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 dgCMatrixdata_type
A character to define the type ofdata
, select ācountā for the data matrix, āmir_infoā for the mir_info data.frame, āmir2tarā for the mir2tar data.frame, āpathwaysā for the pathways data.frame, āgene2goā for the gene2go data.framespecies
Species of the data.āHumanā, āMouseā or āRatāgeneinfo
A data.frame of the system data containing gene symbols of āHumanā, āMouseā and āRatā 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 datasc_celltype
A character containing the cell type of the single-cell RNA-seq dataspecies
A character meaning species of the single-cell transcriptomics data. āHumanā, āMouseā, āRatācondition
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 withSeurat
LogNormalize()
evbiog
A data.frame of the system data containing extracellular vesicle biogenesis genes of āHumanā, āMouseā, āRatā.risc
A data.frame of the system data containing RNA-induced silencing complex related genes of āHumanā, āMouseā, āRatā.ritac
A data.frame of the system data containing RNA-induced transcriptional activation complex related genes of āHumanā, āMouseā, āRatā.
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$var_genes
. Users can use their
own methods to find Find highly variable target genes and replace
them.
Parameters of find_hvtg()
see below:
object
miRTalk object aftercreate_miRTalk()
pvalue
Cutoff of p value. Default is 0.05log2fc
log2 fold change for identifying the highly expressed genes in each cell type. Default is 0.5min_cell_num
Min cell number for each cell type. Default is 10nfeatures
Number of features to select as top variable features. Default is 3000
# find highly variable target genes
obj <- find_hvtg(object = obj)
# the result is a character
var_genes <- obj@data$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
āHumanā, āMouseā, and āRatā. mir2tar
is the system
data.frame containing miRNA-target interactions of āHumanā, āMouseā, and
āRatā.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 aftercreate_miRTalk()
mir_info
A data.frame of the system data containing information of EV-derived miRNA of āHumanā, āMouseā, and āRatāmir2tar
A data.frame of the system data containing miRNA-target interactions of āHumanā, āMouseā, and āRatāmin_percent
Min percent of expressed cells for target genes of miRNA. Default is 0.05database
Which database of miRNA-target interactions to use, āmiRTarBaseā and/or āTarBaseā. Default is the āmiRTarBaseā. It can also be āTarBaseā or c(āmiRTarBaseā, āTarBaseā)resolution
Correct to precursor or mature miRNAs. Use āprecursorā or āmatureā. Default is āmatureāregulation
Inference of negative or positive regulation. Default is ānegativeā. Set it as āpositiveā and set database as āTarBaseā for inferring positive regulation.EXOmotif
A sequence called EXOmotif to help miRNA secretion in EVs such as āCAUGā, āCGGGAGā. Please refer to https://doi.org/10.1038/s41586-021-04234-3if_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 āgene2gene = gene2geneāper_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 afterfind_miRNA()
andfind_hvtg()
min_cell_num
Min cell number for each cell type and expressed miRNA. Default is 10min_percent
Min percent of expressed cells for target genes of miRNA. Default is 0.05pvalue
Cutoff of p value. Default is 0.05if_filter_miRNA
Whether to filter the significantly highly expressed miRNAs. Default is FALSEif_doParallel
Use doParallel. Default is TRUEuse_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 afterfind_miRTalk()
condition
which conditions to plot. Default is plot all conditionscelltype
which cell types to plot by order. Default is to plot all cell typescelltype_color
Colors for the cell types, whose length must be equal tocelltype
miRNA
which miRNAs to use. Default is to plot all inferred miRNAsedge_color
Colors for the edges from the sender cell type, whose length must be equal to celltypeedge_type
Types for the edges from the sender cell type. Default is ābig.arrowā. āellipseā for ellipse, ātriangleā for triangle, ācurvedā for curved. Details seecirclize::chordDiagram()
show_type
which type of miRNAs to show, ānumberā, āEVmiR_scoreā, or āscoreā for sum of inferred miRNAs number, EVmiR_score, and MiTI_score, respectively. Default is ānumberāif_show_autocrine
Whether to show autocrine. Default is FALSEtext_size
Size of text labels. Default is 1.5y_scale
y_scale to adjust the text. Default is 0.1...
parameters pass tocirclize::chordDiagram()
, e.g.,link.arr.width
,link.arr.length
,link.arr.col
plot_miRTalk_circle()
Parameters of plot_miRTalk_circle()
see below:
object
miRTalk object afterfind_miRTalk()
condition
which conditions to plot. Default is plot all conditionscelltype
which cell types to plot. Default is to plot all cell typesmiRNA
which miRNAs to use. Default is to plot all inferred miRNAscelltype_color
Colors for the cell types, whose length must be equal tocelltype
edge_color
Colors for the edges from the sender cell type, whose length must be equal tocelltype
edge_type
Types for the edges. āfanā by default, ālinkā, āhiveāshow_type
which type of miRNAs to show, ānumberā, āEVmiR_scoreā, or āscoreā for sum of inferred miRNAs number, EVmiR_score, and MiTI_score, respectively. Default is ānumberāif_show_autocrine
Whether to show autocrine. Default isFALSE
edge_alpha
Transparency of edge. Default is 0.5node_size
Size of node. Default is 10text_size
Size of text. Default is 5
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 afterfind_miRTalk()
condition
which conditions to plot. Default is plot all conditionscelltype
which cell types to plot. one or more cell typescelltype_dir
which direction to plot, āsenderā or āreceiverā. Default is as āsenderāmiRNA
which miRNAs to use. Default is to plot all inferred miRNAscelltype_color
Colors for the cell types, whose length must be equal tocelltype
edge_color
Colors for the edges from the sender cell type, whose length must be equal tocelltype
edge_type
Types for the edges. āfanā by default, ālinkā, āhiveāshow_type
which type of miRNAs to show, ānumberā, āEVmiR_scoreā, or āscoreā for sum of inferred miRNAs number, EVmiR_score, and MiTI_score, respectively. Default is ānumberāif_show_autocrine
Whether to show autocrine. Default isFALSE
edge_alpha
Transparency of edge. Default is 0.5node_size
Size of node. Default is 10text_size
Size of text. Default is 5
# as receiver
plot_miRTalk_circle_simple(object = obj, celltype = "Tumor", celltype_dir = "reciver")
plot_miRTalk_heatmap()
Parameters of plot_miRTalk_heatmap()
see below:
object
miRTalk object afterfind_miRTalk()
condition
which conditions to plot. Default is plot all conditionscelltype
which cell types to plot. Default is to plot all cell typesmiRNA
which miRNAs to use. Default is to plot all inferred miRNAsshow_type
which type of miRNAs to show, ānumberā, āEVmiR_scoreā, or āscoreā for sum of inferred miRNAs number, EVmiR_score, and MiTI_score, respectively. Default is ānumberātext_size
Size of text labels. Default is 10viridis_option
option inviridis::scale_color_viridis
, can be āAā, āBā, āCā, āDā, āEā, āFā, āGā, āHā. Default is āDā...
parameters pass toheatmaply::heatmaply
, e.g., grid_color, grid_width
plot_miRTalk_sankey()
Parameters of plot_miRTalk_sankey()
see below:
object
miRTalk object afterfind_miRTalk()
condition
which conditions to plot. Default is plot all conditionscelltype
which cell types to plot by order. Default is to plot all cell typesmiRNA
which miRNAs to use. Default is to plot all inferred miRNAscelltype_color
Colors for the cell types, whose length must be equal tocelltype
edge_color
Colors for the edges from the sender cell type, whose length must be equal tocelltype
, Or use āNOā to cancel itshow_type
which type of miRNAs to show, ānumberā, āEVmiR_scoreā, or āscoreā for sum of inferred miRNAs number, EVmiR_score, and MiTI_score, respectively. Default is ānumberāif_show_autocrine
Whether to show autocrine. Default is FALSEedge_alpha
Transparency of edge. Default is 0.5node_size
Size of node. Default is 40text_size
Size of text. Default is 15node_pad
Size of node padding. Numeric essentially influences the width height. Default is 20...
parameters pass tonetworkD3::sankeyNetwork
plot_miR_heatmap()
Parameters of plot_miR_heatmap()
see below:
object
miRTalk object afterfind_miRTalk()
condition
which conditions to plot. Default is plot all conditionscelltype
which cell types to plot. Default is to plot all cell typesmiRNA
which miRNAs to plot. Default is to plot all inferred miRNAstext_size
Size of text labels. Default is 10if_horizontal
Whether to plot with the horizontal direction. Default isTRUE
viridis_option
option inviridis::scale_color_viridis
, can be āAā, āBā, āCā, āDā, āEā, āFā, āGā, āHā. Default is āDā....
parameters pass toheatmaply::heatmaply
, e.g., grid_color, grid_width
plot_target_heatmap()
Parameters of plot_target_heatmap()
see below:
object
miRTalk object afterfind_miRTalk()
condition
which conditions to plot. Default is plot all conditionscelltype
which cell types to plot. Default is to plot all cell typestargetgenes
which targetgenes to plot. Default is to plot all inferred target genes in receiverslimits
A parameter used inheatmaply::heatmaply
, a two dimensional numeric vector specifying the data range for the scale. Default is 0-1text_size
Size of text labels. Default is 10if_horizontal
Whether to plot with the horizontal direction. Default isTRUE
viridis_option
option inviridis::scale_color_viridis
, can be āAā, āBā, āCā, āDā, āEā, āFā, āGā, āHā. Default is āDā....
parameters pass toheatmaply::heatmaply
, e.g., grid_color, grid_width
plot_miR_bubble()
Parameters of plot_miR_bubble()
see below:
object
miRTalk object afterfind_miRTalk()
condition
which conditions to plot. Default is plot all conditionscelltype
which cell types to plot. Default is to plot all cell typesmiRNA
which miRNAs to plot. Default is to plot all inferred miRNAsif_show_autocrine
Whether to show autocrine. Default isFALSE
if_horizontal
Whether to plot with the horizontal direction. Default isTRUE
viridis_option
option inviridis::scale_color_viridis
, can be āAā, āBā, āCā, āDā, āEā, āFā, āGā, āHā. Default is āDā.
plot_miR2tar_chord()
Parameters of plot_miR2tar_chord()
see below:
object
miRTalk object afterfind_miRTalk()
condition
which conditions to plot. Default is plot all conditionscelltype_sender
Name of celltype_sender. One or more cell typescelltype_receiver
Name of celltype_receiver. One or more cell typescelltype_color
Colors for the celltype_sender nodes and celltype_receiver nodes, or use āNOā to make it simplemiRNA
which miRNAs to use. Default is to plot all inferred miRNAsedge_color
Colors for the edges from the sender cell typeedge_type
Types for the edges from the sender cell type. Default is ācircleā, ābig.arrowā for big arrow, ātriangleā for triangle, āellipseā for ellipse, ācurvedā for curved. Details seecirclize::chordDiagram
text_size
Size of text labels. Default is 0.5y_scale
y_scale to adjust the text. Default is 1...
parameters pass tocirclize::chordDiagram
, e.g.,link.arr.width
,link.arr.length
,link.arr.col
plot_miR2tar_circle()
Parameters of plot_miR2tar_circle()
see below:
object
miRTalk object afterfind_miRTalk()
condition
which conditions to plot. Default is plot all conditionscelltype_sender
Name of celltype_sender. One or more cell typescelltype_receiver
Name of celltype_receiver. One or more cell typescelltype_color
Colors for the celltype_sender nodes and celltype_receiver nodes, or use āNOā to make it simplemiRNA
which miRNAs to use. Default is to plot all inferred miRNAsnode_size
Size of node. Default is 3edge_color
Colors for the edges from the sender cell typetext_size
Size of text labels. Default is 3edge_width
y_scale to adjust the text. Default is 0.5if_show_legend
Whether to show legends. Default is FALSE
plot_miR2tar_circle(obj, celltype_sender = "Tumor", celltype_receiver = "Bcell", celltype_color = "NO")
plot_miR2tar_heatmap()
Parameters of plot_miR2tar_heatmap()
see below:
object
miRTalk object afterfind_miRTalk()
condition
which conditions to plot. Default is plot all conditionscelltype_sender
Name of celltype_sender. One or more cell typescelltype_receiver
Name of celltype_receiver. One or more cell typesmiRNA
which miRNAs to use. Default is to plot all inferred miRNAstext_size
Size of text labels. Default is 3if_horizontal
Whether to plot with the horizontal direction. Default isTRUE
viridis_option
option inviridis::scale_color_viridis
, can be āAā, āBā, āCā, āDā, āEā, āFā, āGā, āHā. Default is āDā....
parameters pass toheatmaply::heatmaply
, e.g., grid_color
Note
We add analysis of potential of circulating miRNAs and organāorgan
communication, use get_miRTalk_circulating_score()
.
Detailed see the wiki
page
Parameters of get_miRTalk_circulating_score()
see
below:
object
miRTalk object afterfind_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