jinworks / CellChat

## Update
CellChat v3 (Spatial CellChat) is an updated version that 
- enables the inference of cell-cell communication at single-cell resolution from spatial transcriptomics data. 
- is applicable to diverse technologies of spatial transcriptomics data. We add Frequently Asked Questions (FAQ) when analyzing spatially resolved transcriptomics datasets, particularly on how to apply Spatial CellChat to different technologies of spatial transcriptomics data, including sequencing-based and in-situ imaging-based readouts. 
CellChat v2 is a version that includes
- inference of spatially proximal cell-cell communication between interacting cell groups from spatially resolved transcriptomics
- expanded database CellChatDB v2 by including more than 1000 protein and non-protein interactions (e.g. metabolic and synaptic signaling) with rich annotations. A function named `updateCellChatDB` is also provided for easily updating CellChatDB. 
- new functionalities enabling easily interface with other computational tools for single-cell data analysis and cell-cell communication analysis
- interactive web browser function to allow exploration of CellChat outputs of spatially proximal cell-cell communication
For the version history and detailed important changes, please see the NEWS file.
A step-by-step protocol for cell-cell communication analysis using CellChat is available at Jin et al., Nature Protocols 2024. Please kindly cite this paper when using CellChat version >= 1.5. We greatly appreciate the users' support and suggestions that make it possible for us to update CellChat since we published the first version in the year of 2021. 
## Capabilities
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. 
- It can quantitatively characterize and compare the inferred cell-cell communication networks using an integrated approach by combining social network analysis, pattern recognition, and manifold learning approaches.
- It provides an easy-to-use tool for extracting and visualizing high-order information of the inferred networks. For example, it allows ready prediction of major signaling inputs and outputs for all cell populations and how these populations and signals coordinate together for functions.
- It enables comparative analysis of cell-cell communication across different conditions and identification of altered signaling and cell populations.
- It provides several visualization outputs to facilitate intuitive user-guided data interpretation.
## Installation
To ensure efficient and scalable inference of cell-cell communication at single-cell resolution from spatial transcriptomics data, Spatial CellChat optimizes the data structure within CellChat object. To enable users still can run their previously calculated CellChat v1/v2 object and smoothly upgrade to CellChat v3, we currently deposite t