Inference with custom data provided by users

We have made all data used in miRTalk inference as the system data, in order to allow that users can use their own data for the inference.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. Users can use their own evbiog and risc. See /data page for detailed information. Keep the data type and colnames consistent with the system data.frame when using your own evbiog_custom and risc_custom

> colnames(evbiog)
[1] "species" "gene"
> colnames(risc)
[1] "species" "gene"

> obj <- create_miRTalk(sc_data = sc_data,
                        sc_celltype = sc_celltype,
                        species = "Human",
                        condition = rep("condition",length(sc_celltype)),
                        evbiog = evbiog_custom,
                        risc = risc_custom)

[3] Find highly variable target genes with DEGs and HVGs with find_hvtg. Users can replace these genes stored in obj@data[["var_genes"]] with other genes, for example, var_genes_custom, a character containing genes. Please run find_hvtg before replacing

> obj <- find_hvtg(object = obj)
> str(obj@data[["var_genes"]])
 chr [1:3029] "MGP" "LUM" "DCD" "DCN" "IGKV2D-28" "IGKV3-11" "SPINK1" ...

> obj@data[["var_genes"]] <- var_genes_custom

[4] Find expressed miRNAs among all cells and generate background distribution for permutation test with find_miRNA. Users can use their own mir_info and mir2tar. See /data page for detailed information. Keep the data type and colnames consistent with the system data.frame when using your own mir_info_custom and mir2tar_custom

> colnames(mir_info)
 [1] "miRNA"             "miRNA_mature"      "gene"             
 [4] "species"           "EV_evidence"       "tissue_TarBase"   
 [7] "celltype_TarBase"  "seq"               "circulating_miRNA"
[10] "avg_rpm"
> colnames(mir2tar)
[1] "miRNA"        "source"       "miRNA_mature" "target_gene" 
[5] "species"      "experiments"  "support"      "pmid"        
[9] "regulation"

> obj <- find_miRNA(object = obj,
                    mir_info = mir_info_custom,
                    mir2tar = mir2tar_custom)

[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

[6] Analysis of functional annotations for a miRNA and its target gene, for example, overlapped/shared pathways. Users can use their own gene2path and mir2path. See /data page for detailed information. Keep the data type and colnames consistent with the system data.frame when using your own gene2path_custom and mir2path_custom

> colnames(gene2path)
[1] "term"    "gene"    "type"    "species" "source" 
> colnames(mir2path)
[1] "term"   "mir"    "type"   "source"

> res_pathway <- get_miRTalk_pathway(object = obj,
                                     gene2path = gene2path_custom,
                                     mir2path = mir2path_custom,
                                     miRNA = "hsa-miR-133a-3p",
                                     targetgenes = "ANXA2")