Differential expression analysis reports same pair L-R as both downregulated and upregulated ???

[TorteloteLab]

Hi @sqjin Congrats on this this great library.

I am running some of the functions on my datasets and I came across an interesting observation. After DGE analysis, I am finding the same pair of LR both downregulated and upregulated, as shown in the fig below. How would it be possible?

Thus, I am a bit confuse on how to interpret this observations. Here is the code I used to generate the chart.

The idea is to compare two conditions NPD (control) and LPD LPD should downregulate genes, therefore, NPD will be chosen as positive dateset pos.dataset = "NPD"

features.name = pos.dataset

cellchat <- identifyOverExpressedGenes(cellchat, group.dataset = "datasets", pos.dataset = pos.dataset, features.name = features.name, only.pos = FALSE, thresh.pc = 0.2, thresh.fc = 0.2, thresh.p = 0.05)

net <- netMappingDEG(cellchat, features.name = features.name)

net.up <- subsetCommunication(cellchat, net = net, datasets = "NPD",ligand.logFC = 0.2, receptor.logFC = NULL)

net.down <- subsetCommunication(cellchat, net = net, datasets = "LPD",ligand.logFC = -0.1, receptor.logFC = -0.1)

gene.up <- extractGeneSubsetFromPair(net.up, cellchat)

gene.down <- extractGeneSubsetFromPair(net.down, cellchat)

pairLR.use.up = net.up[, "interaction_name", drop = T]

gg1 <- netVisual_bubble(cellchat, pairLR.use = pairLR.use.up, sources.use = 3, targets.use = c(3), comparison = c(1, 2), angle.x = 45, remove.isolate = T,title.name = paste0("Up-regulated signaling in ", names(object.list)[1]))

pairLR.use.down = net.down[, "interaction_name", drop = T]

gg2 <- netVisual_bubble(cellchat, pairLR.use = pairLR.use.down, sources.use = c(3), targets.use = c(3), comparison = c(1, 2), angle.x = 45, remove.isolate = T,title.name = paste0("Down-regulated signaling in ", names(object.list)[1]))

gg1 + gg2

help is greatly appreciated

[tilofrei]

Hey @giovanegt, for me it helped to add sources.use and targets.use as a parameter to subsetCommunication(). Otherwise only certain groups are visualized but interactions from all groups are included. Furthermore you could tweak the parameters ligand.logFC and receptor.logFC to limit or expand results.

[Tjcyz]

for me it helped to add sources.use and targets.use as a parameter to subsetCommunication(). Otherwise only certain groups are visualized but interactions from all groups are included. Furthermore you could tweak the parameters ligand.logFC and receptor.logFC to limit or expand results.

I have the same problem now, how did you solve the issue?