scmallet
Python wrapper of MALLET for LDA analysis on single-cell data.
MALLET is the LDA backend chosen by pycistopic. The implementation in this package has several difference than pycistopic:
- Improved paralization.
- Allow train LDA model with cell subset and then inference ramaining cells. This is nicely supported by MALLET itself.
- Allow the training process to be resumable.
- Work with anndata.
Installation
You need to have Python 3.9 or newer installed on your system. If you don't have Python installed, we recommend installing Mambaforge.
There are several alternative options to install scmallet:
- Install the latest release of
scmalletfrom PyPI:
pip install scmallet
- Install the latest development version:
pip install git+https://github.com/lhqing/scmallet.git@main
Usage
See example usage here: https://github.com/lhqing/scmallet/blob/main/tests/example.ipynb
Citation
-
Mallet Package: https://mimno.github.io/Mallet/
McCallum, Andrew Kachites. "MALLET: A Machine Learning for Language Toolkit." http://mallet.cs.umass.edu. 2002.
-
PyCistopic: https://github.com/aertslab/pycisTopic
Bravo Gonzalez-Blas, C. & De Winter, S. et al. (2022). SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
Metadata
Release files for scmallet 0.1.2
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 | Actions |
|---|---|---|---|---|
| scmallet-0.1.2.tar.gz | 24.8 kB | Without provenance | Details for scmallet-0.1.2.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| scmallet-0.1.2-py3-none-any.whl | Python 3 | none | any | Details for scmallet-0.1.2-py3-none-any.whl |
Total release size: 44.7 kB
Release files / scmallet-0.1.2.tar.gz
| Download URL | scmallet-0.1.2.tar.gz |
|---|---|
| Size | 24.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
5a7be27e1c542529ff651808edd704ae894e01c4a82de6102cbba28f24700284
|
|
BLAKE2b-256 checksum How to use checksums |
dd73972f1be2a7006cb4df75f5ab4fa8370537118ea85942d8be55196956acf9
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.0.0 CPython/3.12.3
|
Release files / scmallet-0.1.2-py3-none-any.whl
| Download URL | scmallet-0.1.2-py3-none-any.whl |
|---|---|
| Size | 19.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
183feecc31007631d9e6b96c30d92f716ec67fe82e365c91caa591a9393331de
|
|
BLAKE2b-256 checksum How to use checksums |
a0a745b83f94300af1b04784d3f90ec8099a9d7b6a1b6814af2c5c09c5be3ecc
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.0.0 CPython/3.12.3
|
Security for scmallet 0.1.2
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.