corr

README.md

@@ -440,6 +440,11 @@ dev.off()
 </p>
 </details>
 
+
+![f3_Common_net](Figures/f3_Common_net.jpg)
+**Supl.Figure2:** 
+
+
 ## Session Info
 
 <details><summary>Code</summary> 

Scripts/Minors.R

@@ -77,4 +77,282 @@ scatter_gene<-function (object, features, assay = "RNA", reduction = "umap",
     })
     p1 <- ggarrange(plotlist = results, ncol = ncol, nrow = nrow)
     return(p1)
-}
+}
\ No newline at end of file
+
+
+
+#' Build Correlation Network
+#'
+#' @author Dimitrios Kyriakis
+#' @export
+#' @param dataset : A Matrix with columns the nodes
+#' @param regulated : A vector with "UP Regulated" and "DOWN Regulated".
+#' @param cor_thres : Correlation Threshold
+#' @param corr.method : What kind of correlations should be computed? Default is "pearson", but "spearman" and "kendall" are also supported.
+#' @return Network
+#' @examples cor_net(heat_matrix)
+#'
+cor_net <- function (dataset,regulated=NULL,cor_thres=0.5,corr.method="pearson"){
+    require(tidyverse)
+    require(corrr)
+    require(igraph)
+    require(ggraph)
+
+    net_mat_plot <- as.matrix(dataset)
+
+    # # Create a tidy data frame of correlations
+    # d <- as.data.frame(t(net_mat_plot))
+    # tidy_cors <- d %>%
+    #     correlate(method=corr.method) %>%
+    #     stretch()
+
+
+    tryCatch(
+        {
+            # Convert correlations stronger than some value
+            # to an undirected graph object
+            library("Hmisc")
+            res2 <- rcorr(t(as.matrix(dataset_r)))
+            flattenCorrMatrix <- function(cormat, pmat) {
+            ut <- upper.tri(cormat)
+            data.frame(
+                row = rownames(cormat)[row(cormat)[ut]],
+                column = rownames(cormat)[col(cormat)[ut]],
+                cor  =(cormat)[ut],
+                p = pmat[ut]
+                )
+            }
+            return_cor <-flattenCorrMatrix(res2$r, res2$P)
+            corner(return_cor)
+
+            graph_cors <- return_cor %>%
+                dplyr::filter(abs(cor) > cor_thres)%>%
+                dplyr::filter(abs(p) < 0.05) %>%
+                graph_from_data_frame(directed = FALSE)
+                
+            # graph_cors <- tidy_cors %>%
+            #     dplyr::filter(abs(r) > cor_thres) %>%
+            #     graph_from_data_frame(directed = FALSE)
+
+            G2 <-graph_cors
+            return(G2)
+        },
+        error=function(cond){
+            return("No Edge Found. Try to decrease MMHC threshold")
+        }
+    )
+
+}
+
+
+
+
+
+
+
+
+
+#' Build Regulatory Network Using Genie3
+#'
+#' @author Dimitrios Kyriakis
+#' @export
+#' @param dataset : A Matrix with columns the nodes
+#' @param regulated : A vector with "UP Regulated" and "DOWN Regulated".
+#' @param weight.threshold : Weight Threshold (less adds more edges)
+#' @param nCores : Number of cores to use for parallel computing. Default: 4.
+#' @param nTrees : Number of trees in an ensemble for each target gene. Default: 2000.
+#' @return Network
+#' @examples genie3_net(dataset,regulated=NULL,weight.threshold=0.05,title="",nCores=4,nTrees=2000)
+#'
+genie3_net <- function (dataset,regulated=NULL,weight.threshold=0.05,title="",nCores=4,nTrees=2000){
+    require(GENIE3)
+    require(igraph)
+    require(RCy3)
+    require(Rgraphviz)
+    net_mat_plot <- as.matrix(dataset)
+    tryCatch(
+        {
+
+        weightMat <- GENIE3(net_mat_plot, nCores=nCores, verbose=TRUE,nTrees=nTrees)
+        link.list <- getLinkList(weightMat,threshold=weight.threshold)
+        edge_listsi <- link.list[!duplicated(link.list),]
+
+        Gsi <- graph.data.frame(edge_listsi,directed = FALSE)
+        Asi <- get.adjacency(Gsi,sparse = F,attr = "weight",type = "both")
+        G2 <- graph.adjacency(Asi,mode = "undirected",weighted = T)
+
+        return(G2)
+    },
+    error=function(cond){
+        return("Debugonce()")
+    }
+    )
+}
+
+
+
+
+
+#' Build Network
+#'
+#' @author Dimitrios Kyriakis
+#' @export
+#' @param dataset : A Matrix with columns the nodes
+#' @param regulated : A vector with "UP Regulated" and "DOWN Regulated".
+#' @param threshold : Threshold (aplha parameter for mmhc/h2pc, pvalue for corellation, weeight for genie3)
+#' @param nCores : Parallel (aplied only for genie3)
+#' @param nTrees : Number of trees for Random forest (aplied only for genie3)
+#' @param corr.method : What kind of correlations should be computed? Default is "pearson", but "spearman" and "kendall" are also supported.
+#' @param title : Name of the pdf
+#' @return Network
+#' @examples ics_net(dataset,regulated=NULL,threshold=0.01,nCores=4,nTrees=2000,title="Day09",method="mmhc",corr.method="pearson")
+#'
+ics_net <- function (dataset,regulated=NULL,threshold=0.05,nCores=4,nTrees=2000,title="",method="mmhc",corr.method="pearson"){
+    require(bnlearn)
+    require(bnviewer)
+    require(igraph)
+
+    labelx <- paste0(Sys.Date(),"_",title)
+    graphics.off()
+    tryCatch(
+        {
+            # ===== MMPC ======================
+            if(method%in%c("pc","aracne","mmhc")){
+                net_mat_plot <- as.data.frame(t(as.matrix(dataset)))
+                if(method=="pc"){
+                    temp_network <- bnlearn::h2pc(net_mat_plot,restrict.args=list("alpha"=threshold))
+                }else if (method=="aracne"){
+                    temp_network <- bnlearn::aracne(net_mat_plot)
+                }else{
+                    temp_network <- bnlearn::mmhc(net_mat_plot,restrict.args=list("alpha"=threshold))
+                }
+                G1 <- bnviewer::bn.to.igraph(temp_network)
+            }else{
+                if(method=="correlation"){
+                    G1 <- ICSWrapper::cor_net(dataset=dataset,regulated=regulated,cor_thres=threshold,corr.method=corr.method)
+                }else if(method=="genie3"){
+                    G1 <- ICSWrapper::genie3_net(dataset=dataset,regulated=regulated,weight.threshold=threshold,nCores=nCores,nTrees=nTrees)
+                }else{
+                    print("Not available method")
+                    return()
+                }
+            }
+
+
+
+            G2 <- igraph::simplify(G1)
+            if(method=="aracne"){
+                G2 <- as.undirected(G2)
+
+            }
+            if(!method%in%c("pc","mmhc")){
+                clusterlouvain <- cluster_louvain(G2)
+            }else{
+                G3 <- as.undirected(G2)
+                clusterlouvain <- cluster_louvain(G3)
+            }
+
+            V(G2)$cex <- 0.11
+            V(G2)$label.cex <-0.3
+            G3 <- G2
+            Degree_net <- igraph::degree(G2, mode = "in")
+            Between_net <- igraph::betweenness(G2)
+
+            pdf(paste0(labelx,"_Net_",method,".pdf"))
+            # =================================== UP AND DOWN REGULATION ==========================================
+            if(!is.null(regulated)){
+                regulated <- regulated[names(regulated)%in%vertex_attr(G2)$name]
+                Color_rest <-c("skyblue","pink")[as.factor(regulated)]
+                set.seed(1234)  # set seed to make the layout reproducible
+                V(G2)$color <- Color_rest
+                plot(G2, edge.arrow.size=0.25, main="Regulated",cex=0.1,main=labelx)
+                legend("bottomleft",bty = "n",
+                       legend=levels(as.factor(regulated)),
+                       fill= c("skyblue","pink"), horiz=F)
+            }else{
+                Color_rest <-c("skyblue")
+            }
+            set.seed(1234)  # set seed to make the layout reproducible
+            V(G2)$color <- Color_rest
+            
+            set.seed(1234)
+            plot(G2, edge.arrow.size=0.25, main="Gene Network",cex=0.1,main=labelx)
+            set.seed(1234)
+            plot(clusterlouvain,G2, edge.arrow.size=0.25, main="Gene Network",cex=0.1,main=labelx )
+            # -----------------------------------------------------------------------------------------------------
+
+
+            # =================================== Betweenness CENTRALITY ==========================================
+            G4 <- G2
+            V(G4)$color <-Color_rest
+            #Edge Options: Color
+            E(G4)$color <- "grey"
+            set.seed(1234)  # set seed to make the layout reproducible
+            print(Between_net)
+            if(length(unique(Between_net))==1){
+                plot(G4,cex=0.1, edge.arrow.size=0.25,main="Betweeness Centrality")
+            }else{
+                V(G4)$size=ICSWrapper::rescale_ics(Between_net) #because we have wide range, I am dividing by 5 to keep the high in-degree nodes from overshadowing everything else.
+                plot(G4, edge.arrow.size=0.25,main="Betweeness Centrality")
+
+            }
+
+            # -----------------------------------------------------------------------------------------------------
+
+
+
+
+            ## ====================================== DEGREE ======================================================
+            G5 <- G2
+            #Node or Vetex Options: Size and Color
+            V(G5)$color <- Color_rest
+            #Edge Options: Color
+            E(G5)$color <- "grey"
+            #Plotting, Now Specifying an arrow size and getting rid of arrow heads
+            #We are letting the color and the size of the node indicate the directed nature of the graph
+            set.seed(1234)  # set seed to make the layout reproducible
+            print(Degree_net)
+            if(length(unique(Degree_net))==1){
+                plot(G5,cex=0.1, edge.arrow.size=0.25,main="Degree In")
+            }else{
+                V(G5)$size=ICSWrapper::rescale_ics(Degree_net) #because we have wide range, I am dividing by 5 to keep the high in-degree nodes from overshadowing everything else.
+                plot(G5, edge.arrow.size=0.25,main="Degree In")#, vertex.label = NA
+            }
+            dev.off()
+            # -----------------------------------------------------------------------------------------------------
+            graphics.off()
+
+            # ====================================== DATA FRAME ===================================================
+            if(!method%in%c("pc","mmhc")){
+                if(!is.null(regulated)){
+                    df_G2 <- data.frame(Gene=vertex_attr(G2)$name,Cluster=clusterlouvain$membership,Degree=Degree_net,BTC=Between_net,Regulated=regulated,Color=vertex_attr(G2)$color)
+                }else{
+                    df_G2 <- data.frame(Gene=vertex_attr(G2)$name,Cluster=clusterlouvain$membership,Degree=Degree_net,BTC=Between_net,Color=vertex_attr(G2)$color)
+                }
+            }else{
+                if(!is.null(regulated)){
+                    df_G2 <- data.frame(Gene=vertex_attr(G2)$name,Degree=Degree_net,BTC=Between_net,Regulated=regulated,Color=vertex_attr(G2)$color)
+                }else{
+                    df_G2 <- data.frame(Gene=vertex_attr(G2)$name,Degree=Degree_net,BTC=Between_net,Color=vertex_attr(G2)$color)
+                }
+            }
+            write.table( df_G2,paste0(labelx,"_",method,".tsv"), row.names=FALSE,sep="\t")
+            graphics.off()
+            # -----------------------------------------------------------------------------------------------------
+            return(list("graph"=G3,"df"=df_G2))
+    },
+    error=function(cond){
+        return("Try exception gave an error. Check the treshold. Or use debugonce(ics_net) to check where the error comes from and report it")
+    }
+    )
+
+}
+
+
+
+
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