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cell2cell-0.6.8
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TBD
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | cell2cell-0.6.8 |
| نام | cell2cell |
| نسخه کتابخانه | 0.6.8 |
| نگهدارنده | ['cell2cell development team'] |
| ایمیل نگهدارنده | ['earmingol@eng.ucsd.edu'] |
| نویسنده | cell2cell development team |
| ایمیل نویسنده | earmingol@eng.ucsd.edu |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/cell2cell/ |
| مجوز | BSD-3-Clause |
# Inferring cell-cell interactions from transcriptomes with *cell2cell*
[![PyPI Version][pb]][pypi]
[](https://pepy.tech/project/cell2cell)
[pb]: https://badge.fury.io/py/cell2cell.svg
[pypi]: https://pypi.org/project/cell2cell/
## Getting started
Please refer to the [cell2cell website](https://earmingol.github.io/cell2cell/),
which includes tutorials and documentation
## Installation
**First, [install Anaconda following this tutorial](https://docs.anaconda.com/anaconda/install/)**
Once installed, create a new conda environment:
```
conda create -n cell2cell -y python=3.7 jupyter
```
Activate that environment:
```
conda activate cell2cell
```
Then, install cell2cell:
```
pip install cell2cell
```
## Examples
---
- A toy example using the **under-the-hood methods of cell2cell** is
[available here](https://github.com/earmingol/cell2cell/blob/master/examples/cell2cell/Toy-Example.ipynb).
This case allows personalizing the analyses in a higher level, but it may result **harder to use**.
- A toy example using an Interaction Pipeline for **bulk data** is
[available here](https://github.com/earmingol/cell2cell/blob/master/examples/cell2cell/Toy-Example-BulkPipeline.ipynb).
An Interaction Pipeline makes cell2cell **easier to use**.
- A toy example using an Interaction Pipeline for **single-cell data** is
[available here](https://github.com/earmingol/cell2cell/blob/master/examples/cell2cell/Toy-Example-SingleCellPipeline.ipynb).
An Interaction Pipeline makes cell2cell **easier to use**.
- An example of using *cell2cell* to infer cell-cell interactions across the **whole
body of *C. elegans*** is [available here](https://github.com/LewisLabUCSD/Celegans-cell2cell)
---
- Jupyter notebooks for reproducing the results in the manuscript of Tensor-cell2cell
[are available and can be run online in codeocean.com](https://doi.org/10.24433/CO.0051950.v2).
It specifically contains analyses on datasets of **COVID-19, Autism Spectrum Disorders (ASD) and the embryonic development
of *C. elegans***. These analyses evaluate changes in
cell-cell communication dependent on:
- [Different severities of COVID-19](https://files.codeocean.com/files/verified/bffc457e-caa6-4c39-b869-f52330804db0_v2.0/results.5afea95c-aec4-455d-b06e-b0c12ef10df1/06-BALF-Tensor-Factorization.html)
- [ASD condition of patients](https://files.codeocean.com/files/verified/bffc457e-caa6-4c39-b869-f52330804db0_v2.0/results.5afea95c-aec4-455d-b06e-b0c12ef10df1/11-Brain-ASD-Tensor-Factorization.html)
- [Multiple time points of the *C. elegans* development](https://files.codeocean.com/files/verified/bffc457e-caa6-4c39-b869-f52330804db0_v2.0/results.5afea95c-aec4-455d-b06e-b0c12ef10df1/08-Celegans-Tensor-Factorization.html)
- **Detailed tutorials for running Tensor-cell2cell and downstream analyses:**
- [Obtaining patterns of cell-cell communication with Tensor-cell2cell](https://earmingol.github.io/cell2cell/tutorials/ASD/01-Tensor-Factorization-ASD/)
- [Downstream analysis 1: Factor-specific analyses](https://earmingol.github.io/cell2cell/tutorials/ASD/02-Factor-Specific-ASD/)
- [Downstream analysis 2: Gene Set Enrichment Analysis](https://earmingol.github.io/cell2cell/tutorials/ASD/03-GSEA-ASD/)
- **Do you have precomputed communication scores?** Re-use them as a prebuilt tensor as [exemplified here](https://github.com/earmingol/cell2cell/blob/master/examples/tensor_cell2cell/Loading-PreBuiltTensor.ipynb).
This allows reusing previous tensors you built or even plugging in communication scores from other tools.
- **Run Tensor-cell2cell MUCH FASTER and ON THE CLOUD!** An example to perform the analysis on
**Google Colab while using a NVIDIA GPU** is [available here](https://colab.research.google.com/drive/1xE6Pm1u-XoSWV8a3oYpixUFj64FIDtl0?usp=sharing)
---
## Common issues
- When running Tensor-cell2cell (```InteractionTensor.compute_tensor_factorization()``` or ```InteractionTensor.elbow_rank_selection()```), a common error is
associated with Memory. This may happen when the tensor is big enough to make the computer run out of memory when the input of the functions in the parentheses is
```init='svd'```. To avoid this issue, just replace it by ```init='random'```.
## Ligand-Receptor pairs
- A repository with previously published lists of ligand-receptor pairs [is available here](https://github.com/LewisLabUCSD/Ligand-Receptor-Pairs).
You can use any of these lists as an input of cell2cell.
## Citation
- **cell2cell** should be cited using this research article:
- Armingol E., Ghaddar A., Joshi C.J., Baghdassarian H., Shamie I., Chan J.,
Her H.L., Berhanu S., Dar A., Rodriguez-Armstrong F., Yang O., O’Rourke E.J., Lewis N.E.
[Inferring a spatial code of cell-cell interactions across a whole animal body](https://doi.org/10.1371/journal.pcbi.1010715).
*PLOS Computational Biology **18(11)**: e1010715*, (2022). **DOI: 10.1371/journal.pcbi.1010715**
- **Tensor-cell2cell** should be cited using this research article:
- Armingol E., Baghdassarian H., Martino C., Perez-Lopez A., Aamodt C., Knight R., Lewis N.E.
[Context-aware deconvolution of cell-cell communication with Tensor-cell2cell](https://doi.org/10.1038/s41467-022-31369-2)
*Nat. Commun.* **13**, 3665 (2022). **DOI: 10.1038/s41467-022-31369-2**
نیازمندی
| مقدار | نام |
|---|---|
| >=1.16 | numpy |
| >=1.0.0 | pandas |
| >=1.1 | xlrd |
| >=2.6.2 | openpyxl |
| >=2.3 | networkx |
| <=3.5.1,>=3.2.0 | matplotlib |
| >=0.11.0 | seaborn |
| - | scikit-learn |
| - | umap-learn |
| - | tqdm |
| - | statsmodels |
| - | statannotations |
| ==0.8.1 | tensorly |
| - | kneed |
| - | scanpy |
| ==1.0.3 | gseapy |
نحوه نصب
نصب پکیج whl cell2cell-0.6.8:
pip install cell2cell-0.6.8.whl
نصب پکیج tar.gz cell2cell-0.6.8:
pip install cell2cell-0.6.8.tar.gz