Skip to main content

STRIDE

PyPI Downloads Documentation Status

Spatial TRanscrIptomics DEconvolution by topic modeling (STRIDE), is a computational method to decompose cell types from spatial mixtures by leveraging topic profiles trained from single-cell transcriptomics. Besides the cell-type composition deconvolution, STRIDE also provides several downstream analysis functions, including (1) signature (i.e., topic) detection and visualization, (2) spatial clustering and domain identification based on neighborhood cell populations and (3) reconstruction of three-dimensional architecture from sequential ST slides of the same tissue.

avatar

Documentation

For full installation and usage of STRIDE, please refer to the documentation.

Change Log

v1.0.1

  • Build STRIDE.

v1.0.2

  • Add mapping function to identify similarest cells for spatial spots.
  • Fix bugs of integration.
  • Fix bugs of deconvolution with trained topic model.

v1.0.3

  • Update mapping function to make cell mapping consistent with the deconvolved fractions.

v1.0.4

  • Fix a bug that produced an empty *_spot_celltype_frac.txt when a topic column summed to zero (NaN propagated through the deconvolution). Zero-sum topic columns are now filled with 0 after normalization.

Install STRIDE

git clone https://github.com/DongqingSun96/STRIDE.git
cd STRIDE
pip install -r requirements.txt
python setup.py install

Usage

STRIDE --help
usage: STRIDE [-h] [-v] {deconvolve,plot,cluster,integrate,map} ...

STRIDE (Spatial TRanscrIptomics DEconvolution by topic modelling) is a cell-
type deconvolution tool for spatial transcriptomics by using single-cell
transcriptomics data.

positional arguments:
  {deconvolve,plot,cluster,integrate}
    deconvolve          Decompose celltype proportion for spatial
                        transcriptomics.
    plot                Visualize the deconvolution result.
    cluster             Neighbourhood analysis based on cell-type composition
                        and local cell population
    integrate           Integrate multiple samples from the same tissue.
    map                 Identify similarest cells for spatial spots.

optional arguments:
  -h, --help            show this help message and exit
  -v, --version         Print version info.

Metadata

Release files for stridespatial 1.0.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for stridespatial 1.0.4
File Size Uploaded Provenance Actions
stridespatial-1.0.4.tar.gz 34.0 kB Without provenance Details for stridespatial-1.0.4.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for stridespatial 1.0.4
File Interpreter ABI Platform Actions
stridespatial-1.0.4-py3-none-any.whl Python 3 none any Details for stridespatial-1.0.4-py3-none-any.whl

Total release size: 72.1 kB

Release files / stridespatial-1.0.4.tar.gz

Download URL stridespatial-1.0.4.tar.gz
Size 34.0 kB
Tags Source
SHA-256 checksum
How to use checksums
284b597788975037a155eade5e2b4676bef9562975e495d3211b2ef872dfcae2
BLAKE2b-256 checksum
How to use checksums
40f40bfb8a60bddff244159f9d99c4d999d5b13cc4106c3f2ae2048722e2f918
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.10.13

Release files / stridespatial-1.0.4-py3-none-any.whl

Download URL stridespatial-1.0.4-py3-none-any.whl
Size 38.2 kB
Tags Python 3
SHA-256 checksum
How to use checksums
5800251bbdec4eee56855c9f2b10e645b1ff6d6ebb6f1b5228c529ee3849cc09
BLAKE2b-256 checksum
How to use checksums
497649a8f7f202d189c9b7c6e1d2ba98ec2ce455dbe3baa499153be2def293a2
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.10.13

Security for stridespatial 1.0.4

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.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page