The study of cell-cell communication primarily investigates the mechanisms by which a cell transmits and receives signals from its environment, as well as within itself.
scRNA-seq data inherently encompasses gene expression information, which can be leveraged to deduce these intercellular communications.
The first step for cell-cell communication (CCC) analysis is cell types annotation with SingleR. After that, interested cell types are selected for CCC analysis.
In this report, four cell types including B cells, Monocyte, NK Cells and T cells were chosen for this cell-cell communication inference analysis.
The UMAP plots show the result of this step with cell type annotation
The UMAP plots show the result of this step with cell type annotation
The UMAP plots show the result of this step with cell type annotation
CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches.
CellChat infers the biologically significant cell-cell communication by assigning each interaction with a probability value and peforming a permutation test. CellChat models the probability of cell-cell communication by integrating gene expression with prior known knowledge of the interactions between signaling ligands, receptors and their cofactors using the law of mass action.
CellChat starts with the big picture to predict general principles of cell-cell communication. When comparing cell-cell communication among multiple biological conditions, it can answer the following biological questions:
Whether the cell-cell communication is enhanced or not
The interaction between which cell types is significantly changed
How the major sources and targets change from one condition to another
Bar plots shows the number of interactions or the total interaction strength (weights) amongs groups.
Circle plots shows the number of interactions of three groups.
Circle plots shows the number of interactions or the total interaction strength (weights) between two groups.
Circle plots shows the number of interactions or the total interaction strength (weights) between two groups.
Table 1: The table shows all the inferred cell-cell communications at the level of ligands and receptors
The Number of Interactions refers to the count or frequency of interactions between two cell groups.In the circle plot, the thickness or darkness of the lines or arcs connecting the cell groups represents the number of interactions between them. A thicker or darker line indicates a higher number of interactions between the corresponding cell groups.
The Total Interaction Strength or “Weights” refers to the cumulative strength or intensity of interactions between two cell groups. It takes into account various factors such as ligand-receptor affinities, signal intensities, and other quantitative measures. In the circle plot, the thickness or darkness of the lines or arcs connecting the cell groups represents the total interaction strength between them. A thicker or darker line indicates a stronger overall interaction between the corresponding cell groups.
Circle plots shows the number of interactions or the total interaction strength (weights) between any two cell groups.
Circle plots shows the number of interactions or the total interaction strength (weights) between any two cell groups.
Circle plots shows the number of interactions or the total interaction strength (weights) between any two cell groups.
Circle plots shows the total interaction strength (weights) between each cell group to others.
Circle plots shows the total interaction strength (weights) between each cell group to others.
Circle plots shows the total interaction strength (weights) between each cell group to others.
Circle plots shows the total interaction strength (weights) between each cell group to others.
Circle plots shows the total interaction strength (weights) between each cell group to others.
Circle plots shows the total interaction strength (weights) between each cell group to others.
Circle plots shows the total interaction strength (weights) between each cell group to others.
Circle plots shows the total interaction strength (weights) between each cell group to others.
Circle plots shows the total interaction strength (weights) between each cell group to others.
Circle plots shows the total interaction strength (weights) between each cell group to others.
Circle plots shows the total interaction strength (weights) between each cell group to others.
Circle plots shows the total interaction strength (weights) between each cell group to others.
Chord diagram visualized the cell-cell communication between different cell groups.
Hierarchy plot provides an informative and intuitive way to visualize autocrine and paracrine signaling communications between cell groups of interest.
Circle plot visualizes the inferred communication network of signaling pathways.
Heatmap shows the relative importance of each cell group based on the computed four network centrality measures.
Barplot shows relative contribution of each ligand-receptor pair to the overall communication network .
Chord diagram visualized the cell-cell communication between different cell groups.
Hierarchy plot provides an informative and intuitive way to visualize autocrine and paracrine signaling communications between cell groups of interest.
Circle plot visualizes the inferred communication network of signaling pathways.
Heatmap shows the relative importance of each cell group based on the computed four network centrality measures.
Barplot shows relative contribution of each ligand-receptor pair to the overall communication network .
Chord diagram visualized the cell-cell communication between different cell groups.
Hierarchy plot provides an informative and intuitive way to visualize autocrine and paracrine signaling communications between cell groups of interest.
Circle plot visualizes the inferred communication network of signaling pathways.
Heatmap shows the relative importance of each cell group based on the computed four network centrality measures.
Barplot shows relative contribution of each ligand-receptor pair to the overall communication network .
Chord diagram visualized the cell-cell communication between different cell groups.
Hierarchy plot provides an informative and intuitive way to visualize autocrine and paracrine signaling communications between cell groups of interest.
Circle plot visualizes the inferred communication network of signaling pathways.
Heatmap shows the relative importance of each cell group based on the computed four network centrality measures.
Barplot shows relative contribution of each ligand-receptor pair to the overall communication network .
Chord diagram visualized the cell-cell communication between different cell groups.
Hierarchy plot provides an informative and intuitive way to visualize autocrine and paracrine signaling communications between cell groups of interest.
Circle plot visualizes the inferred communication network of signaling pathways.
Heatmap shows the relative importance of each cell group based on the computed four network centrality measures.
Barplot shows relative contribution of each ligand-receptor pair to the overall communication network .
Chord diagram visualized the cell-cell communication between different cell groups.
Hierarchy plot provides an informative and intuitive way to visualize autocrine and paracrine signaling communications between cell groups of interest.
Circle plot visualizes the inferred communication network of signaling pathways.
Heatmap shows the relative importance of each cell group based on the computed four network centrality measures.
Barplot shows relative contribution of each ligand-receptor pair to the overall communication network .
Chord diagram visualized the cell-cell communication between different cell groups.
Hierarchy plot provides an informative and intuitive way to visualize autocrine and paracrine signaling communications between cell groups of interest.
Circle plot visualizes the inferred communication network of signaling pathways.
Heatmap shows the relative importance of each cell group based on the computed four network centrality measures.
Barplot shows relative contribution of each ligand-receptor pair to the overall communication network .
Chord diagram visualized the cell-cell communication between different cell groups.
Hierarchy plot provides an informative and intuitive way to visualize autocrine and paracrine signaling communications between cell groups of interest.
Circle plot visualizes the inferred communication network of signaling pathways.
Heatmap shows the relative importance of each cell group based on the computed four network centrality measures.
Barplot shows relative contribution of each ligand-receptor pair to the overall communication network .
Chord diagram visualized the cell-cell communication between different cell groups.
Hierarchy plot provides an informative and intuitive way to visualize autocrine and paracrine signaling communications between cell groups of interest.
Circle plot visualizes the inferred communication network of signaling pathways.
Heatmap shows the relative importance of each cell group based on the computed four network centrality measures.
Barplot shows relative contribution of each ligand-receptor pair to the overall communication network .
Outgoing patterns reveal how the sender cells (i.e. cells as signal source) coordinate with each other as well as how they coordinate with certain signaling pathways to drive communication.
Incoming patterns show how the target cells (i.e. cells as signal receivers) coordinate with each other as well as how they coordinate with certain signaling pathways to respond to incoming signals.
The inferred outgoing communication patterns of secreting cells, which shows the correspondence between the inferred latent patterns and cell groups, as well as signaling pathways.
The inferred incoming communication patterns of secreting cells, which shows the correspondence between the inferred latent patterns and cell groups, as well as signaling pathways.
The inferred outgoing communication patterns of secreting cells, which shows the correspondence between the inferred latent patterns and cell groups, as well as signaling pathways.
The inferred incoming communication patterns of secreting cells, which shows the correspondence between the inferred latent patterns and cell groups, as well as signaling pathways.
The inferred outgoing communication patterns of secreting cells, which shows the correspondence between the inferred latent patterns and cell groups, as well as signaling pathways.
The inferred incoming communication patterns of secreting cells, which shows the correspondence between the inferred latent patterns and cell groups, as well as signaling pathways.
| Analysis | Software | Version |
|---|---|---|
| Cell–Cell Communication Inference | CellChat | 1.6.1 |
| Cell Type Annotation | SingleR | 1.8.1 |