cntools 2.1.11

A package for identifying cellular neighborhoods

CNTools

System requirements

The software denpendencies are listed in pyproject.toml. The software is independent of operating systems except that it needs Rosetta translation to work in macOS with Apple silicon.

Installation guide

As we need a conda package pydot=1.4.2 (not a pip one), the package should be installed by

conda create -n cntools python=3.8 pydot=1.4.2
conda activate cntools
python -m pip install cntools

In macOS with Apple silicon, the package should be installed in an x86-64 conda environment by

CONDA_SUBDIR=osx-64 conda create -n cntools python=3.8 pydot=1.4.2
conda activate cntools
python -m pip install cntools

Instructions for use

Idenfity and smooth cellular neighborhoods

See tests/test_crc.ipynb for CRC dataset, tests/test_t2d.ipynb for T2D dataset, and tests/test_hlt.ipynb for HLT dataset. For example, to run CNE on the CRC dataset, just do

from cntools.datasets import load_ct_order, prep_crc, Dataset
from cntools.identification import CNE
from cntools.smoothing import NaiveSmooth

# load dataset
df = prep_crc(pd.read_csv('data/CRC/CRC_clusters_neighborhoods_markers.csv'))
ct_order = load_ct_order('data/CRC/ct_order.json')
ds = Dataset(df, ct_order)

# identify
identifier = CNE(n_cns=9, perp=15, lam=0.25)
cns = identifier.fit(ds) # output CN

# smooth
smoother = NaiveSmooth(ds=ds, n_cns=identifier.n_cns, feats=identifier.feats, s=3)
cns_smoothed_naive = smoother.fit(cns) # output CN after smoothing

Analyze cellular neighborhoods

See jupyter notebooks in the tests/analysis folder.

Demo

Run tests/test_crc.ipynb, tests/test_t2d.ipynb, and tests/test_hlt.ipynb for CN identification and smoothing. Run jupyter notebooks in the tests/analysis/ folder for CN analyses. Expected CN outputs are in the tests/cn/*/CNE/ folder. Expected analysis outputs are in the tests/analysis_res/*/CNE/ folder.

Acknowledgements

Our implementation adapts the code of Spatial LDA, Schurch et al. (2020), and Bhate et al. (2022) as cellular neighborhood identification and analysis methods. We thank the authors for sharing their code.

@article{chen2020modeling,
  title={Modeling Multiplexed Images with Spatial-LDA Reveals Novel Tissue Microenvironments},
  author={Chen, Zhenghao and Soifer, Ilya and Hilton, Hugo and Keren, Leeat and Jojic, Vladimir},
  journal={Journal of Computational Biology},
  year={2020},
  publisher={Mary Ann Liebert, Inc., publishers 140 Huguenot Street, 3rd Floor New~…}
}

@article{schurch2020coordinated,
  title={Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front},
  author={Sch{\"u}rch, Christian M and Bhate, Salil S and Barlow, Graham L and Phillips, Darci J and Noti, Luca and Zlobec, Inti and Chu, Pauline and Black, Sarah and Demeter, Janos and McIlwain, David R and others},
  journal={Cell},
  volume={182},
  number={5},
  pages={1341--1359},
  year={2020},
  publisher={Elsevier}
}

@article{bhate2022tissue,
  title={Tissue schematics map the specialization of immune tissue motifs and their appropriation by tumors},
  author={Bhate, Salil S and Barlow, Graham L and Sch{\"u}rch, Christian M and Nolan, Garry P},
  journal={Cell Systems},
  volume={13},
  number={2},
  pages={109--130},
  year={2022},
  publisher={Elsevier}
}