diff --git a/R/demo.R b/R/demo.R index 2fda006..0b61d17 100644 --- a/R/demo.R +++ b/R/demo.R @@ -26,11 +26,7 @@ demo_sc_data <- function() { #' @examples geneinfo_demo <- demo_geneinfo() demo_geneinfo <- function() { - gene1 <- c("A1BG", "A1BG", "A2MP1", "Aco1", "Alb1") - gene2 <- c("A1B", "ABG", "A2MP", "Aco", "Alb") - species <- c("Human", "Human", "Human", "Mouse", "Rat") - geneinfo_demo <- data.frame(symbol = gene1, synonyms = gene2, species = species, stringsAsFactors = FALSE) - return(geneinfo_demo) + return(head(geneinfo)) } #' @title Demo data of mir_info @@ -41,12 +37,7 @@ demo_geneinfo <- function() { #' @examples mir_info_demo <- demo_mir_info() demo_mir_info <- function() { - miRNA <- c("hsa-miR-1", "hsa-miR-1", "hsa-miR-1", "hsa-miR-1", "mmu-miR-105", "rno-miR-106b") - miRNA_mature <- c("hsa-miR-1-5p", "hsa-miR-1-5p", "hsa-miR-1-3p", "hsa-miR-1-3p", "mmu-miR-105", "rno-miR-106b-5p") - gene <- c("MIR1-1", "MIR1-2","MIR1-1", "MIR1-2", "Mir105", "Mir106b") - species <- c("Human", "Human", "Human", "Human", "Mouse", "Rat") - mir_info_demo <- data.frame(miRNA = miRNA, miRNA_mature = miRNA_mature, gene = gene, species = species, stringsAsFactors = FALSE) - return(mir_info_demo) + return(head(mir_info)) } #' @title Demo data of mir2tar @@ -57,10 +48,5 @@ demo_mir_info <- function() { #' @examples mir2tar_demo <- demo_mir2tar() demo_mir2tar <- function() { - miRNA <- c("hsa-miR-1", "hsa-miR-1", "mmu-miR-105", "rno-miR-106b") - miRNA_mature <- c("hsa-miR-1-5p", "hsa-miR-1-3p", "mmu-miR-105", "rno-miR-106b-5p") - target_gene <- c("BDNF", "RBM28", "Abl2", "Mcl1") - species <- c("Human", "Human", "Mouse", "Rat") - mir2tar_demo <- data.frame(miRNA = miRNA, miRNA_mature = miRNA_mature, target_gene = target_gene, species = species, stringsAsFactors = FALSE) - return(mir2tar_demo) + return(head(mir2tar)) } diff --git a/R/methods.R b/R/methods.R index e26d82a..01f1b4c 100644 --- a/R/methods.R +++ b/R/methods.R @@ -135,7 +135,7 @@ find_hvtg <- function(object, pvalue = 0.05, log2fc = 0.5, min_cell_num = 10, nf #' @description Find expressed miRNAs among all cells and generate background distribution for permutation test #' @param object miRTalk object after \code{\link{create_miRTalk}} #' @param mir_info A data.frame of the system data containing information of EV-derived miRNA of \code{'Human'}, \code{'Mouse'} or \code{'Rat'}. see \code{\link{demo_mir_info}} -#' @param mir2tar A data.frame of the system data containing relationship of miRNA and its target genes for \code{'Human'}, \code{'Mouse'} or \code{'Rat'}. see \code{\link{demo_mir2tar}} +#' @param mir2tar A data.frame of the system data containing miRNA-target interactions for \code{'Human'}, \code{'Mouse'} or \code{'Rat'}. see \code{\link{demo_mir2tar}} #' @param min_percent Min percent of expressed cells for target genes of miRNA. Default is \code{0.05} #' @param 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") #' @param resolution Correct to precursor or mature miRNAs. Use 'precursor' or 'mature'. Default is \code{'mature'} @@ -253,7 +253,6 @@ find_miRNA <- function(object, mir_info, mir2tar, min_percent = 0.05, database = #' @param min_percent Min percent of expressed cells for target genes of miRNA. Default is \code{0.05} #' @param pvalue Cutoff of p value. Default is \code{0.05} #' @param if_filter_miRNA Whether to filter the significantly highly expressed miRNAs. Default is FALSE -#' @param if_consider_condition Whether to infer for each condition. Default is TRUE #' @param if_doParallel Use doParallel. Default is TRUE #' @param use_n_cores Number of CPU cores to use. Default is 4 #' @return miRTalk object containing the inferred cell-cell communications mediated by EV-derived miRNAs @@ -262,7 +261,7 @@ find_miRNA <- function(object, mir_info, mir2tar, min_percent = 0.05, database = #' @importFrom correlation correlation #' @export -find_miRTalk <- function(object, min_cell_num = 10, min_percent = 0.05, pvalue = 0.05, per_num = 1000, if_filter_miRNA = FALSE, if_consider_condition = TRUE, if_doParallel = TRUE, use_n_cores = 4) { +find_miRTalk <- function(object, min_cell_num = 10, min_percent = 0.05, pvalue = 0.05, per_num = 1000, if_filter_miRNA = FALSE, if_doParallel = TRUE, use_n_cores = 4) { # check input if (!is(object, "miRTalk")) { stop("Invalid class for object: must be 'miRTalk'!") diff --git a/R/plot.R b/R/plot.R index 1fb9f6a..32a6d55 100644 --- a/R/plot.R +++ b/R/plot.R @@ -928,7 +928,6 @@ plot_miR_bubble <- function(object, condition = NULL, celltype = NULL, miRNA = N #' @param miRNA which miRNAs to use. Default is to plot all inferred miRNAs in senders. #' @param edge_color Colors for the edges from the sender cell type #' @param edge_type Types for the edges from the sender cell type. Default is \code{"circle"}. \code{"big.arrow"} for big arrow, "triangle" for triangle, "ellipse" for ellipse, "curved" for curved. Details see \code{\link[circlize]{chordDiagram}} -#' @param show_type which type of miRNAs to show, \code{"prob"} or \code{"score"} for inferred miRNAs-target interactions. Default is \code{"prob"} #' @param text_size Size of text labels. Default is \code{0.5} #' @param y_scale y_scale to adjust the text. Default is \code{1} #' @param ... parameters pass to \code{\link[circlize]{chordDiagram}}, e.g., link.arr.width, link.arr.length, link.arr.col diff --git a/README.md b/README.md index 0362498..51096d5 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # miRTalk -[![R-CMD-check](https://img.shields.io/badge/R--CMD--check-passing-brightgreen?logo=github)](https://github.com/multitalk/miRTalk/actions/runs/5220129987) [![miRTalkDB](https://img.shields.io/badge/miRTalkDB-v1.0-yellow)](https://github.com/multitalk/miRTalk/tree/main/data) [![miRTalk-tutorial](https://img.shields.io/badge/miRTalk-tutorial-blue)](https://raw.githack.com/multitalk/miRTalk/main/vignettes/tutorial.html) +[![R-CMD-check](https://img.shields.io/badge/R--CMD--check-passing-brightgreen?logo=github)](https://github.com/multitalk/miRTalk/actions) [![miRTalkDB](https://img.shields.io/badge/miRTalkDB-v1.0-yellow)](https://github.com/multitalk/miRTalk/tree/main/data) [![miRTalk-tutorial](https://img.shields.io/badge/miRTalk-tutorial-blue)](https://raw.githack.com/multitalk/miRTalk/main/vignettes/tutorial.html) ### Extracellular vesicle-derived miRNA-mediated cell-cell communication inference for single-cell transcriptomic data diff --git a/man/find_miRNA.Rd b/man/find_miRNA.Rd index 336454f..a0ad366 100644 --- a/man/find_miRNA.Rd +++ b/man/find_miRNA.Rd @@ -24,7 +24,7 @@ find_miRNA( \item{mir_info}{A data.frame of the system data containing information of EV-derived miRNA of \code{'Human'}, \code{'Mouse'} or \code{'Rat'}. see \code{\link{demo_mir_info}}} -\item{mir2tar}{A data.frame of the system data containing relationship of miRNA and its target genes for \code{'Human'}, \code{'Mouse'} or \code{'Rat'}. see \code{\link{demo_mir2tar}}} +\item{mir2tar}{A data.frame of the system data containing miRNA-target interactions for \code{'Human'}, \code{'Mouse'} or \code{'Rat'}. see \code{\link{demo_mir2tar}}} \item{min_percent}{Min percent of expressed cells for target genes of miRNA. Default is \code{0.05}} diff --git a/man/find_miRTalk.Rd b/man/find_miRTalk.Rd index b6198ae..e008427 100644 --- a/man/find_miRTalk.Rd +++ b/man/find_miRTalk.Rd @@ -11,7 +11,6 @@ find_miRTalk( pvalue = 0.05, per_num = 1000, if_filter_miRNA = FALSE, - if_consider_condition = TRUE, if_doParallel = TRUE, use_n_cores = 4 ) @@ -27,8 +26,6 @@ find_miRTalk( \item{if_filter_miRNA}{Whether to filter the significantly highly expressed miRNAs. Default is FALSE} -\item{if_consider_condition}{Whether to infer for each condition. Default is TRUE} - \item{if_doParallel}{Use doParallel. Default is TRUE} \item{use_n_cores}{Number of CPU cores to use. Default is 4} diff --git a/man/plot_miR2tar_chord.Rd b/man/plot_miR2tar_chord.Rd index f2c2911..0030769 100644 --- a/man/plot_miR2tar_chord.Rd +++ b/man/plot_miR2tar_chord.Rd @@ -40,8 +40,6 @@ plot_miR2tar_chord( \item{y_scale}{y_scale to adjust the text. Default is \code{1}} \item{...}{parameters pass to \code{\link[circlize]{chordDiagram}}, e.g., link.arr.width, link.arr.length, link.arr.col} - -\item{show_type}{which type of miRNAs to show, \code{"prob"} or \code{"score"} for inferred miRNAs-target interactions. Default is \code{"prob"}} } \value{ Chord plot of EV-derived miRNAs and target genes diff --git a/vignettes/tutorial.html b/vignettes/tutorial.html index 41faff4..f7979cc 100644 --- a/vignettes/tutorial.html +++ b/vignettes/tutorial.html @@ -7,7 +7,7 @@ - + miRTalk tutorial @@ -37,18 +37,6 @@ */ if("undefined"==typeof jQuery)throw new Error("Bootstrap's JavaScript requires jQuery");+function(a){"use strict";var b=a.fn.jquery.split(" ")[0].split(".");if(b[0]<2&&b[1]<9||1==b[0]&&9==b[1]&&b[2]<1||b[0]>3)throw new Error("Bootstrap's JavaScript requires jQuery version 1.9.1 or higher, but lower than version 4")}(jQuery),+function(a){"use strict";function b(){var a=document.createElement("bootstrap"),b={WebkitTransition:"webkitTransitionEnd",MozTransition:"transitionend",OTransition:"oTransitionEnd otransitionend",transition:"transitionend"};for(var c in b)if(void 0!==a.style[c])return{end:b[c]};return!1}a.fn.emulateTransitionEnd=function(b){var 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t={not_string:/[^s]/,not_bool:/[^t]/,not_type:/[^T]/,not_primitive:/[^v]/,number:/[diefg]/,numeric_arg:/[bcdiefguxX]/,json:/[j]/,not_json:/[^j]/,text:/^[^\x25]+/,modulo:/^\x25{2}/,placeholder:/^\x25(?:([1-9]\d*)\$|\(([^)]+)\))?(\+)?(0|'[^$])?(-)?(\d+)?(?:\.(\d+))?([b-gijostTuvxX])/,key:/^([a-z_][a-z_\d]*)/i,key_access:/^\.([a-z_][a-z_\d]*)/i,index_access:/^\[(\d+)\]/,sign:/^[+-]/};function e(t){return i(o(t),arguments)}function n(t,r){return e.apply(null,[t].concat(r||[]))}function i(r,n){var 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6:u.push([t-.5-.25*(-n-r+a+i)/(n-r+i-a),e-.5-.25*(-i-r+a+n)/(i-r+n-a)]);break;case 7:u.push([t-.75-.25*(a+i-2*h)/(i-a),e-.75-.25*(a+n-2*h)/(n-a)]);break;case 8:u.push([t-.75-.25*(-a-i+2*h)/(a-i),e-.75-.25*(-a-n+2*h)/(a-n)]);break;case 9:u.push([t-.5-.25*(n+r+-a-i)/(r-n+a-i),e-.5-.25*(i+r+-a-n)/(r-i+a-n)]);break;case 10:u.push([t-.5-.5*(-n-r-a-i+4*h)/(n-r+a-i),e-.5]);break;case 11:u.push([t-.25-.25*(-a-i+2*h)/(a-i),e-.75-.25*(i+r-2*h)/(r-i)]);break;case 12:u.push([t-.5,e-.5-.5*(-i-r-a-n+4*h)/(i-r+a-n)]);break;case 13:u.push([t-.75-.25*(n+r-2*h)/(r-n),e-.25-.25*(-a-n+2*h)/(a-n)]);break;case 14:u.push([t-.25-.25*(-n-r+2*h)/(n-r),e-.25-.25*(-i-r+2*h)/(i-r)]);break;case 15:u.push([t-.5,e-.5])}},cell:function(t,e,r,n,i,a,o,s,l){i?s.push([t,e]):s.push([e,t])}});return function(t,e){var r=[],i=[];return n(t,r,i,e),{positions:r,cells:i}}}};var o={}},{"ndarray-extract-contour":251,"zero-crossings":318}],303:[function(t,e,r){(function(r){(function(){"use strict";e.exports=function 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p=h.prototype;p.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},p.getDistanceLimits=function(t){var e=this.radius.bounds[0];return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},p.recalcMatrix=function(t){this.center.curve(t),this.up.curve(t),this.right.curve(t),this.radius.curve(t),this.angle.curve(t);for(var e=this.computedUp,r=this.computedRight,n=0,i=0,a=0;a<3;++a)i+=e[a]*r[a],n+=e[a]*e[a];var l=Math.sqrt(n),u=0;for(a=0;a<3;++a)r[a]-=e[a]*i/n,u+=r[a]*r[a],e[a]/=l;var f=Math.sqrt(u);for(a=0;a<3;++a)r[a]/=f;var h=this.computedToward;o(h,e,r),s(h,h);var p=Math.exp(this.computedRadius[0]),d=this.computedAngle[0],m=this.computedAngle[1],g=Math.cos(d),v=Math.sin(d),y=Math.cos(m),x=Math.sin(m),b=this.computedCenter,_=g*y,w=v*y,T=x,k=-g*x,A=-v*x,M=y,S=this.computedEye,E=this.computedMatrix;for(a=0;a<3;++a){var L=_*r[a]+w*h[a]+T*e[a];E[4*a+1]=k*r[a]+A*h[a]+M*e[a],E[4*a+2]=L,E[4*a+3]=0}var C=E[1],P=E[5],I=E[9],O=E[2],z=E[6],D=E[10],R=P*D-I*z,F=I*O-C*D,B=C*z-P*O,N=c(R,F,B);R/=N,F/=N,B/=N,E[0]=R,E[4]=F,E[8]=B;for(a=0;a<3;++a)S[a]=b[a]+E[2+4*a]*p;for(a=0;a<3;++a){u=0;for(var j=0;j<3;++j)u+=E[a+4*j]*S[j];E[12+a]=-u}E[15]=1},p.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r};var d=[0,0,0];p.rotate=function(t,e,r,n){if(this.angle.move(t,e,r),n){this.recalcMatrix(t);var i=this.computedMatrix;d[0]=i[2],d[1]=i[6],d[2]=i[10];for(var o=this.computedUp,s=this.computedRight,l=this.computedToward,c=0;c<3;++c)i[4*c]=o[c],i[4*c+1]=s[c],i[4*c+2]=l[c];a(i,i,n,d);for(c=0;c<3;++c)o[c]=i[4*c],s[c]=i[4*c+1];this.up.set(t,o[0],o[1],o[2]),this.right.set(t,s[0],s[1],s[2])}},p.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=(Math.exp(this.computedRadius[0]),i[1]),o=i[5],s=i[9],l=c(a,o,s);a/=l,o/=l,s/=l;var u=i[0],f=i[4],h=i[8],p=u*a+f*o+h*s,d=c(u-=a*p,f-=o*p,h-=s*p),m=(u/=d)*e+a*r,g=(f/=d)*e+o*r,v=(h/=d)*e+s*r;this.center.move(t,m,g,v);var y=Math.exp(this.computedRadius[0]);y=Math.max(1e-4,y+n),this.radius.set(t,Math.log(y))},p.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},p.setMatrix=function(t,e,r,n){var a=1;"number"==typeof r&&(a=0|r),(a<0||a>3)&&(a=1);var o=(a+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var s=e[a],l=e[a+4],f=e[a+8];if(n){var h=Math.abs(s),p=Math.abs(l),d=Math.abs(f),m=Math.max(h,p,d);h===m?(s=s<0?-1:1,l=f=0):d===m?(f=f<0?-1:1,s=l=0):(l=l<0?-1:1,s=f=0)}else{var g=c(s,l,f);s/=g,l/=g,f/=g}var v,y,x=e[o],b=e[o+4],_=e[o+8],w=x*s+b*l+_*f,T=c(x-=s*w,b-=l*w,_-=f*w),k=l*(_/=T)-f*(b/=T),A=f*(x/=T)-s*_,M=s*b-l*x,S=c(k,A,M);if(k/=S,A/=S,M/=S,this.center.jump(t,q,G,Y),this.radius.idle(t),this.up.jump(t,s,l,f),this.right.jump(t,x,b,_),2===a){var E=e[1],L=e[5],C=e[9],P=E*x+L*b+C*_,I=E*k+L*A+C*M;v=R<0?-Math.PI/2:Math.PI/2,y=Math.atan2(I,P)}else{var O=e[2],z=e[6],D=e[10],R=O*s+z*l+D*f,F=O*x+z*b+D*_,B=O*k+z*A+D*M;v=Math.asin(u(R)),y=Math.atan2(B,F)}this.angle.jump(t,y,v),this.recalcMatrix(t);var N=e[2],j=e[6],U=e[10],V=this.computedMatrix;i(V,e);var H=V[15],q=V[12]/H,G=V[13]/H,Y=V[14]/H,W=Math.exp(this.computedRadius[0]);this.center.jump(t,q-N*W,G-j*W,Y-U*W)},p.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},p.idle=function(t){this.center.idle(t),this.up.idle(t),this.right.idle(t),this.radius.idle(t),this.angle.idle(t)},p.flush=function(t){this.center.flush(t),this.up.flush(t),this.right.flush(t),this.radius.flush(t),this.angle.flush(t)},p.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},p.lookAt=function(t,e,r,n){this.recalcMatrix(t),e=e||this.computedEye,r=r||this.computedCenter;var 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plot_miRTalk_heatmap(object = obj)
+
+ +

+# use condition parameter to plot results under different conditions separately

plot_miRTalk_sankey()

@@ -4605,6 +4667,8 @@

plot_miRTalk_sankey()

  • 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 @@ -4615,8 +4679,8 @@

    plot_miRTalk_sankey()

    type, whose length must be equal to celltype, Or use “NO” to cancel it
  • show_type which type of miRNAs to show, “number”, -“activity”, or “score” for sum of inferred miRNAs number and activity, -respectively, or “prob” for max probability. Default is “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
    • @@ -4627,9 +4691,11 @@

    plot_miRTalk_sankey()

  • ... parameters pass to networkD3::sankeyNetwork
-
plot_miRTalk_sankey(object = obj)
-
- +
plot_miRTalk_sankey(object = obj)
+
+ +

+# use condition parameter to plot results under different conditions separately

plot_miR_heatmap()

@@ -4637,6 +4703,8 @@

plot_miR_heatmap()

  • 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 @@ -4650,9 +4718,41 @@

    plot_miR_heatmap()

  • ... parameters pass to heatmaply::heatmaply, e.g., grid_color, grid_width
-
plot_miR_heatmap(object = obj)
-
- +
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()

@@ -4660,6 +4760,8 @@

plot_miR_bubble()

  • 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 @@ -4672,8 +4774,10 @@

    plot_miR_bubble()

    viridis::scale_color_viridis, can be “A”, “B”, “C”, “D”, “E”, “F”, “G”, “H”. Default is “D”.
-
plot_miR_bubble(object = obj)
-

+
plot_miR_bubble(object = obj)
+

+

+# use condition parameter to plot results under different conditions separately

plot_miR2tar_chord()

@@ -4681,6 +4785,8 @@

plot_miR2tar_chord()

  • 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 @@ -4695,16 +4801,16 @@

    plot_miR2tar_chord()

    type. Default is “circle”, “big.arrow” for big arrow, “triangle” for triangle, “ellipse” for ellipse, “curved” for curved. Details see circlize::chordDiagram
    • -
  • show_type which type of miRNAs to show, “prob” or -“score” for inferred miRNAs-target interactions. Default is “prob”
  • 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 = "Stromal")
-

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

+

+# use condition parameter to plot results under different conditions separately

plot_miR2tar_circle()

@@ -4712,6 +4818,8 @@

plot_miR2tar_circle()

  • 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 @@ -4720,17 +4828,22 @@

    plot_miR2tar_circle()

    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 = "Stromal")
-

-

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

+
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()

@@ -4738,14 +4851,14 @@

plot_miR2tar_heatmap()

  • 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
    • -
  • show_type which type of miRNAs to show, “prob” or -“score” for inferred miRNAs-target interactions. Default is “prob”
  • text_size Size of text labels. Default is 3
  • if_horizontal Whether to plot with the horizontal direction. Default is TRUE
    • @@ -4755,165 +4868,172 @@

    plot_miR2tar_heatmap()

  • ... parameters pass to heatmaply::heatmaply, e.g., grid_color
-
plot_miR2tar_heatmap(obj, celltype_sender = "Tumor", celltype_receiver = "Stromal", grid_color = "black")
-
- +
plot_miR2tar_heatmap(obj, celltype_sender = "Tumor", celltype_receiver = "Bcell", grid_color = "black")
+
+ +

+# use condition parameter to plot results under different conditions separately

Note

-
-

To get pathways of target genes, use -get_pathways()

-

Parameters of get_pathways() see below:

+
+

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()
    • -
  • pathways A data.frame of the system data containing -gene-gene interactions and pathways from KEGG and Reactome for ‘Human’, -‘Mouse’ or ‘Rat’. see demo_pathways()
    • +
  • 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.
-
# demo_pathways
-demo_pathways()
-#>      src dest                    pathway species
-#> 1 CDKN1A CDK2      p53 signaling pathway   Human
-#> 2 CDKN1A CDK4      p53 signaling pathway   Human
-#> 3   CDK2 TP53      p53 signaling pathway   Human
-#> 4   Akt1 Atf2 PI3K-Akt signaling pathway   Mouse
-#> 5 Tcirg1 Ppa1  Oxidative phosphorylation     Rat
-
-# get pathways
-obj_pathways <- get_pathways(obj, pathways = pathways)
-head(obj_pathways)
-#>     target_genes        id                      pathway species
-#> 330        ALDOA hsa:00010 Glycolysis / Gluconeogenesis   Human
-#> 331        ALDOA hsa:00010 Glycolysis / Gluconeogenesis   Human
-#> 332        ALDOA hsa:00010 Glycolysis / Gluconeogenesis   Human
-#> 333        ALDOA hsa:00010 Glycolysis / Gluconeogenesis   Human
-#> 335        ALDOA hsa:00010 Glycolysis / Gluconeogenesis   Human
-#> 337        ALDOA hsa:00010 Glycolysis / Gluconeogenesis   Human
+
# 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"
-
-

To get GO terms of target genes, use get_gene2go()

-

Parameters of get_gene2go() see below:

+
+

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()
    • -
  • gene2go A data.frame of the system data containing GO -terms for ‘Human’, ‘Mouse’ or ‘Rat’. see -demo_gene2go()
    • -
  • if_show_negative Whether to show the results with -negative regulation. Default is TRUE.
-
# demo_gene2go
-demo_gene2go()
-#>   symbol              GO_term species
-#> 1   A1BG   molecular_function   Human
-#> 2   A1BG extracellular region   Human
-#> 3   A1BG  extracellular space   Human
-#> 4   Zzz3          DNA binding   Mouse
-#> 5    Zyx    metal ion binding     Rat
-
-# get pathways
-obj_gene2go <- get_gene2go(obj, gene2go = gene2go)
-head(obj_gene2go)
-#>      symbol evidence   qualifier
-#> 2851 DNAJB6      IDA involved_in
-#> 2854 DNAJB6      IEA involved_in
-#> 2865 DNAJB6      IDA involved_in
-#> 2866 DNAJB6      IMP involved_in
-#> 6714   NME6      IBA involved_in
-#> 6715   NME6      IDA involved_in
-#>                                                                                        GO_term
-#> 2851 negative regulation of cysteine-type endopeptidase activity involved in apoptotic process
-#> 2854                                       negative regulation of transcription, DNA-templated
-#> 2865                                            negative regulation of inclusion body assembly
-#> 2866                                            negative regulation of inclusion body assembly
-#> 6714                                                        negative regulation of cell growth
-#> 6715                                                        negative regulation of cell growth
-#>        PubMed category species
-#> 2851 11896048  Process   Human
-#> 2854        -  Process   Human
-#> 2865 21231916  Process   Human
-#> 2866 20889486  Process   Human
-#> 6714 21873635  Process   Human
-#> 6715 10618642  Process   Human
-
-
-

To plot the sparse expression of miRNA genes in scRNA-seq, you can -use the Nebulosa::plot_density()

-
Nebulosa::plot_density(object = obj_seurat, features = "MIR24-2")
-
sessionInfo()
-#> R version 4.1.1 (2021-08-10)
-#> Platform: x86_64-w64-mingw32/x64 (64-bit)
-#> Running under: Windows 10 x64 (build 19045)
-#> 
-#> Matrix products: default
-#> 
-#> locale:
-#> [1] LC_COLLATE=Chinese (Simplified)_China.936 
-#> [2] LC_CTYPE=Chinese (Simplified)_China.936   
-#> [3] LC_MONETARY=Chinese (Simplified)_China.936
-#> [4] LC_NUMERIC=C                              
-#> [5] LC_TIME=Chinese (Simplified)_China.936    
-#> 
-#> attached base packages:
-#> [1] parallel  stats     graphics  grDevices utils     datasets  methods  
-#> [8] base     
-#> 
-#> other attached packages:
-#> [1] miRTalk_1.0       doParallel_1.0.17 iterators_1.0.13  foreach_1.5.1    
-#> 
-#> loaded via a namespace (and not attached):
-#>   [1] circlize_0.4.13       plyr_1.8.6            igraph_1.2.7         
-#>   [4] lazyeval_0.2.2        sp_1.4-5              splines_4.1.1        
-#>   [7] crosstalk_1.2.0       listenv_0.8.0         scattermore_0.7      
-#>  [10] ggplot2_3.3.6         digest_0.6.28         ca_0.71.1            
-#>  [13] htmltools_0.5.2       viridis_0.6.2         fansi_0.5.0          
-#>  [16] magrittr_2.0.1        tensor_1.5            cluster_2.1.2        
-#>  [19] ROCR_1.0-11           limma_3.50.0          globals_0.14.0       
-#>  [22] graphlayouts_0.7.1    matrixStats_0.61.0    spatstat.sparse_3.0-0
-#>  [25] prettyunits_1.1.1     rmdformats_1.0.3      colorspace_2.0-2     
-#>  [28] ggrepel_0.9.1         xfun_0.30             dplyr_1.0.7          
-#>  [31] crayon_1.4.2          jsonlite_1.7.2        progressr_0.9.0      
-#>  [34] spatstat.data_3.0-0   survival_3.2-11       zoo_1.8-9            
-#>  [37] glue_1.4.2            polyclip_1.10-0       registry_0.5-1       
-#>  [40] gtable_0.3.0          webshot_0.5.4         leiden_0.3.9         
-#>  [43] future.apply_1.8.1    shape_1.4.6           abind_1.4-5          
-#>  [46] scales_1.1.1          pheatmap_1.0.12       DBI_1.1.1            
-#>  [49] miniUI_0.1.1.1        Rcpp_1.0.7            progress_1.2.2       
-#>  [52] viridisLite_0.4.0     xtable_1.8-4          reticulate_1.22      
-#>  [55] spatstat.core_2.3-0   datawizard_0.6.2      htmlwidgets_1.5.4    
-#>  [58] httr_1.4.2            RColorBrewer_1.1-2    ellipsis_0.3.2       
-#>  [61] Seurat_4.1.1          ica_1.0-2             pkgconfig_2.0.3      
-#>  [64] farver_2.1.0          sass_0.4.0            uwot_0.1.10          
-#>  [67] deldir_1.0-6          utf8_1.2.2            labeling_0.4.2       
-#>  [70] tidyselect_1.1.1      rlang_0.4.12          reshape2_1.4.4       
-#>  [73] later_1.3.0           munsell_0.5.0         tools_4.1.1          
-#>  [76] generics_0.1.1        ggridges_0.5.3        evaluate_0.14        
-#>  [79] stringr_1.4.0         fastmap_1.1.0         heatmaply_1.4.0      
-#>  [82] yaml_2.2.1            goftest_1.2-3         knitr_1.36           
-#>  [85] fitdistrplus_1.1-6    tidygraph_1.2.0       purrr_0.3.4          
-#>  [88] RANN_2.6.1            dendextend_1.16.0     ggraph_2.0.5         
-#>  [91] pbapply_1.5-0         future_1.23.0         nlme_3.1-152         
-#>  [94] mime_0.12             correlation_0.8.3     compiler_4.1.1       
-#>  [97] plotly_4.10.0         png_0.1-7             spatstat.utils_3.0-1 
-#> [100] tibble_3.1.5          tweenr_1.0.2          bslib_0.3.1          
-#> [103] stringi_1.7.5         highr_0.9             rgeos_0.5-9          
-#> [106] lattice_0.20-44       Matrix_1.3-4          vctrs_0.3.8          
-#> [109] pillar_1.6.4          lifecycle_1.0.1       networkD3_0.4        
-#> [112] spatstat.geom_3.0-3   lmtest_0.9-38         jquerylib_0.1.4      
-#> [115] GlobalOptions_0.1.2   RcppAnnoy_0.0.19      insight_0.18.5       
-#> [118] data.table_1.14.2     cowplot_1.1.1         irlba_2.3.3          
-#> [121] seriation_1.4.0       httpuv_1.6.3          patchwork_1.1.1      
-#> [124] R6_2.5.1              bookdown_0.25         promises_1.2.0.1     
-#> [127] TSP_1.2-1             KernSmooth_2.23-20    gridExtra_2.3        
-#> [130] parallelly_1.28.1     codetools_0.2-18      MASS_7.3-54          
-#> [133] assertthat_0.2.1      SeuratObject_4.1.0    sctransform_0.3.3    
-#> [136] bayestestR_0.13.0     hms_1.1.1             mgcv_1.8-36          
-#> [139] grid_4.1.1            rpart_4.1-15          tidyr_1.1.4          
-#> [142] rmarkdown_2.13        Rtsne_0.15            ggforce_0.3.3        
-#> [145] shiny_1.7.1
+
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