Yan, Yang Wang, Angela Ruohao Wu, Can Yang. Construction of a 3D whole organism spatial atlas by joint modeling of multiple slices. bioRxiv 2023.02.02.526814; doi: https://doi.org/10.1101/2023.02.02.526814
Metadata
Release files for stitch3d 1.0.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| stitch3d-1.0.3.tar.gz | 16.6 kB | Without provenance | Details |
Release files / stitch3d-1.0.3.tar.gz
| Download URL | stitch3d-1.0.3.tar.gz |
|---|---|
| Size | 16.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
1d763bc57bb7aeb4b460ef3f4d5e873c2268c52265a08319dc16786dbc878158
|
|
BLAKE2b-256 checksum How to use checksums |
62e7bc223a0b8c85a2c6e6260e4c04b3c03d2149884ae9b210f1fc4ab355555d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.10.9
|
Security for stitch3d 1.0.3
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.