Please refer to ventolab/CellphoneDB for the most up to date version. |
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CellPhoneDB is a publicly available repository of curated receptors, ligands and their 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. Databases from which 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 application (cellphonedb.org) and run in the analysis in our private cloud, or just run CellPhoneDB with your own computational resources. (The latter is preferable if you are going to work with big datasets).
NOTE: Works with Python v3.6 or greater. 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 using an isolated python environment (as described in steps 1 and 2) using conda or virtualenv but you could of course omit these steps and install via pip immediately.
- Create python=>3.6 environment
- Using conda:
conda create -n cpdb python=3.7 - Using virtualenv:
python -m venv cpdb
- Activate environment
- Using conda:
source activate cpdb - Using virtualenv:
source cpdb/bin/activate
- Install CellPhoneDB
pip install cellphonedb
Please, activate your environment if you didn't previously
- Using conda:
source activate cpdb - Using virtualenv:
source cpdb/bin/activate
To use the example data, please download meta/counts test data. i.e.
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.txtNote: counts file can be a text file or h5ad (recoommended), h5 or a path to a folder containing mtx/barcode/features.
cellphonedb method statistical_analysis test_meta.txt test_counts.txtUsing text files
cellphonedb method analysis test_meta.txt test_counts.txt Using h5ad count file
cellphonedb method analysis test_meta.txt test_counts.h5adPlease check the results documentation in order to understand the results.
~ Optional Method parameters:
--counts-data: [ensembl | gene_name | hgnc_symbol] Type of gene identifiers in the counts data--project-name: Name of the project. A subfolder with this name is created in the output folder--iterations: Number of iterations for the statistical analysis [1000]--threshold: % of cells expressing the specific ligand/receptor--result-precision: Number of decimal digits in results [3]--output-path: Directory where the results will be allocated (the directory must exist) [out]--output-format: Output format of the results files (extension will be added to filename if not present) [txt]--means-result-name: Means result filename [means]--significant-means-result-name: Significant mean result filename [significant_means]--deconvoluted-result-name: Deconvoluted result filename [deconvoluted]--verbose/--quiet: Print or hide CellPhoneDB logs [verbose]--subsampling: Enable subsampling--subsampling-log: Enable subsampling log1p for non log-transformed data inputs !!mandatory!!--subsampling-num-pc: Subsampling NumPC argument (number of PCs to use) [100]--subsampling-num-cells: Number of cells to subsample to [1/3 of cells]
~ Optional Method Statistical parameters
--pvalues-result-name: P-values result filename [pvalues]--pvalue: P-value threshold [0.05]--debug-seed: Debug random seed -1. To disable it please use a value >=0 [-1]--threads: Number of threads to use. >=1 [4]
Set number of iterations and threads
cellphonedb method statistical_analysis yourmetafile.txt yourcountsfile.txt --iterations=10 --threads=2Set project subfolder
cellphonedb method analysis yourmetafile.txt yourcountsfile.txt --project-name=new_projectSet output path
mkdir custom_folder
cellphonedb method statistical_analysis yourmetafile.txt yourcountsfile.txt --output-path=custom_folderSubsampling
cellphonedb method analysis yourmetafile.txt yourcountsfile.txt --subsampling --subsampling-log false --subsampling-num-cells 3000In order to plot results from the statistical methods, you need to run it first.
Currently there are two plot types available: dot_plot & heatmap_plot
Once you have the needed files (means & pvalues) you can proceed as follows:
cellphonedb plot dot_plotcellphonedb plot heatmap_plot yourmeta.txtThis plot type requires ggplot2 R package installed and working
You can tweak the options for the plot with these arguments:
--means-path: The means output file [./out/means.txt]--pvalues-path: The pvalues output file [./out/pvalues.txt]--output-path: Output folder [./out]--output-name: Filename of the output plot [plot.pdf]--rows: File with a list of rows to plot, one per line [all available]--columns: File with a list of columns to plot, one per line [all available]--verbose / --quiet: Print or hide CellPhoneDB logs [verbose]
Available output formats are those supported by R's ggplot2 package, among others they are:
pdfpngjpeg
This format will be inferred from the --output-name argument
To plot only desired rows/columns (samples for rows and columns based in example data files):
cellphonedb plot dot_plot --rows in/rows.txt --columns in/columns.txtThis plot type requires pheatmap R package installed and working
This plot type includes two features count & log_count
You can tweak the options for the plot with these arguments:
--pvalues-path: The pvalues output file [./out/pvalues.txt]--output-path: Output folder [./out]--count-name: Filename of the output plot [heatmap_count.pdf]--log-name: Filename of the output plot using log-count of interactions [heatmap_log_count.pdf]--count-network-name: Filename of the output network file [count_network.txt]--interaction-count-name: Filename of the output interactions-count file [interactions_count.txt]--pvalue: pvalue threshold to consider when plotting [0.05]--verbose / --quiet: Print or hide cellphonedb logs [verbose]
Available output formats are those supported by R's pheatmap package, among others they are:
pdfpngjpeg
This format will be inferred from the --count-name & --log-name arguments.
CellPhoneDB databases can be updated from the remote repository through our tool. Furthermore, available versions can be listed and downloaded for use.
To use one of those versions, a user must provide the argument --database <version_or_file> to the method to be executed.
If the given parameter is a readable database file, it will be used as is. Otherwise it will use some of the versions matching the selected version.
If the selected version does not exist in the local environment it will be downloaded from the remote repository. (See below.) If no --database argument is given in methods execution, it will use the latest local version available.
Downloaded versions will be stored in a user folder under ~/.cpdb/releases
The command to list available versions from the remote repository is:
cellphonedb database list_remoteThe command to list available versions from the local repository is:
cellphonedb database list_localThe command to download a version from the remote repository is:
cellphonedb database downloador
cellphonedb database download --version <version_spec|latest> whereby version_spec must be one of the listed in the database list_remote command.
If no version is specified or latest is used as a version_spec, the newest available version will be downloaded
A user can generate custom databases and use them. In order to generate a new database, a user can provide his/her own lists.
These lists can be: genes, proteins, complexes and/or interactions. In the generation process they will get merged with the ones from the CellPhoneDB release sources. The user lists have higher precedence than the ones included in CellPhoneDB package.
To generate such a database the user has to issue this command:
cellphonedb database generate Generate specific parameters:
--user-protein: Protein input file--user-gene: Gene input file--user-complex: Complex input file--user-interactions: Interactions input file--fetch: Some lists can be downloaded from original sources while creating the database, eg: uniprot, ensembl. By default, the snapshots included in the CellPhoneDB package will be used; to enable a fresh copy--fetchmust be appended to the command--result-path: Output folder--log-file: Log file--user-interactions-only: Use only interactions provided.
Result database file is generated in the folder out with cellphonedb_user_{datetime}.db. The user defined input tables will be merged with the current CellPhoneDB input tables. To use this database, please use --database parameter in methods.
E.g:
cellphonedb method statistical_analysis in/example_data/test_meta.txt in/example_data/test_counts.txt --database out/cellphonedb_user_2019-05-10-11_10.db
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To add or correct some interactions
Input:
your_custom_interaction_file.csv: Comma separated file (use mandatory columns!) with interactions to add/correct.
Command:
cellphonedb database generate --user-interactions your_custom_interaction_file.csv
Result:
New database file with CellPhoneDB interactions + user custom interactions. For duplicated interactions, user lists overwrite the CellPhoneDB original data.
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To use only user-specific interactions
Input:
your_custom_interaction_file.csv: Comma separated file (use mandatory columns!) with interactions to use.
Command:
cellphonedb database generate --user-interactions your_custom_interaction_file.csv --user-interactions-only
Result:
New database file with only user custom interactions.
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To correct any protein data
Input:
your_custom_protein_file.csv: Comma separated file (use mandatory columns!) with proteins to overwrite.
Command:
cellphonedb database generate --user-protein your_custom_protein_file.csv
Result:
New database file with CellPhoneDB interactions + user custom interactions. For duplicated interactions or proteins, user lists overwrite the CellPhoneDB original data.
-
To add some interactions and correct any protein data
Input:
your_custom_interaction_file.csv: Comma separated file (use mandatory columns!) with interactions to add/correct.your_custom_protein_file.csv: Comma separated file (use mandatory columns!) with proteins to overwrite.
Command:
cellphonedb database generate --user-interactions your_custom_interaction_file.csv --user-protein your_custom_protein_file.csv
Result:
New database file with CellPhoneDB interactions + user custom interactions. On duplicated interactions or proteins, user list overwrites CellPhoneDB original data.
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To update remote sources (UniProt, IMEx, ensembl, etc.)
IMPORTANT
This command uses external resources allocated in external servers. The command may not end correctly if external servers are not available. The timing of this step depends on external servers and the user's internet connection and can take a lot of time.
Input:
your_custom_interaction_file.csv: Comma separated file (use mandatory columns!) with interactions to add/correct.your_custom_protein_file.csv: Comma separated file (use mandatory columns!) with proteins to overwrite.
Command:
cellphonedb database generate --fetch
Result:
New database file with CellPhoneDB interactions + user custom interactions. For duplicated interactions or proteins, user lists overwrite the CellPhoneDB original data.
Some lists can be downloaded from original sources while creating the database, e.g. uniprot, ensembl. By default, the snapshots included in the CellPhoneDB package will be used---to enable a fresh copy --fetch must be appended to the command.
In order to use specific lists those can be specified like this --user-protein, --user-gene, --user-complex, --user-interactions, --user-interactions-only
followed by the corresponding file path.
The database file can be then used as explained below. The intermediate lists used for the generation will be saved along the database itself.
As the lists are processed, then filtered, and lastly collected, two versions may exist: _generated is the unfiltered one whereas _input is the final state prior being inserted in the database.
CellPhoneDB is an open-source project. If you are interested in contributing to this project, please let us know.
You can check all project documentation in the Docs section
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