Hey people,

I asked this question also on the CellChat GitHub forum, but since things there haven't been answered for months, I'm skeptical about getting an answer there, hence I'm posting here too:

Anyway, I was wondering if anoyone knows: using CellChat (v2.2), when comparing between two groups (WT vs. KO,), each of which comprised of 4-5 mice, is there a way to find out if a specific pathway is enriched in one group (compared to the other) in a specific source cell type and a specific target cell type in a statistically-significant manner, based on the individual mice (comparing e.g. 4 values for "WT" [4 mice] and 4 values for "KO", thus being able to run a statistical test)?

I'll try to explain it by example, using, in this case, the "rankNet" function - but I am totally fine with any other function if it can help.When I run:

gg1 <- rankNet(cellchat, 
               mode = "comparison", 
               signaling = "COLLAGEN",
               sources.use = "FIB1",
               targets.use = "FIB2",
               do.stat = TRUE,
               return.data = TRUE)

The p-values (in "gg1[["signaling.contribution"]]$pvalues") will always be "0" (or completely absent), no matter which source, target and pathway are specified. But that, to my understanding, is because the source code for "rankNet" forces a "pvalue" of "0" here, because there are only 2 "prob.values" (one for KO, one for WT). However, as I've mentioned, my WT and KO groups consist of 4-5 mice each. Is there a way to leverage that fact to be able to find out if a specified pathway is substantially changed between the WT and KO groups in a specified source and a specified target?

Not a Bioconductor package, I commented on the crosspost at https://www.biostars.org/p/9617271/