Inference and analysis of cell-cell communication using CellChat methods
Aim. Evidence-backed execution summary for Inference and analysis of cell-cell communication using CellChat methods from Inference and analysis of cell-cell communication using CellChat.
Show snapshot details
On this page
This experiment, in seven questions
Jump straight to the part of the recipe you need. Data and provenance labels stay close to the action they support.
Shopping and prep list
What do I need before I start?
mouse
Subject model for the experiment.
- Use
- confirm full cohort details in the source paper
Joint learning of time-course scRNA-seq data to uncover dynamic communication patterns
reagent used in the protocol.
- Use
- Moreover, we studied the detailed changes in the outgoing signaling across all significant pathways using pattern recognition analysis (Fig.; see "Methods" section). We found that skin fibroblasts change their major and minor outgoing communication patterns between E13.5 and E14.5. At E13.5, early...
Discussion
Recent advances in spatially resolved transcriptomic techniques offer an opportunity to explore spatial organization of cells in tissues. The integration of spatial information with scRNA-seq data will likely offer new insights into cellular crosstalk,. The present version of CellChat provides an easy-to-use tool...
- Use
- Recent advances in spatially resolved transcriptomic techniques offer an opportunity to explore spatial organization of cells in tissues. The integration of spatial information with scRNA-seq data will likely offer new insights into cellular crosstalk,. The present version of CellChat provides an easy-to-use tool...
Introduction
Software used for acquisition, scoring, statistics, or reporting.
- Use
- Here we develop CellChat, an open source R package ( https://github.com/sqjin/CellChat ) to infer, visualize and analyze intercellular communications from scRNA-seq data. First, we manually curate a comprehensive signaling molecule interaction database that takes into account the known structural composition of liga...
Identification of major signals for specific cell groups and global communication patterns
Software used for acquisition, scoring, statistics, or reporting.
- Use
- To identify key signals and latent communication patterns among all signaling pathways, CellChat uses an unsupervised learning method non-negative matrix factorization that has been successfully applied in pattern recognition,,,. First, the latent patterns were found for sending cells by summarizing the communic...
Before you run
What should be confirmed before execution?
First confirmation
Equipment is listed but no product mappings are linked.
Confirm before execution
This page is backed by a publishable Replication Data Ledger package with zero critical source-verification issues.
Confirm before execution
Open the source paper before finalizing run-specific details.
Procurement checkpoint
Use source-stated vendors where present. Treat mapped products as sourcing options unless the page marks an exact source match.
Open quote workflowStep-by-step procedure
What do I do, in order?
Introduction
Here we develop CellChat, an open source R package ( https://github.com/sqjin/CellChat ) to infer, visualize and analyze intercellular communications from scRNA-seq data. First, we manually curate a comprehensive signaling molecule interaction database that takes into account the known structural composition of ligand-receptor interactions, such as multimeric ligand-receptor complexes, soluble agonists and antagonists, as well as stimulatory and inhibitory membrane-bound co-receptors. Next, CellChat infers cell-state specific signaling communications within a given scRNA-seq data using mass action models, along with differential expression analysis and statistical tests on cell groups, which can be both discrete states or continuous states along the pseudotime cell trajectory. CellChat also provides several visualization outputs to facilitate intuitive user-guided data interpretation....
Methods
To construct a database of ligand-receptor interactions that comprehensively represents the current state of knowledge, we manually reviewed other publicly available signaling pathway databases, as well as peer-reviewed literature and developed CellChatDB. CellChatDB is a database of literature-supported ligand-receptor interactions in both mouse and human. The majority of ligand-receptor interactions in CellChatDB were manually curated on the basis of KEGG (Kyoto Encyclopedia of Genes and Genomes) signaling pathway database ( https://www.genome.jp/kegg/pathway.html ). Additional signaling molecular interactions were gathered from recent peer-reviewed experimental studies. We took into account not only the structural composition of ligand-receptor interactions, that often involve multimeric receptors, but also cofactor molecules, including soluble agonists and antagonists, as we...
Identification of major signals for specific cell groups and global communication patterns
To identify key signals and latent communication patterns among all signaling pathways, CellChat uses an unsupervised learning method non-negative matrix factorization that has been successfully applied in pattern recognition,,,. First, the latent patterns were found for sending cells by summarizing the communication probability array P (three-dimensional) along the second dimension to obtain a two-dimensional matrix P j. A non-negative matrix factorization was then carried out via: 4 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\mathrm{min}}_{W,H > 0}\left\| {P_j - WH} \right\|,}$$\end{document} min W, H > 0 P j - W H, where the two low-dimensional matrices W and H are the cell loading a...
Measurement outputs
What raw and processed outputs should exist?
Cell clustering is a pre-requisite for cell-cell communication analysis with CellChat and other tools, such as CellPhoneDB, iTALK and SingleCellSignalR. While different nu...
- Raw artifact
- Per-sample or per-animal endpoint measurements collected during the experiment
- Processed artifact
- Structured table with cleaned measurements ready for comparison
- Reported as
- Summary statistics and between-group or across-timepoint comparisons
To further demonstrate the predictive nature of CellChat, we studied signaling communication between E14.5 dermal condensate (DC) and epithelial placode cells, since these cells...
- Raw artifact
- Per-sample or per-animal endpoint measurements collected during the experiment
- Processed artifact
- Structured table with cleaned measurements ready for comparison
- Reported as
- Summary statistics and between-group or across-timepoint comparisons
Several methods have been recently developed to infer cell-cell communication from scRNA-seq data -, such as SingleCellSignalR, iTALK, and NicheNet. However, the...
- Raw artifact
- Per-sample or per-animal endpoint measurements collected during the experiment
- Processed artifact
- Structured table with cleaned measurements ready for comparison
- Reported as
- Summary statistics and between-group or across-timepoint comparisons
Here we develop CellChat, an open source R package ( https://github.com/sqjin/CellChat ) to infer, visualize and analyze intercellular communications from scRNA-seq data. First,...
- Raw artifact
- Per-sample or per-animal endpoint measurements collected during the experiment
- Processed artifact
- Structured table with cleaned measurements ready for comparison
- Reported as
- Summary statistics and between-group or across-timepoint comparisons
Analysis plan
How should the outputs become interpretable results?
Acquisition
Collect raw experimental outputs with enough metadata to preserve sample identity, condition, and timing.
inferred from protocolPreprocessing / cleaning
To further demonstrate the predictive nature of CellChat, we studied signaling communication between E14.5 dermal condensate (DC) and epithelial placode cells, since these cells spatially colocalize and actively signal to each other during the initial stages of embryonic hair...
from paperScoring or quantification
Quantify the primary readouts for this experiment: Cell clustering is a pre-requisite for cell-cell communication analysis with CellChat and other tools, such as CellPhoneDB, iTALK and SingleCellSignalR. While different nu...; To further demonstrate the predictive nature of CellChat, we studied signaling communication between E14.5 dermal condensate (DC) and epithelial placode cells, since these cells...; Several methods have been recently developed to infer cell-cell communication from scRNA-seq data -, such as SingleCellSignalR, iTALK, and NicheNet. However, the...; Here we develop CellChat, an open source R package ( https://github.com/sqjin/CellChat ) to infer, visualize and analyze intercellular communications from scRNA-seq data. First,....
from paperStatistical comparison
To further demonstrate the predictive nature of CellChat, we studied signaling communication between E14.5 dermal condensate (DC) and epithelial placode cells, since these cells...; Here we develop CellChat, an open source R package ( https://github.com/sqjin/CellChat ) to infer, visualize and analyze intercellular communications from scRNA-seq data. First,...; CellChat requires gene expression data from cells as the user input and models the probability of cell-cell communication by integrating gene expression with prior knowled...; Inference and visualization of intercellular communications. To predict significant communications, CellChat identifies differentially over-expressed ligands and receptors for e...
from paperReporting output
Report representative outputs alongside summary comparisons for Cell clustering is a pre-requisite for cell-cell communication analysis with CellChat and other tools, such as CellPhoneDB, iTALK and SingleCellSignalR. While different nu..., To further demonstrate the predictive nature of CellChat, we studied signaling communication between E14.5 dermal condensate (DC) and epithelial placode cells, since these cells..., Several methods have been recently developed to infer cell-cell communication from scRNA-seq data -, such as SingleCellSignalR, iTALK, and NicheNet. However, the..., Here we develop CellChat, an open source R package ( https://github.com/sqjin/CellChat ) to infer, visualize and analyze intercellular communications from scRNA-seq data. First,....
inferred from protocolStructured statistical methods
To further demonstrate the predictive nature of CellChat, we studied signaling communication between E14.5 dermal condensate (DC) and epithelial placode cells, since these cells...; Here we develop CellChat, an open source R package ( https://github.com/sqjin/CellChat ) to infer, visualize and analyze intercellular communications from scRNA-seq data. First,...; CellChat requires gene expression data from cells as the user input and models the probability of cell-cell communication by integrating gene expression with prior knowled...; Inference and visualization of intercellular communications. To predict significant communications, CellChat identifies differentially over-expressed ligands and receptors for e...
source structuredSource and audit
What supports the facts on this page?
Evidence quotes (3)
Here we develop CellChat, an open source R package ( https://github.com/sqjin/CellChat ) to infer, visualize and analyze intercellular communications from scRNA-seq data. First, we manually curate a comprehensive signaling molecule interaction database that takes into account the known structural composition of ligand-receptor interactions, such as multimeric ligand-receptor complexes, soluble agonists and antagonists, as well as stimulatory and inhibitory membrane-bound co-receptors. Next, CellChat infers cell-state specific signaling communications within a given scRNA-seq data using mass action models, along with differential expression analysis and statistical tests on cell groups, which can be both discrete states or continuous states along the pseudotime cell trajectory. CellChat also provides several visualization outputs to facilitate intuitive user-guided data interpretation. CellChat can quantitatively characterize and compare the inferred intercellular communications through social network analysis tool, pattern recognition methods, and manifold learning approaches. Such analyses enable identification of the specific signaling roles played by each cell population,...
To construct a database of ligand-receptor interactions that comprehensively represents the current state of knowledge, we manually reviewed other publicly available signaling pathway databases, as well as peer-reviewed literature and developed CellChatDB. CellChatDB is a database of literature-supported ligand-receptor interactions in both mouse and human. The majority of ligand-receptor interactions in CellChatDB were manually curated on the basis of KEGG (Kyoto Encyclopedia of Genes and Genomes) signaling pathway database ( https://www.genome.jp/kegg/pathway.html ). Additional signaling molecular interactions were gathered from recent peer-reviewed experimental studies. We took into account not only the structural composition of ligand-receptor interactions, that often involve multimeric receptors, but also cofactor molecules, including soluble agonists and antagonists, as well as co-stimulatory and co-inhibitory membrane-bound receptors that can prominently modulate ligand-receptor mediated signaling events. The detailed steps for how CellChatDB was built and how to update CellChatDB by adding user-defined ligand-receptor pairs were provided in Supplementary Note...
To identify key signals and latent communication patterns among all signaling pathways, CellChat uses an unsupervised learning method non-negative matrix factorization that has been successfully applied in pattern recognition,,,. First, the latent patterns were found for sending cells by summarizing the communication probability array P (three-dimensional) along the second dimension to obtain a two-dimensional matrix P j. A non-negative matrix factorization was then carried out via: 4 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\mathrm{min}}_{W,H > 0}\left\| {P_j - WH} \right\|,}$$\end{document} min W, H > 0 P j - W H, where the two low-dimensional matrices W and H are the cell loading and signaling loading matrices with sizes K × R and R × N, respectively. Each of the R columns in W and the corresponding rows in H is considered as a communication pattern. W ir is the loading values of cell group i in pattern r, representing the contributions...
Machine-readable layer
[
{
"@context": "https://schema.org",
"@type": "HowTo",
"name": "Inference and analysis of cell-cell communication using CellChat methods",
"description": "Evidence-backed execution summary for Inference and analysis of cell-cell communication using CellChat methods from Inference and analysis of cell-cell communication using CellChat.",
"step": [
{
"@type": "HowToStep",
"position": 1,
"name": "Introduction",
"text": "Here we develop CellChat, an open source R package ( https://github.com/sqjin/CellChat ) to infer, visualize and analyze intercellular communications from scRNA-seq data. First, we manually curate a comprehensive signaling molecule interaction database that takes into account the known structural composition of ligand-receptor interactions, such as multimeric ligand-receptor complexes, soluble agonists and antagonists, as well as stimulatory and inhibitory membrane-bound co-receptors. Next, CellChat infers cell-state specific signaling communications within a given scRNA-seq data using mass action models, along with differential expression analysis and statistical tests on cell groups, which can be both discrete states or continuous states along the pseudotime cell trajectory. CellChat also provides several visualization outputs to facilitate intuitive user-guided data interpretation...."
},
{
"@type": "HowToStep",
"position": 2,
"name": "Methods",
"text": "To construct a database of ligand-receptor interactions that comprehensively represents the current state of knowledge, we manually reviewed other publicly available signaling pathway databases, as well as peer-reviewed literature and developed CellChatDB. CellChatDB is a database of literature-supported ligand-receptor interactions in both mouse and human. The majority of ligand-receptor interactions in CellChatDB were manually curated on the basis of KEGG (Kyoto Encyclopedia of Genes and Genomes) signaling pathway database ( https://www.genome.jp/kegg/pathway.html ). Additional signaling molecular interactions were gathered from recent peer-reviewed experimental studies. We took into account not only the structural composition of ligand-receptor interactions, that often involve multimeric receptors, but also cofactor molecules, including soluble agonists and antagonists, as we..."
},
{
"@type": "HowToStep",
"position": 3,
"name": "Identification of major signals for specific cell groups and global communication patterns",
"text": "To identify key signals and latent communication patterns among all signaling pathways, CellChat uses an unsupervised learning method non-negative matrix factorization that has been successfully applied in pattern recognition,,,. First, the latent patterns were found for sending cells by summarizing the communication probability array P (three-dimensional) along the second dimension to obtain a two-dimensional matrix P j. A non-negative matrix factorization was then carried out via: 4 \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\mathrm{min}}_{W,H > 0}\\left\\| {P_j - WH} \\right\\|,}$$\\end{document} min W, H > 0 P j - W H, where the two low-dimensional matrices W and H are the cell loading a..."
}
],
"tool": [
{
"@type": "HowToTool",
"name": "Discussion"
}
],
"supply": [
{
"@type": "HowToSupply",
"name": "Joint learning of time-course scRNA-seq data to uncover dynamic communication patterns"
}
],
"isBasedOn": {
"@type": "ScholarlyArticle",
"headline": "Inference and analysis of cell-cell communication using CellChat",
"datePublished": "2021",
"author": [
{
"@type": "Person",
"name": "Suoqin Jin"
},
{
"@type": "Person",
"name": "Christian F. Guerrero-Juarez"
},
{
"@type": "Person",
"name": "Lihua Zhang"
},
{
"@type": "Person",
"name": "Ivan Chang"
},
{
"@type": "Person",
"name": "Raul Ramos"
},
{
"@type": "Person",
"name": "Chen-Hsiang Kuan"
},
{
"@type": "Person",
"name": "Peggy Myung"
},
{
"@type": "Person",
"name": "Maksim V. Plikus"
},
{
"@type": "Person",
"name": "Qing Nie"
}
],
"identifier": "10.1038/s41467-021-21246-9"
}
},
{
"@context": "https://schema.org",
"@type": "BreadcrumbList",
"itemListElement": [
{
"@type": "ListItem",
"position": 1,
"name": "Experiments",
"item": "https://replicatescience.com/experiments"
},
{
"@type": "ListItem",
"position": 2,
"name": "Inference and analysis of cell-cell communication using CellChat methods",
"item": "https://replicatescience.com/experiments/inference-and-analysis-of-cell-cell-communication-using-cellchat-methods-suoqin-jin-pmc7889871/inference-and-analysis-of-cell-cell-communication-using-cellchat-mlpgyw1q"
}
]
}
]