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Author: zhangjunpeng411

CSmiRsyn.R

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+## Function for discovering cell-specific miRNA-miRNA positive correlation
+# miR1 and miR2: Gene expression values of two miRNAs in single cells
+# cell_index: Index of single cells
+# boxsize: Size of neighborhood (0.1 in default)
+# interp_betw_point: The number of interpolation points between each cell (5 in default), interp_betw_point = 0 is used to compute
+# the normalized statistic of edge gx-gy in large number of cells (more than 100)
+# bootstrap_num: The number of bootstrapping for interpolating pseudo-cells 
+# Output: res is a list of cell-specific miRNA-miRNA positive correlation
+CSmiRsyn_edge_bootstrap <- function(miR1, miR2, cell_index, boxsize = 0.1, bootstrap_betw_point = 5, bootstrap_num = 100) {
+
+    cell_num <- length(miR1)
+    set.seed(123)
+    bootstrap_sample <- lapply(seq(bootstrap_num), function(i) sample(seq(cell_num), bootstrap_betw_point * (cell_num - 1), replace = TRUE))
+    miR1_bootstrap <- lapply(seq(bootstrap_num), function(i) c(miR1, miR1[bootstrap_sample[[i]]]))
+    miR2_bootstrap <- lapply(seq(bootstrap_num), function(i) c(miR2, miR2[bootstrap_sample[[i]]]))
+    res <- do.call(pmedian, lapply(seq(bootstrap_num), 
+	                           function(k) csn_edge(miR1_bootstrap[[k]], 
+	                           miR2_bootstrap[[k]], 
+	                           boxsize = boxsize)[seq(cell_num)]))   
+    
+    return(res[cell_index])
+}
+
+## Identifying cell-specific miRNA synergistic network
+CSmiRsyn_net <- function(miRTarget, cell_index, miRExp, mRExp, minSharedmR = 1, p.value.cutoff = 0.05) {
+    
+    miRTarget <- as.matrix(miRTarget)    
+    miRExpNames <- as.matrix(colnames(miRExp))
+    
+    miR <- miRTarget[, 1]
+    mR <- miRTarget[, 2]
+
+    miRSym <- unique(miR)
+    mRSym <- unique(mR)
+
+    m2 <- length(miRSym)
+
+    # Initialize variables
+    miRInt <- matrix(NA, m2 * (m2 - 1)/2, 2)
+    C <- matrix(NA, m2 * (m2 - 1)/2, 3)
+
+    for (i in seq_len(m2 - 1)) {
+        for (j in seq(i + 1, m2)) {
+
+            Interin1 <- miRTarget[which(miRTarget[, 1] %in% miRSym[i]), 2]
+            Interin2 <- miRTarget[which(miRTarget[, 1] %in% miRSym[j]), 2]
+
+            M1 <- length(Interin1)
+            M2 <- length(Interin2)
+            M3 <- length(intersect(Interin1, Interin2))
+            M4 <- length(mRSym)
+            M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
+
+            if (M3 >= minSharedmR & M5 < p.value.cutoff) {
+
+                miRInt[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- miRSym[i]
+                miRInt[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- miRSym[j]
+
+                miRExpIdx1 <- which(miRExpNames %in% miRSym[i])
+                miRExpIdx2 <- which(miRExpNames %in% miRSym[j])
+		
+                # Calculate cell-specific correlation of each miRNA-miRNA pair		
+                M6 <- CSmiRsyn_edge_bootstrap(miRExp[, miRExpIdx1], miRExp[, miRExpIdx2], cell_index)
+		M7 <- pnorm(-M6)
+                
+                C[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- M5
+                C[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- M6
+		C[(i - 1) * m2 + j - sum(seq_len(i)), 3] <- M7
+                
+            }
+        }
+    }
+
+    # Extract miRNA-miRNA pairs
+    miRInt <- miRInt[which((C[, 1] < p.value.cutoff & C[, 3] < p.value.cutoff) == "TRUE"), ]
+
+    C <- C[which((C[, 1] < p.value.cutoff & C[, 3] < p.value.cutoff) == "TRUE"), ]
+
+    if (is.vector(C)) {
+        res_miRInt <- c(miRInt, C)
+        names(res_miRInt) <- c("miRNA_1", "miRNA_2", "p_value of shared mRNAs", "correlation", "p_value of correlation")
+            
+    } else {
+        res_miRInt <- cbind(miRInt, C)
+        colnames(res_miRInt) <- c("miRNA_1", "miRNA_2", "p_value of shared mRNAs", "correlation", "p_value of correlation")
+    }
+
+    return(res_miRInt)
+}
+
+## Function for calculating similarity matrix between two list of module groups
+# Module.group1: List object, the first list of module group
+# Module.group2: List object, the second list of module group
+# Output: Sim is a similarity matrix between two list of module groups
+Sim.module.group <- function(Module.group1, Module.group2){
+
+    if(class(Module.group1)!="list" | class(Module.group2)!="list") {
+    stop("Please check your input module group! The input module group should be list object! \n")
+    }
+
+    m <- length(Module.group1)
+    n <- length(Module.group2)
+    Sim <- matrix(NA, m, n)
+    
+    for (i in seq(m)){
+        for (j in seq(n)){	    
+	    overlap_interin <- length(intersect(Module.group1[[i]], Module.group2[[j]]))
+	    Sim[i, j] <- overlap_interin/min(length(Module.group1[[i]]), length(Module.group2[[j]]))
+	}    
+    }
+
+    if (m < n) {        
+	GS <- mean(unlist(lapply(seq(m), function(i) Sim[i, max.col(Sim)[i]])))*m/n
+    } else if (m == n) {
+        GS <- mean(c(unlist(lapply(seq(m), function(i) Sim[i, max.col(Sim)[i]])), 
+	           unlist(lapply(seq(n), function(i) Sim[max.col(t(Sim))[i], i]))))
+    } else if (m > n) {
+        GS <- mean(unlist(lapply(seq(n), function(i) Sim[max.col(t(Sim))[i], i])))*n/m
+    }
+
+    return(GS)
+}
+
+## Function for calculating cluster coefficients in random networks
+# nodes.num: The number of nodes
+# edges.num: The number of edges
+# perm: The number of permutation
+# directed: Logical value, false or true
+# Output: Mean and std of cluster coefficients in random networks
+Random_net_clusterCoeff <- function(nodes.num, edges.num, perm = 10000, directed = FALSE) {
+    set.seed(123)
+    res <- c()
+    for (i in seq(perm)) {
+    g <- sample_pa(n = nodes.num, m = edges.num, directed = directed)
+    g <- delete_edges(g, sample(1:gsize(g), size = gsize(g) - edges.num))
+        res[i] <- transitivity(g, type="average")
+    }
+
+    return(list(mean(res), sd(res)))
+}
+