| Package |
|
| Documentation |
|
| Paper |
|
| License |
|
stLearn - A downstream analysis toolkit for Spatial Transcriptomic data
stLearn is designed to comprehensively analyse Spatial Transcriptomics (ST) data to investigate complex biological processes within an undissociated tissue. ST is emerging as the “next generation” of single-cell RNA sequencing because it adds spatial and morphological context to the transcriptional profile of cells in an intact tissue section. However, existing ST analysis methods typically use the captured spatial and/or morphological data as a visualisation tool rather than as informative features for model development. We have developed an analysis method that exploits all three data types: Spatial distance, tissue Morphology, and gene Expression measurements (SME) from ST data. This combinatorial approach allows us to more accurately model underlying tissue biology, and allows researchers to address key questions in three major research areas: cell type identification, spatial trajectory reconstruction, and the study of cell-cell interactions within an undissociated tissue sample.
Getting Started
Citing stLearn
If you have used stLearn in your research, please consider citing us:
Pham et al., (2020). stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues biorxiv https://doi.org/10.1101/2020.05.31.125658
======= History
0.4.11 (2022-11-25)
0.4.10 (2022-11-22)
0.4.8 (2022-06-15)
0.4.7 (2022-03-28)
0.4.6 (2022-03-09)
0.4.5 (2022-03-02)
0.4.0 (2022-02-03)
0.3.2 (2021-03-29)
0.3.1 (2020-12-24)
0.2.7 (2020-09-12)
0.2.6 (2020-08-04)
2 maintainers
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file stlearn-0.4.11.tar.gz.
File metadata
- Download URL: stlearn-0.4.11.tar.gz
- Upload date:
- Size: 444.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/4.0.1 CPython/3.8.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
59adb90a16a24b70ca11ddceb560d5637727f6c72e7bf8bb0e98b4061ae3fab3
|
|
| MD5 |
e05f1bd2ebe8cca781147e425ddb11f3
|
|
| BLAKE2b-256 |
ce9617ee5132628006bce6ae3549207f91bb35e8d82cf220019e317067cc5691
|
File details
Details for the file stlearn-0.4.11-py3.8.egg.
File metadata
- Download URL: stlearn-0.4.11-py3.8.egg
- Upload date:
- Size: 678.7 kB
- Tags: Egg
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/4.0.2 CPython/3.8.16
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
76af4f1864e689aa4020c2d92d38d95a6653913de7ee246cdd79ced7c1e6bd15
|
|
| MD5 |
c57a46b5c93d5394ef3d6cafd80bd08c
|
|
| BLAKE2b-256 |
f89fa693b5c8086edc073bb9eb47f9e496dc8f1805b71a05708734824bddbbd6
|
File details
Details for the file stlearn-0.4.11-py2.py3-none-any.whl.
File metadata
- Download URL: stlearn-0.4.11-py2.py3-none-any.whl
- Upload date:
- Size: 418.6 kB
- Tags: Python 2, Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/4.0.1 CPython/3.8.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
7acd0071ec7d0c22087c3add2db3a51b1b0937cef37ad56d34a4a354fb8fd6c0
|
|
| MD5 |
8c706f7359de765ae44ab9c3771cf517
|
|
| BLAKE2b-256 |
42b27cc1bb51de24665eae28dbda82b6b875a4da6242275bf04b79b8cc8e437d
|
