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Inference of miRNA‐target interactions with positive regulation
Considering the target gene can be positively reguated by the miRNA, we allow users to infer the cell-cell communication mediated by the positively regulated miRNA-target interactions with the parameter regulation = "positive"
in the function find_miRNA
, namely high expression of the miRNA gene in the sender cell type and high expression of the target gene in the receiver cell type. Step-by-step procedures are shown below:
[1] load the example data
> load(paste0(system.file(package = 'miRTalk'), "/extdata/example.rda"))
> dim(sc_data)
[1] 22898 515
> table(sc_celltype)
sc_celltype
Bcell Myeloid Stromal Tcell Tumor
83 38 23 54 317
[2] create miRTalk object using single-cell transcriptomics data
> obj <- create_miRTalk(sc_data = sc_data,
sc_celltype = sc_celltype,
species = "Human",
condition = rep("condition",length(sc_celltype)),
evbiog = evbiog,
risc = risc,
ritac = ritac)
Warning: The following features are not present in the object: AGO2, not searching for symbol synonyms
[3] Find highly variable target genes with DEGs and HVGs with find_hvtg
> obj <- find_hvtg(object = obj)
[4] Find expressed miRNAs among all cells and generate background distribution for permutation test with find_miRNA
. Set the parameters database
as TarBase
and regulation
as negative
for the inference of negatively regulated miRNA-target interactions. We use ritac
for positive regulation
> obj <- find_miRNA(object = obj,
mir_info = mir_info,
mir2tar = mir2tar,
database = "TarBase",
regulation = "positive")
[5] Infer cell-cell communication mediated by EV-derived miRNAs from senders to receivers
> obj <- find_miRTalk(obj, if_doParallel = F)
[condition]
[++++++++++++++++++++++++++++++] Finished:100% time:00:04:57
> obj
An object of class miRTalk
0 EV-derived miRNA-target interactions
In this dataset, no positively regulated miRNA-target interactions found. Try it with your own dataset.