We can have a situation where ligand/receptor expression is low, but target genes (repressors and activators) are highly expressed. In this case we will get a false positive for \(P_{i,j}\). In order to correct for this, we introduce normalizing coefficients for the relationship between \(\alpha_{i,j}\) and \(\beta / \gamma\). When \(\alpha\) is low, then K and D will decrease rapidly with increasing \(\beta\) and \(\gamma\), penalizing the resulting increase in P (which is increasingly likely to be a false positive, given the disparity between \(\alpha\) and \(\beta\),\(\gamma\)) \(K_{i,j} = \frac{\alpha_{i,j}}{\alpha_{i,j} + \beta_{i,j}}\) Note that activated genes are required under this model.
GetSignalingPartners(data, ids, pathway, normalize_aggregate = TRUE)
| data | a matrix of expression values for each cell (rows) and gene (columns) |
|---|---|
| ids | a vector of gene ids |
| pathway | a data frame with "ligands", "receptors", "direction", and "targets" |
| normalize_aggregate | whether or not to normalize the P_agg matrix by dividing each row by its sum default is true |
a list containing:
a list of the cell-cell signaling probabilities for all ligand/receptor pairs
the aggregate matrix of all ligand/receptor pairs