CellPhoneDB is a publicly available repository of curated receptors, ligands and its interactions. Subunit architecture is included for both ligands and receptors, representing heteromeric complexes accurately. This is crucial, as cell-cell communication relies on multi-subunit protein complexes that go beyond the binary representation used in most databases and studies.

CellPhoneDB integrates existing datasets that pertain to cellular communication and new manually reviewed information. Prestigious databases CellPhoneDB gets information are: UniProt, Ensembl, PDB, the IMEx consortium, IUPHAR.

CellPhoneDB can be used to search for a particular ligand/receptor or interrogate your own single-cell transcriptomics data.

To start using CellPhoneDB, you can use our interactive web (cellphonedb.org) and run in the analysis in our private cloud, or just run CellPhoneDB in your computer/cloud/farm. Please, take the second way if you are going to work with big datasets).

NOTE: Works with Python v3.5 or superior. If your default Python interpreter is for v2.x (you can check it with python --version), calls to python/pip should be substituted by python3/pip3.

We highly recommend to use a virtual env (steps 1 and 2) but you can omit.

1. Create python > 3.5 virtual-env

python -m venv cpdb-venv

2. Activate virtual-env

source cpdb-venv/bin/activate

3. Install cellphonedb

pip install cellphonedb

Please, run step 0 if you didn't activate the virtual-env previously

0. Activate virtual-env

source cpdb-venv/bin/activate

For run example data, please download meta/counts test data.

Hint (if you are in terminal):

curl https://raw.githubusercontent.com/Teichlab/cellphonedb/master/in/example_data/test_counts.txt --output test_counts.txt
curl https://raw.githubusercontent.com/Teichlab/cellphonedb/master/in/example_data/test_meta.txt --output test_meta.txt

cellphonedb method statistical_analysis test_meta.txt test_counts.txt 

cellphonedb method analysis test_meta.txt test_counts.txt 

Please check result documentation for underestand the results.

~ Optional Method parameters:

• --project-name: Name of the project. It creates a subfolder in output folder
• --iterations: Number of pvalues analysis iterations [1000]
• --threshold: % of cells expressing a gene
• --result-precision: Number of decimal digits in results [3]
• --output-path: Directory where the results will be allocated (the directory must exist) [out]
• --means-result-name: Means result namefile [means.txt]
• --significant-mean-result-name: Significant result namefile [significant_means.txt]
• --deconvoluted-result-name: Deconvoluted result namefile [deconvoluted.txt]
• --verbose/--quiet: Print or hide cellphonedb logs [verbose]

~ Optional Method Statistical parameters

• --pvalues-result-name: Pvalues result namefile [pvalues.txt]
• --means-pvalues-result-name: Pvalues-means result namefile [pvalues_means.txt]
• --debug-seed: Debug random seed -1 for disable it. >=0 [-1]
• --threads: Number of threads to use. >=1 [-1]

Usage Examples:

Set number of iterations and threads

cellphonedb method statistical_analysis yourmetafile.txt yourcountsfile.txt --iterations=10 --threads=2

Set project subfolder

cellphonedb method analysis yourmetafile.txt yourcountsfile.txt --project-name=new_project

Set output path

mkdir custom_folder
cellphonedb method statistical_analysis yourmetafile.txt yourcountsfile.txt --output-path=custom_folder

CellPhoneDB is Open Source project. If you are interesed to contribute to this project, please let us know.

You can check all project documentation in Docs section