miRTalk tutorial

Pre-processing

`rev_gene()`

Revise genes with rev_gene(). For scRNA-seq data, we suggest to revise the gene symbols with rev_gene(). geneinfo is the system data.frame containing the information of human and mouse from NCBI gene(updated in June. 19, 2022). To use your own geneinfo data.frame, please refer to demo_geneinfo() to build a new one, e.g., rat, zebrafish, Drosophila, C. elegans, etc.

Parameters of rev_gene() see below:

data A data.frame or matrix or dgCMatrix
data_type A character to define the type of data, select ‘count’ for the data matrix, ‘mir_info’ for the mir_info data.frame, ‘mir2tar’ for the mir2tar data.frame, ‘pathways’ for the pathways data.frame, ‘gene2go’ for the gene2go data.frame
species Species of the data.’Human’, ‘Mouse’ or ‘Rat’
geneinfo A data.frame of the system data containing gene symbols of ‘Human’, ‘Mouse’ and ‘Rat’ updated on June 19, 2022 for revising gene symbols

library(miRTalk)
#> Loading required package: doParallel
#> Loading required package: foreach
#> Loading required package: iterators
#> Loading required package: parallel
#> The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
#> which was just loaded, will retire in October 2023.
#> Please refer to R-spatial evolution reports for details, especially
#> https://r-spatial.org/r/2023/05/15/evolution4.html.
#> It may be desirable to make the sf package available;
#> package maintainers should consider adding sf to Suggests:.
#> The sp package is now running under evolution status 2
#>      (status 2 uses the sf package in place of rgdal)
#> Warning: replacing previous import 'Seurat::JS' by 'networkD3::JS' when loading
#> 'miRTalk'
load(paste0(system.file(package = "miRTalk"), "/extdata/example.rda"))

# demo_geneinfo
demo_geneinfo()
#>   symbol synonyms species
#> 1   A1BG      A1B   Human
#> 2   A1BG      ABG   Human
#> 3   A1BG      GAB   Human
#> 4   A1BG HYST2477   Human
#> 5    A2M     A2MD   Human
#> 6    A2M   CPAMD5   Human

# demo_sc_data
demo_sc_data()
#> 6 x 6 sparse Matrix of class "dgCMatrix"
#>      cell1 cell2 cell3 cell4 cell5 cell6
#> A1BG     4     .     .    18    37     .
#> A2M     42     .    32     .     8    50
#> A2MP     .     5    13    20     .     .
#> NAT1    48     .     .     .     .     7
#> NAT2     .    34    18     .    42     .
#> NATP     4     .     .     .     .     .

# revise gene symbols
sc_data <- rev_gene(data = sc_data,data_type = "count",species = "Human",geneinfo = geneinfo)

Processing

`create_miRTalk()`

Create miRTalk object with create_miRTalk(). Users need to provide the raw count data and the cell type for create_miRTalk(). miRTalk will use NormalizeData() in Seurat to normalize data by default. If your data has been normalized by other methods, you can set the parameter if_normalize = FALSE. Users need to define the condition with condition, provide the signatures of extracellular vesicle biogenesis genes with evbiog, and provide the signatures of RNA-induced silencing complex (RISC) related genes with risc. The default is to use the system data. Detailed system data see the GitHub readme

Parameters of create_miRTalk() see below:

sc_data A data.frame or matrix or dgCMatrix containing raw counts of single-cell RNA-seq data
sc_celltype A character containing the cell type of the single-cell RNA-seq data
species A character meaning species of the single-cell transcriptomics data. “Human”, “Mouse”, “Rat”
condition A character with the same length as the number of cells, e.g., control/disease/treatment, phase 1/2/3, men/women.
if_normalize Normalize sc_data with Seurat LogNormalize()
evbiog A data.frame of the system data containing extracellular vesicle biogenesis genes of “Human”, “Mouse”, “Rat”.
risc A data.frame of the system data containing RNA-induced silencing complex related genes of “Human”, “Mouse”, “Rat”.
ritac A data.frame of the system data containing RNA-induced transcriptional activation complex related genes of “Human”, “Mouse”, “Rat”.

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

#> Warning: The following features are not present in the object: AGO2, not
#> searching for symbol synonyms
obj
#> An object of class miRTalk 
#> 0 EV-derived miRNA-target interactions

# If your data has been normalized by other methods, you can set the parameter `if_normalize = FALSE`
# You can use your own evbiog, risc, and ritac gene signatures.

`find_hvtg()`

Find highly variable target genes with DEGs and HVGs find_hvtg(). miRTalk uses the top 3000 HVGs and DEGs for each cell types with Seurat methods. The result is a character stored obj@data$var_genes. Users can use their own methods to find Find highly variable target genes and replace them.

Parameters of find_hvtg() see below:

object miRTalk object after create_miRTalk()
pvalue Cutoff of p value. Default is 0.05
log2fc log2 fold change for identifying the highly expressed genes in each cell type. Default is 0.5
min_cell_num Min cell number for each cell type. Default is 10
nfeatures Number of features to select as top variable features. Default is 3000

# find highly variable target genes
obj <- find_hvtg(object = obj)

# the result is a character
var_genes <- obj@data$var_genes
str(var_genes)
#>  chr [1:3029] "MGP" "LUM" "DCD" "DCN" "IGKV2D-28" "IGKV3-11" "SPINK1" ...

# if your have your own result, you can replace them with the following code

# object@data$var_genes <- your_own_var_genes

`find_miRNA()`

Find expressed miRNAs among all cells with find_miRNA(). Users need to provided EV-derived miRNAs and miRNA-target interactions (MiTIs) databases for find_miRNA(). mir_info is the system data.frame containing information of EV-derived miRNA of “Human”, “Mouse”, and “Rat”. mir2tar is the system data.frame containing miRNA-target interactions of “Human”, “Mouse”, and “Rat”.To use your own mir_info data.frame, please refer to demo_mir_info() and demo_mir2tar() to build a new one, e.g., zebrafish, Drosophila, C. elegans, etc. Detailed system data see the GitHub readme

Parameters of find_miRNA() see below:

object miRTalk object after create_miRTalk()
mir_info A data.frame of the system data containing information of EV-derived miRNA of “Human”, “Mouse”, and “Rat”
mir2tar A data.frame of the system data containing miRNA-target interactions of “Human”, “Mouse”, and “Rat”
min_percent Min percent of expressed cells for target genes of miRNA. Default is 0.05
database Which database of miRNA-target interactions to use, “miRTarBase” and/or “TarBase”. Default is the “miRTarBase”. It can also be “TarBase” or c(“miRTarBase”, “TarBase”)
resolution Correct to precursor or mature miRNAs. Use ‘precursor’ or ‘mature’. Default is ‘mature’
regulation Inference of negative or positive regulation. Default is “negative”. Set it as “positive” and set database as “TarBase” for inferring positive regulation.
EXOmotifA sequence called EXOmotif to help miRNA secretion in EVs such as “CAUG”, “CGGGAG”. Please refer to https://doi.org/10.1038/s41586-021-04234-3
if_use_human_data Whether to use homologous human data in mir_info and mir2tar for mouse or rat scRNA-seq data. For human scRNA-seq data, no need to do it. For mouse or rat data, you can set it TRUE.
if_combine Whether to use combined homologous mir_info and mir2tar when if_use_human_data is TRUE. Default is TRUE.
gene2gene A data.frame of the system data containing the gene orthologs among human, mouse, and rat. If if_use_human_data is TRUE, please provide it, like “gene2gene = gene2gene”
per_num Number of permutation test. Default is 1000

# find MiTIs with negative regulation by default using risc
obj <- find_miRNA(object = obj,
                  mir_info = mir_info,
                  mir2tar = mir2tar)

# find MiTIs with positive regulation by default using ritac
# obj <- find_miRNA(object = obj,
#                   mir_info = mir_info,
#                   mir2tar = mir2tar,
#                   regulation = "positive")

# Use different databases
# obj <- find_miRNA(object = obj,
#                   mir_info = mir_info,
#                   mir2tar = mir2tar,
#                   database = c("miRTarBase", "TarBase"))

# Use EXOmotif to refine EV-derived miRNAs
# obj <- find_miRNA(object = obj,
#                   mir_info = mir_info,
#                   mir2tar = mir2tar,
#                   EXOmotif = "CAUG")

# Correct to precursor miRNAs
# obj <- find_miRNA(object = obj,
#                   mir_info = mir_info,
#                   mir2tar = mir2tar,
#                   resolution = "precursor")

# Use human database (mir_info and mir2tar) for mouse or rat scRNA-seq data
# obj <- find_miRNA(object = obj,
#                   mir_info = mir_info,
#                   mir2tar = mir2tar,
#                   if_use_human_data = TRUE,
#                   gene2gene = gene2gene)

Note: see the wiki page for detailed examples with custom data

`find_miRTalk()`

Infer cell-cell communications mediated by EV-derived miRNAs from senders to receivers with find_miRTalk(). The result is a character stored obj@cci. Users can use get_miRTalk_cci() to get simple results of miRNA-target interactions.

Parameters of find_miRTalk() see below:

object miRTalk object after find_miRNA() and find_hvtg()
min_cell_num Min cell number for each cell type and expressed miRNA. Default is 10
min_percent Min percent of expressed cells for target genes of miRNA. Default is 0.05
pvalue Cutoff of p value. Default is 0.05
if_filter_miRNA Whether to filter the significantly highly expressed miRNAs. Default is FALSE
if_doParallel Use doParallel. Default is TRUE
use_n_cores umber of CPU cores to use. Default is 4

# infer cell-cell communications mediated by EV-derived miRNAs from senders to receivers
obj <- find_miRTalk(object = obj)
#> [condition]
obj
#> An object of class miRTalk 
#> 460 EV-derived miRNA-target interactions

# the result is a data.frame
cci <- obj@cci
str(cci)
#> 'data.frame':    460 obs. of  19 variables:
#>  $ celltype_sender           : chr  "Bcell" "Bcell" "Bcell" "Bcell" ...
#>  $ celltype_receiver         : chr  "Bcell" "Bcell" "Bcell" "Bcell" ...
#>  $ miRNA                     : chr  "hsa-miR-3916" "hsa-miR-4426" "hsa-miR-146a-5p" "hsa-miR-146a-5p" ...
#>  $ miR_gene                  : chr  "MIR3916" "MIR4426" "MIR146A" "MIR146A" ...
#>  $ percent_sender            : num  0.164 0.19 0.385 0.385 0.385 ...
#>  $ percent_receiver          : num  0.164 0.19 0.385 0.385 0.385 ...
#>  $ EVmiR_score               : num  0.0174 0.0117 0.0265 0.0265 0.0265 ...
#>  $ target_gene               : chr  "CLDN4" "SCD" "SPP1" "IRF7" ...
#>  $ target_gene_rank          : num  12281 7488 7071 7257 323 ...
#>  $ target_gene_activity      : num  0.00086 0.00989 0.01153 0.01078 0.26459 ...
#>  $ target_gene_mean_exp      : num  0.00417 0.04804 0.05596 0.05233 1.28454 ...
#>  $ target_gene_mean_exp_other: num  0.801 0.649 0.256 0.127 1.654 ...
#>  $ target_gene_percent       : num  0.313 0.398 0.193 0.193 0.976 ...
#>  $ target_gene_percent_other : num  0.815 0.85 0.329 0.303 0.931 ...
#>  $ pvalue                    : num  1.45e-26 1.72e-23 7.81e-03 1.52e-02 2.27e-02 ...
#>  $ sig                       : chr  "YES" "YES" "YES" "YES" ...
#>  $ score                     : num  0.01266 0.00402 0.00648 0.00711 0.00466 ...
#>  $ prob                      : num  0.042 0.013 0.01 0.008 0.007 0.007 0.006 0.005 0.005 0.004 ...
#>  $ condition                 : chr  "condition" "condition" "condition" "condition" ...

# get simple results of miRNA-target interactions
obj_cci <- get_miRTalk_cci(obj)
str(obj_cci)
#> 'data.frame':    449 obs. of  10 variables:
#>  $ celltype_sender     : chr  "Bcell" "Myeloid" "Tcell" "Tumor" ...
#>  $ celltype_receiver   : chr  "Bcell" "Bcell" "Bcell" "Bcell" ...
#>  $ miRNA               : chr  "hsa-miR-3916" "hsa-miR-3916" "hsa-miR-3916" "hsa-miR-3916" ...
#>  $ EVmiR_score         : num  0.0174 0.0238 0.0313 0.0448 0.0117 ...
#>  $ target_gene         : chr  "CLDN4" "CLDN4" "CLDN4" "CLDN4" ...
#>  $ target_gene_activity: num  0.00086 0.00086 0.00086 0.00086 0.00989 ...
#>  $ score               : num  0.01266 0.04056 0.03305 0.01867 0.00402 ...
#>  $ condition           : chr  "condition" "condition" "condition" "condition" ...
#>  $ miR2tar             : chr  "hsa-miR-3916:CLDN4" "hsa-miR-3916:CLDN4" "hsa-miR-3916:CLDN4" "hsa-miR-3916:CLDN4" ...
#>  $ specifity           : num  0.148 0.203 0.267 0.382 0.246 ...

Visualization

`plot_miRTalk_chord()`

Parameters of plot_miRTalk_chord() see below:

object miRTalk object after find_miRTalk()
condition which conditions to plot. Default is plot all conditions
celltype which cell types to plot by order. Default is to plot all cell types
celltype_color Colors for the cell types, whose length must be equal to celltype
miRNA which miRNAs to use. Default is to plot all inferred miRNAs
edge_color Colors for the edges from the sender cell type, whose length must be equal to celltype
edge_type Types for the edges from the sender cell type. Default is “big.arrow”. “ellipse” for ellipse, “triangle” for triangle, “curved” for curved. Details see circlize::chordDiagram()
show_type which type of miRNAs to show, “number”, “EVmiR_score”, or “score” for sum of inferred miRNAs number, EVmiR_score, and MiTI_score, respectively. Default is “number”
if_show_autocrine Whether to show autocrine. Default is FALSE
text_size Size of text labels. Default is 1.5
y_scale y_scale to adjust the text. Default is 0.1
... parameters pass to circlize::chordDiagram(), e.g., link.arr.width, link.arr.length, link.arr.col

plot_miRTalk_chord(object = obj)


# use condition parameter to plot results under different conditions separately

`plot_miRTalk_circle()`

Parameters of plot_miRTalk_circle() see below:

object miRTalk object after find_miRTalk()
condition which conditions to plot. Default is plot all conditions
celltype which cell types to plot. Default is to plot all cell types
miRNA which miRNAs to use. Default is to plot all inferred miRNAs
celltype_color Colors for the cell types, whose length must be equal to celltype
edge_color Colors for the edges from the sender cell type, whose length must be equal to celltype
edge_type Types for the edges. “fan” by default, “link”, “hive”
show_type which type of miRNAs to show, “number”, “EVmiR_score”, or “score” for sum of inferred miRNAs number, EVmiR_score, and MiTI_score, respectively. Default is “number”
if_show_autocrine Whether to show autocrine. Default is FALSE
edge_alpha Transparency of edge. Default is 0.5
node_size Size of node. Default is 10
text_size Size of text. Default is 5

plot_miRTalk_circle(object = obj)


# use condition parameter to plot results under different conditions separately

`plot_miRTalk_circle_simple()`

To show one or more senders or receivers by retaining all cell types, users can use `plot_miRTalk_circle_simple()` for plotting.

Parameters of plot_miRTalk_circle_simple() see below:

object miRTalk object after find_miRTalk()
condition which conditions to plot. Default is plot all conditions
celltype which cell types to plot. one or more cell types
celltype_dir which direction to plot, “sender” or “receiver”. Default is as “sender”
miRNA which miRNAs to use. Default is to plot all inferred miRNAs
celltype_color Colors for the cell types, whose length must be equal to celltype
edge_color Colors for the edges from the sender cell type, whose length must be equal to celltype
edge_type Types for the edges. “fan” by default, “link”, “hive”
show_type which type of miRNAs to show, “number”, “EVmiR_score”, or “score” for sum of inferred miRNAs number, EVmiR_score, and MiTI_score, respectively. Default is “number”
if_show_autocrine Whether to show autocrine. Default is FALSE
edge_alpha Transparency of edge. Default is 0.5
node_size Size of node. Default is 10
text_size Size of text. Default is 5

# as sender
plot_miRTalk_circle_simple(object = obj, celltype = "Tumor", celltype_dir = "sender")


# as receiver
plot_miRTalk_circle_simple(object = obj, celltype = "Tumor", celltype_dir = "reciver")


# use condition parameter to plot results under different conditions separately

`plot_miRTalk_heatmap()`

Parameters of plot_miRTalk_heatmap() see below:

object miRTalk object after find_miRTalk()
condition which conditions to plot. Default is plot all conditions
celltype which cell types to plot. Default is to plot all cell types
miRNA which miRNAs to use. Default is to plot all inferred miRNAs
show_type which type of miRNAs to show, “number”, “EVmiR_score”, or “score” for sum of inferred miRNAs number, EVmiR_score, and MiTI_score, respectively. Default is “number”
text_size Size of text labels. Default is 10
viridis_option option in viridis::scale_color_viridis, can be “A”, “B”, “C”, “D”, “E”, “F”, “G”, “H”. Default is “D”
... parameters pass to heatmaply::heatmaply, e.g., grid_color, grid_width

plot_miRTalk_heatmap(object = obj)


# use condition parameter to plot results under different conditions separately

`plot_miRTalk_sankey()`

Parameters of plot_miRTalk_sankey() see below:

object miRTalk object after find_miRTalk()
condition which conditions to plot. Default is plot all conditions
celltype which cell types to plot by order. Default is to plot all cell types
miRNA which miRNAs to use. Default is to plot all inferred miRNAs
celltype_color Colors for the cell types, whose length must be equal to celltype
edge_color Colors for the edges from the sender cell type, whose length must be equal to celltype, Or use “NO” to cancel it
show_type which type of miRNAs to show, “number”, “EVmiR_score”, or “score” for sum of inferred miRNAs number, EVmiR_score, and MiTI_score, respectively. Default is “number”
if_show_autocrine Whether to show autocrine. Default is FALSE
edge_alpha Transparency of edge. Default is 0.5
node_size Size of node. Default is 40
text_size Size of text. Default is 15
node_pad Size of node padding. Numeric essentially influences the width height. Default is 20
... parameters pass to networkD3::sankeyNetwork

plot_miRTalk_sankey(object = obj)


# use condition parameter to plot results under different conditions separately

`plot_miR_heatmap()`

Parameters of plot_miR_heatmap()see below:

object miRTalk object after find_miRTalk()
condition which conditions to plot. Default is plot all conditions
celltype which cell types to plot. Default is to plot all cell types
miRNA which miRNAs to plot. Default is to plot all inferred miRNAs
text_size Size of text labels. Default is 10
if_horizontal Whether to plot with the horizontal direction. Default is TRUE
viridis_option option in viridis::scale_color_viridis, can be “A”, “B”, “C”, “D”, “E”, “F”, “G”, “H”. Default is “D”.
... parameters pass to heatmaply::heatmaply, e.g., grid_color, grid_width

plot_miR_heatmap(object = obj)


# use condition parameter to plot results under different conditions separately

`plot_target_heatmap()`

Parameters of plot_target_heatmap()see below:

object miRTalk object after find_miRTalk()
condition which conditions to plot. Default is plot all conditions
celltype which cell types to plot. Default is to plot all cell types
targetgenes which targetgenes to plot. Default is to plot all inferred target genes in receivers
limits A parameter used in heatmaply::heatmaply, a two dimensional numeric vector specifying the data range for the scale. Default is 0-1
text_size Size of text labels. Default is 10
if_horizontal Whether to plot with the horizontal direction. Default is TRUE
viridis_option option in viridis::scale_color_viridis, can be “A”, “B”, “C”, “D”, “E”, “F”, “G”, “H”. Default is “D”.
... parameters pass to heatmaply::heatmaply, e.g., grid_color, grid_width

plot_target_heatmap(object = obj, celltype = "Bcell")


# use condition parameter to plot results under different conditions separately

`plot_miR_bubble()`

Parameters of plot_miR_bubble() see below:

object miRTalk object after find_miRTalk()
condition which conditions to plot. Default is plot all conditions
celltype which cell types to plot. Default is to plot all cell types
miRNA which miRNAs to plot. Default is to plot all inferred miRNAs
if_show_autocrine Whether to show autocrine. Default is FALSE
if_horizontal Whether to plot with the horizontal direction. Default is TRUE
viridis_option option in viridis::scale_color_viridis, can be “A”, “B”, “C”, “D”, “E”, “F”, “G”, “H”. Default is “D”.

plot_miR_bubble(object = obj)


# use condition parameter to plot results under different conditions separately

`plot_miR2tar_chord()`

Parameters of plot_miR2tar_chord() see below:

object miRTalk object after find_miRTalk()
condition which conditions to plot. Default is plot all conditions
celltype_sender Name of celltype_sender. One or more cell types
celltype_receiver Name of celltype_receiver. One or more cell types
celltype_color Colors for the celltype_sender nodes and celltype_receiver nodes, or use “NO” to make it simple
miRNA which miRNAs to use. Default is to plot all inferred miRNAs
edge_color Colors for the edges from the sender cell type
edge_type Types for the edges from the sender cell type. Default is “circle”, “big.arrow” for big arrow, “triangle” for triangle, “ellipse” for ellipse, “curved” for curved. Details see circlize::chordDiagram
text_size Size of text labels. Default is 0.5
y_scale y_scale to adjust the text. Default is 1
... parameters pass to circlize::chordDiagram, e.g., link.arr.width, link.arr.length, link.arr.col

plot_miR2tar_chord(obj, celltype_sender = "Tumor", celltype_receiver = "Bcell")


# use condition parameter to plot results under different conditions separately

`plot_miR2tar_circle()`

Parameters of plot_miR2tar_circle() see below:

object miRTalk object after find_miRTalk()
condition which conditions to plot. Default is plot all conditions
celltype_sender Name of celltype_sender. One or more cell types
celltype_receiver Name of celltype_receiver. One or more cell types
celltype_color Colors for the celltype_sender nodes and celltype_receiver nodes, or use “NO” to make it simple
miRNA which miRNAs to use. Default is to plot all inferred miRNAs
node_size Size of node. Default is 3
edge_color Colors for the edges from the sender cell type
text_size Size of text labels. Default is 3
edge_width y_scale to adjust the text. Default is 0.5
if_show_legend Whether to show legends. Default is FALSE

plot_miR2tar_circle(obj, celltype_sender = "Tumor", celltype_receiver = "Bcell")


plot_miR2tar_circle(obj, celltype_sender = "Tumor", celltype_receiver = "Bcell", celltype_color = "NO")


# use condition parameter to plot results under different conditions separately

`plot_miR2tar_heatmap()`

Parameters of plot_miR2tar_heatmap() see below:

object miRTalk object after find_miRTalk()
condition which conditions to plot. Default is plot all conditions
celltype_sender Name of celltype_sender. One or more cell types
celltype_receiver Name of celltype_receiver. One or more cell types
miRNA which miRNAs to use. Default is to plot all inferred miRNAs
text_size Size of text labels. Default is 3
if_horizontal Whether to plot with the horizontal direction. Default is TRUE
viridis_option option in viridis::scale_color_viridis, can be “A”, “B”, “C”, “D”, “E”, “F”, “G”, “H”. Default is “D”.
... parameters pass to heatmaply::heatmaply, e.g., grid_color

plot_miR2tar_heatmap(obj, celltype_sender = "Tumor", celltype_receiver = "Bcell", grid_color = "black")


# use condition parameter to plot results under different conditions separately

Note

We add analysis of functional annotations for a miRNA and its target gene, for example, overlapped/shared pathways, use `get_miRTalk_pathway()`. Detailed see the wiki page

Parameters of get_miRTalk_pathway() see below:

object miRTalk object after find_miRTalk()
gene2path A data.frame of the system data containing gene-related pathways from KEGG, Reactome, GO_BP, Wikipathways
mir2path A data.frame of the system data containing miRNA-related pathways from KEGG, Reactome, GO_BP, Wikipathways
miRNA which miRNAs to analyze. Default is all inferred miRNAs in senders.
targetgenes which targetgenes to analyze. Default is all inferred target genes in receivers.

# get pathways
res_pathway <- get_miRTalk_pathway(object = obj,
                                     gene2path = gene2path,
                                     mir2path = mir2path,
                                     miRNA = "hsa-miR-133a-3p",
                                     targetgenes = "ANXA2")

str(res_pathway[[1]]$miRNA_term) # pathways for hsa-miR-133a-3p
#>  chr [1:382] "CELLULAR RESPONSE TO GLUCOCORTICOID STIMULUS" "ANGIOGENESIS" ...

str(res_pathway[[2]]$target_gene_term) # pathways for ANXA2
#>  chr [1:173] "THE PROCESS IN WHICH INACTIVE PLASMINOGEN IS PROCESSED TO ACTIVE PLASMIN. THIS PROCESS INCLUDES CLEAVAGE AT AN "| __truncated__ ...

res_pathway[[3]]$miRNA_term # overlapped/shared pathways
#>  [1] "ANGIOGENESIS"                                                                          
#>  [2] "INNATE IMMUNE SYSTEM"                                                                  
#>  [3] "GENE AND PROTEIN EXPRESSION BY JAK-STAT SIGNALING AFTER INTERLEUKIN-12 STIMULATION"    
#>  [4] "SIGNALING BY INTERLEUKINS"                                                             
#>  [5] "INTERLEUKIN-12 SIGNALING"                                                              
#>  [6] "MUSCLE CONTRACTION"                                                                    
#>  [7] "NEUTROPHIL DEGRANULATION"                                                              
#>  [8] "SMOOTH MUSCLE CONTRACTION"                                                             
#>  [9] "OSTEOCLAST DEVELOPMENT"                                                                
#> [10] "MUSCLE CONTRACTION"                                                                    
#> [11] "INTERLEUKIN-12 FAMILY SIGNALING"                                                       
#> [12] "NEUTROPHIL DEGRANULATION"                                                              
#> [13] "POSITIVE REGULATION OF LOW-DENSITY LIPOPROTEIN PARTICLE CLEARANCE"                     
#> [14] "POSITIVE REGULATION OF TRANSCRIPTION BY RNA POLYMERASE II"                             
#> [15] "POSITIVE REGULATION OF RECEPTOR-MEDIATED ENDOCYTOSIS INVOLVED IN CHOLESTEROL TRANSPORT"

We add analysis of potential of circulating miRNAs and organ‐organ communication, use `get_miRTalk_circulating_score()`. Detailed see the wiki page

Parameters of get_miRTalk_circulating_score() see below:

object miRTalk object after find_miRTalk()

library(ggplot2)
# get circulating miRNAs
res_circulating <- get_miRTalk_circulating_score(obj)
str(res_circulating)
#> 'data.frame':    164 obs. of  7 variables:
#>  $ miRNA            : chr  "hsa-miR-146a-5p" "hsa-miR-146a-5p" "hsa-miR-146a-5p" "hsa-miR-146a-5p" ...
#>  $ tissue_TarBase   : chr  "Kidney; Pleura; Lymphatic tissue; Cervix; Bone marrow; Umbilical vein; Pancreas; Brain - Motor cortex; Brain - "| __truncated__ "Kidney; Pleura; Lymphatic tissue; Cervix; Bone marrow; Umbilical vein; Pancreas; Brain - Motor cortex; Brain - "| __truncated__ "Kidney; Pleura; Lymphatic tissue; Cervix; Bone marrow; Umbilical vein; Pancreas; Brain - Motor cortex; Brain - "| __truncated__ "Kidney; Pleura; Lymphatic tissue; Cervix; Bone marrow; Umbilical vein; Pancreas; Brain - Motor cortex; Brain - "| __truncated__ ...
#>  $ score            : num  0.661 0.661 0.661 0.661 0.661 ...
#>  $ miR_gene         : chr  "MIR146A" "MIR146A" "MIR146A" "MIR146A" ...
#>  $ celltype_receiver: chr  "Tcell" "Tumor" "Bcell" "Bcell" ...
#>  $ target_gene      : chr  "MYLK" "CD80" "SPP1" "HSPA1A" ...
#>  $ condition        : chr  "condition" "condition" "condition" "condition" ...

#plotting
res_circulating$celltype_receiver <- paste0(res_circulating$condition,"_",res_circulating$celltype_receiver)
res_circulating <- unique(res_circulating[,c("miRNA","score","celltype_receiver")])
res_cir_plot <- reshape2::dcast(data = res_circulating, formula = miRNA ~ celltype_receiver, value.var = "score", fun.aggregate = mean, fill = 0)
rownames(res_cir_plot) <- res_cir_plot$miRNA
res_cir_plot <- res_cir_plot[,-1]
heat_col <- viridis::viridis(n = 256, alpha = 1, begin = 0, end = 1, option = "D")
heatmaply::heatmaply(x = as.matrix(res_cir_plot), colors = heat_col, limits = c(0,1),dendrogram = "none",  margins = c(60,100,40,20), titleX = FALSE, main = "Circulating potential", branches_lwd = 0.1, fontsize_row = 10, fontsize_col = 10, labCol = colnames(res_cir_plot), labRow = rownames(res_cir_plot), heatmap_layers = theme(axis.line=element_blank()))

sessionInfo()
#> R version 4.2.1 (2022-06-23)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#> 
#> Matrix products: default
#> BLAS:   /slurm/soft/R/4.2.1/lib64/R/lib/libRblas.so
#> LAPACK: /slurm/soft/R/4.2.1/lib64/R/lib/libRlapack.so
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> attached base packages:
#> [1] parallel  stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#> [1] ggplot2_3.4.2     miRTalk_1.0       doParallel_1.0.17 iterators_1.0.14 
#> [5] foreach_1.5.2    
#> 
#> loaded via a namespace (and not attached):
#>   [1] circlize_0.4.15        plyr_1.8.8             igraph_1.5.0          
#>   [4] lazyeval_0.2.2         sp_2.0-0               splines_4.2.1         
#>   [7] crosstalk_1.2.0        listenv_0.9.0          scattermore_1.2       
#>  [10] digest_0.6.31          ca_0.71.1              htmltools_0.5.5       
#>  [13] viridis_0.6.3          fansi_1.0.4            magrittr_2.0.3        
#>  [16] tensor_1.5             cluster_2.1.3          ROCR_1.0-11           
#>  [19] limma_3.54.2           globals_0.16.2         graphlayouts_1.1.0    
#>  [22] matrixStats_1.0.0      spatstat.sparse_3.0-2  prettyunits_1.1.1     
#>  [25] rmdformats_1.0.4       colorspace_2.1-0       ggrepel_0.9.3         
#>  [28] xfun_0.39              dplyr_1.1.2            crayon_1.5.2          
#>  [31] jsonlite_1.8.5         progressr_0.13.0       spatstat.data_3.0-1   
#>  [34] survival_3.3-1         zoo_1.8-12             glue_1.6.2            
#>  [37] polyclip_1.10-4        registry_0.5-1         gtable_0.3.3          
#>  [40] webshot_0.5.5          leiden_0.4.3           future.apply_1.11.0   
#>  [43] shape_1.4.6            abind_1.4-5            scales_1.2.1          
#>  [46] pheatmap_1.0.12        spatstat.random_3.1-5  miniUI_0.1.1.1        
#>  [49] Rcpp_1.0.10            progress_1.2.2         viridisLite_0.4.2     
#>  [52] xtable_1.8-4           reticulate_1.30        datawizard_0.12.0     
#>  [55] htmlwidgets_1.6.2      httr_1.4.6             RColorBrewer_1.1-3    
#>  [58] ellipsis_0.3.2         Seurat_4.3.0.1         ica_1.0-3             
#>  [61] pkgconfig_2.0.3        farver_2.1.1           sass_0.4.6            
#>  [64] uwot_0.1.14            deldir_1.0-9           utf8_1.2.3            
#>  [67] labeling_0.4.2         tidyselect_1.2.0       rlang_1.1.1           
#>  [70] reshape2_1.4.4         later_1.3.1            munsell_0.5.0         
#>  [73] tools_4.2.1            cachem_1.0.8           cli_3.6.1             
#>  [76] generics_0.1.3         ggridges_0.5.4         evaluate_0.21         
#>  [79] stringr_1.5.0          fastmap_1.1.1          heatmaply_1.5.0       
#>  [82] yaml_2.3.7             goftest_1.2-3          knitr_1.43            
#>  [85] fitdistrplus_1.1-11    tidygraph_1.3.0        purrr_1.0.1           
#>  [88] RANN_2.6.1             dendextend_1.17.1      ggraph_2.1.0          
#>  [91] pbapply_1.7-0          future_1.32.0          nlme_3.1-157          
#>  [94] mime_0.12              correlation_0.8.5      compiler_4.2.1        
#>  [97] rstudioapi_0.14        plotly_4.10.2          png_0.1-8             
#> [100] spatstat.utils_3.0-5   tibble_3.2.1           tweenr_2.0.2          
#> [103] bslib_0.5.0            stringi_1.7.12         highr_0.10            
#> [106] lattice_0.20-45        Matrix_1.5-4.1         vctrs_0.6.3           
#> [109] networkD3_0.4          pillar_1.9.0           lifecycle_1.0.3       
#> [112] spatstat.geom_3.2-1    lmtest_0.9-40          jquerylib_0.1.4       
#> [115] GlobalOptions_0.1.2    RcppAnnoy_0.0.20       insight_0.20.1        
#> [118] data.table_1.14.8      cowplot_1.1.1          irlba_2.3.5.1         
#> [121] seriation_1.5.4        httpuv_1.6.11          patchwork_1.1.2       
#> [124] R6_2.5.1               bookdown_0.40          promises_1.2.0.1      
#> [127] TSP_1.2-4              KernSmooth_2.23-20     gridExtra_2.3         
#> [130] parallelly_1.36.0      codetools_0.2-18       MASS_7.3-57           
#> [133] assertthat_0.2.1       withr_2.5.0            SeuratObject_4.1.3    
#> [136] sctransform_0.3.5      bayestestR_0.13.2      hms_1.1.3             
#> [139] grid_4.2.1             tidyr_1.3.0            rmarkdown_2.22        
#> [142] Rtsne_0.16             spatstat.explore_3.2-1 ggforce_0.4.1         
#> [145] shiny_1.7.4

About

Please refer to the miRTalk on GitHub or the document for more information.

miRTalk tutorial