sqjin
commented
8 months ago @Dena2021 Two issues were observed: 1) You should use the normalized data, which should be not the count data. In your seurat object, the data slot is the same as the count slot. If you use the sctransform, you can set data.input <- GetAssayData(seurat_object_con, assay = "SCT", slot = "data")
2) You cell group label include 0, which is not allowed in CellChat.
Hi Sqjin, Thanks for providing this package. I am trying to compare two scRNA samples that were analysed together in Seurat and then separated by subset to extract each sample and analyse it separately with cellchat, and then compare between them. The exact pipeline works fine with one sample, but with the other the sample, the netP is empty after running computeCommunProb. Interestingly, the command subsetCommunication(cellchat_c,slot.name = "netP") works fine (see outputs) Yet command computeCommunProbPathway gives an error.
Any advise?
See code-
data.input <- GetAssayData(seurat_object_con, assay = "RNA", slot = "data") # normalized data matrix labels <- Idents(seurat_object_con) meta <- data.frame(group = labels, row.names = names(labels)) # create a dataframe of the cell labels
CellChatDB <- CellChatDB.mouse # use CellChatDB.mouse if running on mouse data showDatabaseCategory(CellChatDB)
Show the structure of the database
dplyr::glimpse(CellChatDB$interaction)
use a subset of CellChatDB for cell-cell communication analysis
CellChatDB.use <- subsetDB(CellChatDB, search = "Secreted Signaling", key = "annotation") # use Secreted Signaling
cellchat_c <- createCellChat(object = data.input, meta = meta, group.by = "group") cellchat_c <- addMeta(cellchat_c, meta = meta) cellchat_c <- setIdent(cellchat_c, ident.use = "group") # set "group" as default cell identity levels(cellchat_c@idents) # show factor levels of the cell labels groupSize <- as.numeric(table(cellchat_c@idents)) # number of cells in each cell group cellchat_c@DB <- CellChatDB.use
subset the expression data of signaling genes for saving computation cost
cellchat_c <- subsetData(cellchat_c) cellchat_c <- identifyOverExpressedGenes(cellchat_c) cellchat_c <- identifyOverExpressedInteractions(cellchat_c)
cellchat_c <- computeCommunProb(cellchat_c)
Filter out the cell-cell communication if there are only few number of cells in certain cell groups
cellchat_c <- filterCommunication(cellchat_c, min.cells = 20)
c.df.net <- subsetCommunication(cellchat_c) #returns a data frame consisting of all the inferred cell-cell communications at the level of ligands/receptors. Set slot.name = "netP" to access the the inferred communications at the level of signaling pathways
c.df.net.np <- subsetCommunication(cellchat_c,slot.name = "netP")
rio::export(c.df.net, file = "Con_Inferred_cell_cell_comm.xlsx", sep = "\t", rowNames = TRUE, colNames=TRUE) rio::export(c.df.net.np, file = "Con_Inferred_sign_pthways_comm.xlsx", sep = "\t", rowNames = TRUE, colNames=TRUE)
cellchat_c <- computeCommunProbPathway(cellchat_c) Error in prob.pathways[, , pathways.sig, drop = FALSE] : subscript out of bounds
Con_Inferred_sign_pthways_comm.xlsx Con_Inferred_cell_cell_comm.xlsx sessioninfo.txt str_cellchat_c.txt