error when calling identifyOverExpressedGenes function

[idupanloup]

I get an error when calling the identifyOverExpressedGenes function.
Here's my code:

object.list <- list(db1 = db1.cellchat, db2 = db2.cellchat, db3 = db3.cellchat, db4 = db4.cellchat)
merged.cellchat <- mergeCellChat(object.list, cell.prefix = TRUE)
Merge the following slots: 'data.signaling','net', 'netP','meta', 'idents', 'var.features' , 'DB', and 'LR'.
Warning message:
In mergeCellChat(object.list, cell.prefix = TRUE) : Prefix cell names!
CellChatDB.use <- CellChatDB
merged.cellchat@DB <- CellChatDB.use
merged.cellchat <- subsetData(merged.cellchat)
Issue identified!! Please check the official Gene Symbol of the following genes:
H2-Q8 H2-T9 H2-T18 H2-Q9 H2-L H2-BI H2-D H60a H2-Ea-ps
merged.cellchat <- identifyOverExpressedGenes(merged.cellchat)
Use the joint cell labels from the merged CellChat object
Error in h(simpleError(msg, call)) :

Many thanks for your feedback !

[sqjin]

@idupanloup For the merged cellchat object, you cannot run subsetData and identifyOverExpressedGenes. If you want to compare signaling networks across different datasets, you need to first run cellchat on each dataset and then follow the tutorial on the comparison analysis.

[idupanloup]

@sqjin I ran CellChat on the 2 datasets and now get a new error message:

db1

data.input <- GetAssayData(db1.Seurat, assay = "RNA", slot = "data") # normalized data matrix
labels <- Idents(db1.Seurat)
meta <- data.frame(labels = labels, row.names = names(labels))
db1.cellchat <- createCellChat(object = data.input, meta = meta, group.by = "labels")
rm(db1.Seurat)

db1.cellchat@DB <- CellChatDB.use

db1.cellchat <- subsetData(db1.cellchat)
db1.cellchat <- identifyOverExpressedGenes(db1.cellchat)
db1.cellchat <- identifyOverExpressedInteractions(db1.cellchat)
db1.cellchat <- projectData(db1.cellchat, PPI.mouse)

db1.cellchat <- computeCommunProb(db1.cellchat, raw.use = TRUE)

Filter out the cell-cell communication if there are only few number of cells in certain cell groups

db1.cellchat <- filterCommunication(db1.cellchat, min.cells = 10)

db1.cellchat <- computeCommunProbPathway(db1.cellchat)
db1.cellchat <- aggregateNet(db1.cellchat)

Compute the network centrality scores

db1.cellchat <- netAnalysis_computeCentrality(db1.cellchat, slot.name = "netP")

db2

data.input <- GetAssayData(db2.Seurat, assay = "RNA", slot = "data") # normalized data matrix
labels <- Idents(db2.Seurat)
meta <- data.frame(labels = labels, row.names = names(labels))
db2.cellchat <- createCellChat(object = data.input, meta = meta, group.by = "labels")
rm(db2.Seurat)

db2.cellchat@DB <- CellChatDB.use

db2.cellchat <- subsetData(db2.cellchat)
db2.cellchat <- identifyOverExpressedGenes(db2.cellchat)
db2.cellchat <- identifyOverExpressedInteractions(db2.cellchat)
db2.cellchat <- projectData(db2.cellchat, PPI.mouse)

db2.cellchat <- computeCommunProb(db2.cellchat, raw.use = TRUE)

Filter out the cell-cell communication if there are only few number of cells in certain cell groups

db2.cellchat <- filterCommunication(db2.cellchat, min.cells = 10)

db2.cellchat <- computeCommunProbPathway(db2.cellchat)
db2.cellchat <- aggregateNet(db2.cellchat)

Compute the network centrality scores

db2.cellchat <- netAnalysis_computeCentrality(db2.cellchat, slot.name = "netP")

merged.cellchat

object.list <- list(db1 = db1.cellchat, db2 = db2.cellchat)
merged.cellchat <- mergeCellChat(object.list, add.names = names(object.list), cell.prefix = TRUE)

Merge the following slots: 'data.signaling','net', 'netP','meta', 'idents', 'var.features' , 'DB', and 'LR'.

merged.cellchat@DB <- CellChatDB.use

gg1 <- compareInteractions(merged.cellchat, show.legend = F, group = c(1,2))
gg2 <- compareInteractions(merged.cellchat, show.legend = F, group = c(1,2), measure = "weight")
gg1 + gg2

netVisual_diffInteraction(merged.cellchat, weight.scale = T)
netVisual_diffInteraction(merged.cellchat, weight.scale = T, measure = "weight")

Error in obj2 - obj1 : non-conformable arrays

Do you know what can be the problem ?
Many thanks for your help and valuable feedback !

[sqjin]

@idupanloup Sorry. I missed your information. I guess the reason is that you have different cell groups between the two datasets. If so , please check the third tutorial.