Identify dysfunctional signaling by using differential expression analysis

[tumorscholar]

Hello Jin,
Thank you very much for creating this nice tool.
I have used the below-written script and generated net file for the interaction pair.

#Identify dysfunctional signaling by using differential expression analysis
#define a positive dataset, i.e., the dataset with positive fold change against the other dataset
pos.dataset = "Fibrosis"
# define a char name used for storing the results of differential expression analysis
features.name = paste0(pos.dataset, ".merged")

cellchat <- identifyOverExpressedGenes(cellchat, group.dataset = "datasets", pos.dataset = pos.dataset, features.name = features.name, only.pos = FALSE, thresh.pc = 0.1, thresh.fc = 0.05,thresh.p = 0.05, group.DE.combined = FALSE) 

# map the results of differential expression analysis onto the inferred cell-cell communications to easily manage/subset the ligand-receptor pairs of interest
net <- netMappingDEG(cellchat, features.name = features.name, variable.all = TRUE)
write.csv(net, "/data/home/hdx044/files/cellchat/LIVER/InteractionInLIVERfibrosis.csv")

Some interaction pairs like MIF-associated MIF - (CD74+CXCR4) or MIF - (CD74+CD44) miss the value associated with the MIF ligand.

Could you please give the possible reasons for this?

Thank you
Regards,
Raju