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pciseq

A probabilistic cell typing algorithm for spatial transcriptomics.

https://github.com/acycliq/pciseq

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
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    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
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    Academic publication links
    Links to: nature.com
  • ○
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    JOSS paper metadata
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    Scientific vocabulary similarity
    Low similarity (16.1%) to scientific vocabulary

Keywords

neuroscience spatial-transcriptomics
Last synced: 6 months ago · JSON representation

Repository

A probabilistic cell typing algorithm for spatial transcriptomics.

Basic Info
  • Host: GitHub
  • Owner: acycliq
  • License: mit
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 173 MB
Statistics
  • Stars: 24
  • Watchers: 4
  • Forks: 8
  • Open Issues: 2
  • Releases: 0
Topics
neuroscience spatial-transcriptomics
Created about 5 years ago · Last pushed 8 months ago
Metadata Files
Readme

README.md

pciSeq: Probabilistic Cell typing by In situ Sequencing

Actions Status repo size Downloads Python version Licence: GPL v3 Contributors

GitHub stars GitHub forks

A Python package that implements the cell calling algorithm as described in Qian, X., et al. Nature Methods (2020)

screenshot

Installation

python -m pip install pciSeq Requirement: Python >= 3.8

If you want to work with the source code you can download the repo and then replicate the python environment by conda env create -n pciSeq -f /path/to/environment.yml

That will create a conda environment with the name pciSeq containing all the necessary packages to run the algorithm. To activate it run conda activate pciSeq or, if you open the project in your IDE, then in your project settings, switch your interpreter to the interpreter of the pciSeq env.

Usage

You need to create two pandas dataframes for the spots and the single cell data and a coo_matrix for the label image (which in most cases will be the output of some image segmentation application). Then you pass them into the pciSeq.fit() method as follows: ``` import pciSeq

res = pciSeq.fit(spots=spotsdf, coo=labelimage, scRNAseq=scRNA_df) `` See the demo below for a more detailed explanation about the arguments ofpciSeq.fit()` and its return values.

There is also a fourth argument (optional) to override the default hyperparameter values which are initialised by the config.py module. To pass user-defined hyperparameter values, create a dictionary with keys the hyperparameter names and values their new values. For example, to exclude all Npy and Vip spots you can do:

``` import pciSeq

opts = { 'excludegenes': ['Npy', 'Vip'] } res = pciSeq.fit(spots=spotsdf, coo=labelimage, scRNAseq=scRNAdf, opts=opts) ```

Demo

You can run a pciSeq demo in google colab: Open In Colab

Viewer

An interactive viewer to explore the data runs on this url. Instructions about building this viewer with your own data are here. \ If you have v 0.0.49 or greater you can also launch the viewer automatically by setting opts = {'launch_viewer': True} and passing it to pciSeq.fit(), see Open In Colab

Diagnostics

Diagnostics will help you understand whether pciSeq has been misconfigured, the algorithm has taken the wrong path and will produce meaningless results when it finishes. You will need however to install redis (or Memurai if you are using Windows).

For linux do: sudo apt-get install redis-server redis-tools and then start the service: sudo service redis-server start

You can get the free version of memurai from here. Once installed, the service should start automatically but you can manually do that by: memurai.exe –service-start

If redis (or memurai) is missing from your system, the call to launch the diagnostics dashboard will be ignored. If you are interested in this feature you may find this notebook Open In Colab useful

Change Log

[0.0.59] - 2023-09-22

  • Fixed a SIGSERV error under pandas '2.1.1'

[0.0.56] - 2023-07-03

  • Diagnostics dashboard

  • Baselayers on the viewer. You can have multiple background images and switch between them.

[0.0.50] - 2023-05-27

  • Single cell data are optional, more info can be found here Open In Colab

  • pciSeq.fit() takes keyword arguments

References

Qian, X., et al. (2020). Probabilistic cell typing enables fine mapping of closely related cell types in situ. Nat Methods 17, 101 - 106.

Owner

  • Name: Dimitris Nicoloutsopoulos
  • Login: acycliq
  • Kind: user
  • Location: London

Quantitative neuroscience @ UCL

GitHub Events

Total
  • Issues event: 3
  • Watch event: 1
  • Delete event: 14
  • Issue comment event: 15
  • Push event: 200
  • Pull request event: 3
  • Create event: 23
Last Year
  • Issues event: 3
  • Watch event: 1
  • Delete event: 14
  • Issue comment event: 15
  • Push event: 200
  • Pull request event: 3
  • Create event: 23

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 770
  • Total Committers: 2
  • Avg Commits per committer: 385.0
  • Development Distribution Score (DDS): 0.003
Past Year
  • Commits: 227
  • Committers: 1
  • Avg Commits per committer: 227.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Dimitris d****s@g****m 768
Mathieu m****x@m****m 2
Committer Domains (Top 20 + Academic)
me.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 15
  • Total pull requests: 3
  • Average time to close issues: 4 months
  • Average time to close pull requests: about 3 hours
  • Total issue authors: 12
  • Total pull request authors: 2
  • Average comments per issue: 7.47
  • Average comments per pull request: 1.33
  • Merged pull requests: 3
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 2
  • Average time to close issues: 2 days
  • Average time to close pull requests: 19 minutes
  • Issue authors: 2
  • Pull request authors: 1
  • Average comments per issue: 6.0
  • Average comments per pull request: 1.0
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • rocketeer1998 (2)
  • pakiessling (2)
  • VPetukhov (1)
  • jlause (1)
  • CristiSoitu (1)
  • LouisK92 (1)
  • acycliq (1)
  • YubinXie (1)
  • Epoch1128 (1)
  • mcaponegro (1)
  • evenDDDDD (1)
  • ocimakamboj (1)
Pull Request Authors
  • mwshinn (2)
  • MathieuBo (1)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 93 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 62
  • Total maintainers: 1
pypi.org: pciseq

Probabilistic cell typing for spatial transcriptomics

  • Versions: 62
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 93 Last month
Rankings
Dependent packages count: 10.0%
Forks count: 11.9%
Stargazers count: 14.5%
Average: 15.2%
Downloads: 18.1%
Dependent repos count: 21.7%
Maintainers (1)
acycliq
Last synced: 6 months ago

Dependencies

setup.py pypi
.github/workflows/pytest.yaml actions
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
requirements.txt pypi
  • altair *
  • diplib *
  • fastremap *
  • flask *
  • jupyterlab *
  • natsort *
  • numexpr *
  • numpy_groupies *
  • pandas *
  • pip *
  • pyvips *
  • redis *
  • scikit-image *
  • scikit-learn *
  • scipy *
  • streamlit *
  • tqdm *
environment.yml pypi