Missing MHC interactions when they are expected

[dn-ra]

Hi,

Thank you for writing and maintaining this useful tool.

I'm performing cell interaction analysis on a dataset of DCs and T cells. I've output a list of interactions from a DC celltype to a T celltype and with a LRscore threshold of 0.3 as below:

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cDC1	T0	interaction type	LRscore
Gnai2	S1pr1	paracrine	0.79762
Calm1	Pde1c	paracrine	0.489527
Tgm2	Adgrg1	paracrine	0.455561
Spp1	S1pr1	paracrine	0.403408
Calm1	Oprm1	paracrine	0.39834
Gnai2	Oprm1	paracrine	0.358413
Calm3	Pde1c	paracrine	0.348447
Pdgfb	S1pr1	paracrine	0.341336
Calm2	Pde1c	paracrine	0.333758

Trouble is, there should be some MHC interactions present in here. Even when I reduce the LRscore down to 0.05, the expected interactions still don't show up.

I should note that I have input my own cluster labels into the analysis rather than using the data_prepare() pipeline as we had to perform some integration.
What might be causing the drop out of expected interactions? Can inputting our own cluster labels cause this?

An example for the expression of one of the expected ligand-receptor pairings is present in the below plot. Clearly the involved molecules are present in the dataset. So what might I be doing wrong so that it isn't picked up in the analysis?

Here's the code I used to run SingleCellSignalR:

clust.ana <- cluster_analysis(data = seu_split[['LN']]@assays$RNA@data, genes = rownames(seu_split[['LN']]), 
                              cluster = seu_split[['LN']]$refined_cluster %>% as.factor() %>% as.numeric(), 
                              write = FALSE) #differentially expressed genes separating clusters. This actually isn't necessary. Jsut tells you if the interactions between two cell types are specific to that pairing.

signal <- cell_signaling(data =  seu_split[['LN']]@assays$RNA@data %>% data.frame(), 
                         genes = rownames(seu_split[['LN']]), cluster = seu_split[['LN']]$refined_cluster %>% as.factor() %>% as.numeric(), 
                         write = FALSE, species = 'mus musculus', s.score = 0.05,
                         c.names = seu_split[['LN']]$refined_cluster %>% as.factor %>% levels()) #generate cellular interaction lists

inter.net <- inter_network(data = seu_split[['LN']]@assays$RNA@data, signal = signal, genes = genes, 
                           cluster = seu_split[['LN']]$refined_cluster %>% as.factor() %>% as.numeric(), 
                           write = FALSE) #generate intercellular network

Thanks for your advice.