CellChat is now applicable to spatial imaging data. We showcase its application to 10X Visium data. When spatial locations of spots/cells are available, CellChat infers spatial-informed cell-cell communication between interacting cell groups. CellChat restricts cell-cell communication within the maximum interaction/diffusion length of molecules.
A brief tutorial for spatial imaging data analysis is available in the tutorial directory. CellChat's various functionality can be used for further data exploration, analysis, and visualization.
We have redesigned the structure of CellChat object. When loading previous CellChat object (version < 1.6.0), please update the object via updateCellChat
.
Functions that have been updated for analyzing spatial imaging data, such as CellChat-class
, createCellChat
, updateCellChat
, computeCommunProb
, netVisual
, netVisual_aggregate
, netVisual_individual
netVisual_spatial
, computeRegionDistance
We have now presented our comparison framework for systematically detecting dysregulated cell-cell communication across biological conditions, and then utilized it to study the aging-induced signaling changes during skin wound healing. Our results not only present general communication rules and signaling mechanisms in wound healing associated with aging, but also provide a paradigm for other researchers to study cell-cell communication in other contexts. Please check out our paper (Vu#, Jin#, Sun# et al., Cell Reports, 2022) for the detailed methods and applications.
A number of functions have been updated with enhanced functionalities, such as rankNet
, netVisual_aggregate
, netVisual_circle
, 'plotGeneExpression', netVisual_embedding
.
computeEnrichmentScore
, 'netVisual_barplot', 'barPlot'
netAnalysis_computeCentrality
, netVisual_embeddingPairwise
, netAnalysis_signalingChanges_scatter
NB: The method for computing the 'influencer' metric in the function netAnalysis_computeCentrality
has been changed due to the issue that "lower" mode gives different results when re-ordering the matrix. The results changed, but it looks like the dominant patterns do not change, i.e., the top cell groups ranked based on this metrix retain the same.
netAnalysis_diff_signalingRole_scatter
for 2D visualization of differential signaling roles of each cell group when comparing mutiple datasets.netVisual
, netVisual_aggregate
, netVisual_individual
, netVisual_hierarchy1
, netVisual_hierarchy2
,netVisual_circle
,createCellChat
,netAnalysis_computeCentrality
,netAnalysis_signalingRole_scatter
, netAnalysis_signalingChanges_scatter
Add thresh = 0.05
in netAnalysis_computeCentrality
to only consider the significant interactions. This will slightly change (very likely quantitative instead of qualitative change) the results computed by previous version of CellChat.
In the updated netAnalysis_computeCentrality
, we now also compute unweighted outdegree (i.e., the total number of outgoing links) and indegree (i.e., the total number of incoming links).
netAnalysis_signalingRole_scatter
and netAnalysis_signalingChanges_scatter
now support the comparison of the total number of outgoing and incoming links.
Change the default setting for visualizing cell-cell communication network: 1) using circle
plot instead of hierarchy
; 2) using the same node size instead of different size (setting vertex.weight = NULL
will give different size as in previous version of CellChat).
updateClusterLabels
to update cell cluster labels without re-running the time-consuming function computeCommunProb
.netAnalysis_signalingRole_scatter
, setIdent
, netVisual_diffInteraction
, sketchData
, identifyOverExpressedGenes
,netEmbedding
,runUMAP
subsetCellChat
to create a object using a portion of cellscomputeAveExpr
to compute the average expression per cell groupsketchData
to downsample the single cell data for faster calculationnetAnalysis_signalingChanges_scatter
to identify the signaling changes associated with one cell groupcomputeCommunProb
now supports a faster calculation and displays a ProgressBarcomputeCommunProbPathway
now returns the significant pathways that are ordered based on the total communication probabilitiesupdateCellChat()
)help()
to check the documentation, e.g., help(CellChat)
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