Skip to main content

Popari: a probabilistic graphical model for integrated spatial transcriptomics analysis

Project description

SpiceMix

overview

SpiceMix is an unsupervised tool for analyzing data of the spatial transcriptome. SpiceMix models the observed expression of genes within a cell as a mixture of latent factors. These factors are assumed to have some spatial affinity between neighboring cells. The factors and affinities are not known a priori, but are learned by SpiceMix directly from the data, by an alternating optimization method that seeks to maximize their posterior probability given the observed gene expression. In this way, SpiceMix learns a more expressive representation of the identity of cells from their spatial transcriptome data than other available methods.

SpiceMix can be applied to any type of spatial transcriptomics data, including MERFISH, seqFISH, HDST, and Slide-seq.

Install

pip install popari

Publishing

pip install hatch
pip install keyrings.alt

hatch build
hatch publish

Username: __token__ Password: {API token for PyPI}

Tests

To run the provided tests and ensure that SpiceMix can run on your platform, follow the instructions below:

  • Download this repo.
git clone https://github.com/alam-shahul/SpiceMixPlus.git
  • Install pytest in your environment.
pip install pytest
  • Navigate to the root directory of this repo.
  • Run the following command. With GPU resources, this test should execute without errors in ~2.5 minutes:
python -m pytest -s tests/test_popari_shared.py

Building Documentation

Assuming you have CMake:

  1. Navigate to docs/.
cd docs/
  1. Install Sphinx requirements.
pip install -r requirements.txt
  1. Clean and build.
make clean
make html
  1. Push to GitHub, and documentation will automatically build.

Cite

Cite our paper:

@article{chidester2020spicemix,
  title={SPICEMIX: Integrative single-cell spatial modeling for inferring cell identity},
  author={Chidester, Benjamin and Zhou, Tianming and Ma, Jian},
  journal={bioRxiv},
  year={2020},
  publisher={Cold Spring Harbor Laboratory}
}

paper

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

popari-0.0.37.tar.gz (48.5 kB view details)

Uploaded Source

Built Distribution

popari-0.0.37-py3-none-any.whl (56.4 kB view details)

Uploaded Python 3

File details

Details for the file popari-0.0.37.tar.gz.

File metadata

  • Download URL: popari-0.0.37.tar.gz
  • Upload date:
  • Size: 48.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-httpx/0.23.1

File hashes

Hashes for popari-0.0.37.tar.gz
Algorithm Hash digest
SHA256 4a803bba0aa0b70d83d55ee7e8ee53c6181b8c25825f572cd3387b748d43c1ba
MD5 31a366f6d06bef2af65976e13434c7d2
BLAKE2b-256 4709be1fe6c6322fdb3a4c61c13bf2bd27569e2ac79266ab80e99a71814d633d

See more details on using hashes here.

File details

Details for the file popari-0.0.37-py3-none-any.whl.

File metadata

  • Download URL: popari-0.0.37-py3-none-any.whl
  • Upload date:
  • Size: 56.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-httpx/0.23.1

File hashes

Hashes for popari-0.0.37-py3-none-any.whl
Algorithm Hash digest
SHA256 72bef20362ef25a9f5cced39ae750047d367f4d38f20f47ee7458981d2c96870
MD5 b43de81083d07bb220ceb8c5660fa2c1
BLAKE2b-256 534595e1e1c70e7a7575e41a979cba15362f4c7972405c65439ef528aedd2e26

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page