R toolkit for inference, visualization and analysis of cell-cell communication from single-cell and spatially resolved transcriptomics
updateCellChat
function.scale.factors
for easier interpretation when applying other spatial technologies. In version 2.1.1, we change scale.factors
to spatial.factors
, but users still can run the old CellChat object with scale.factors
. Users can also update the old CellChat object.object@meta$slices
to object@meta$samples
in order to allow the identification of consistent communication across samples using the updated function filterCommunication
. Users need to update the old CellChat object using updateCellChat
. The ECM-Receptor signaling is assumed as diffusible signaling by default when analyzing spatial transcriptomics.CellChat v2 is an updated version that includes
updateCellChatDB
is also provided for easily updating CellChatDB.For the version history and detailed important changes, please see the NEWS file.
Please check the CellChat v2 paper for a comprehensive protocol of CellChat v2 that is used for both single-cell and spatially resolved transcriptomic data.
In addition to infer the intercellular communication from any given scRNA-seq data and spatially resolved transcriptomics data, CellChat provides functionality for further data exploration, analysis, and visualization.
CellChat R package can be easily installed from Github using devtools:
devtools::install_github("jinworks/CellChat")
Please make sure you have installed the correct version of NMF
and circlize
package. See instruction below.
install.packages('NMF')
. Please check here for other solutions if you encounter any issue. You might can set Sys.setenv(R_REMOTES_NO_ERRORS_FROM_WARNINGS=TRUE)
if it throws R version error.devtools::install_github("jokergoo/circlize")
if you encounter any issue.devtools::install_github("jokergoo/ComplexHeatmap")
if you encounter any issue.pip install umap-learn
. Please check here if you encounter any issue.Some users might have issues when installing CellChat pacakge due to different operating systems and new R version. Please check the following solutions:
Please check the tutorial directory of the repo. Example datasets are publicly available at figshare.
We build a user-friendly web-based “CellChat Explorer” that contains two major components:
We also developed an Interactive Web Browser that allows exploration of CellChat outputs of spatially proximal cell-cell communication using a built-in function runCellChatApp
, and a standalone CellChat Shiny App for the above Cell-Cell Communication Atlas Explorer.
If you have any question, comment or suggestion, please use github issue tracker to report coding related issues of CellChat.
CellChat is an open source software package and any contribution is highly appreciated!
We use GitHub's Pull Request mechanism for reviewing and accepting submissions of any contribution. Issue a pull request on the GitHub website to request that we merge your branch's changes into CellChat's master branch. Be sure to include a description of your changes in the pull request, as well as any other information that will help the CellChat developers involved in reviewing your code.
Hardware requirements: CellChat package requires only a standard computer with enough RAM to support the in-memory operations.
Software requirements: This package is supported for macOS, Windows and Linux. The package has been tested on macOS: Ventura (version 13.5) and Windows 10. Dependencies of CellChat package are indicated in the Description file, and can be automatically installed when installing CellChat pacakge. CellChat can be installed on a normal computer within few mins.
CellChat is an R package designed for inference, analysis, and visualization of cell-cell communication from single-cell and spatially resolved transcriptomics. CellChat aims to enable users to identify and interpret cell-cell communication within an easily interpretable framework, with the emphasis of clear, attractive, and interpretable visualizations.
CellChatDB is a manually curated database of literature-supported ligand-receptor interactions in mutiple species, leading to a comprehensive recapitulation of known molecular interaction mechanisms including multi-subunit structure of ligand-receptor complexes and co-factors.
If you use CellChat or CellChatDB in your research, please considering citing our papers: