@@ -1,6 +1,6 @@

 Package: LACE

-Version: 1.3.0

-Date: 2020-10-20

+Version: 1.3.1

+Date: 2020-11-27

 Title: Longitudinal Analysis of Cancer Evolution (LACE)

 Authors@R: c(person("Daniele", "Ramazzotti", role=c("aut"),email="daniele.ramazzotti@yahoo.com",

                 comment = c(ORCID = "0000-0002-6087-2666")),

@@ -260,8 +260,8 @@ LACE <- function( D, lik_w = NULL, alpha = NULL, beta = NULL, initialization = N

     # Include Root in clones_prevalence

     clones_prevalence <- rbind(rep(NA,ncol(clones_prevalence)),clones_prevalence)

-    rownames(clones_prevalence)[1] <- "Root_0"

-    clones_prevalence["Root_0",] <- (1-colSums(clones_prevalence,na.rm=TRUE))

+    rownames(clones_prevalence)[1] <- "Root"

+    clones_prevalence["Root",] <- (1-colSums(clones_prevalence,na.rm=TRUE))

     # Compute clones' summary

     clones_summary <- list()

@@ -92,7 +92,7 @@ as.adj.matrix.unsorted <- function( B, root = FALSE ) {

     }

     return(adj_matrix)

-

+    

 }

 # Build B from an adjacency matrix where we assume clones and mutations to be both ordered

@@ -130,14 +130,17 @@ as.B <- function( adj_matrix ) {

 # check that point Q is not on segments A-B

 checkQonAB <- function( Ax, Ay, Bx, By, Qx, Qy ) {

+

     if((Qx <= max(Ax,Bx)) && (Qx >= min(Ax, Bx)) && (Qy <= max(Ay, By)) && (Qy >= min(Ay, By))) {

         return(TRUE)

     }

     return(FALSE)

+

 }

 # check segments orientation

-orientation <- function( Ax, Ay, Bx, By, Qx, Qy ) {    

+orientation <- function( Ax, Ay, Bx, By, Qx, Qy ) {

+

     res <- ((Qy - Ay) * (Bx - Qx)) - ((Qx - Ax)*(By - Qy))

     if(res > 0) {

         return(1)

@@ -148,6 +151,7 @@ orientation <- function( Ax, Ay, Bx, By, Qx, Qy ) {

     else {

         return(0)

     }

+

 }

 # check if two segments interserct

@@ -175,7 +179,7 @@ checkSegmentIntersect <- function( ax1, ay1, ax2, ay2, bx1, by1, bx2, by2 ) {

     }

     return(FALSE)

-

+    

 }

 #' Compute mutation distance among variants from LACE corrected genotype and use it to perform hierarchical clustering.

@@ -195,7 +199,7 @@ compute.mutation.distance <- function( inference ) {

     mutation_distance <- dist(t(inference$corrected_genotype),method="euclidean")

     mutation_distance <- list(distance_matrix=as.matrix(mutation_distance),hierarchical_clustering=hclust(mutation_distance,method="complete"))

     return(mutation_distance)

-

+    

 }

 #' Compute error rates for the considered variants comparing observed data to LACE corrected genotype.

@@ -222,23 +226,23 @@ compute.variants.error.rates <- function( D, inference ) {

     for(i in 1:length(D)) {

         observed_genotype <- rbind(observed_genotype,D[[i]])

     }

-

+    

     for(i in rownames(variants_error_rates)) {

         fn <- length(which(observed_genotype[,i]==0&corrected_genotype[,i]==1))

         fp <- length(which(observed_genotype[,i]==1&corrected_genotype[,i]==0))

         variants_error_rates[i,"Percentage_False_Positives"] <- fp/nrow(observed_genotype)

         variants_error_rates[i,"Percentage_False_Negatives"] <- fn/nrow(observed_genotype)

     }

-

+    

     return(variants_error_rates)

-

+    

 }

 recursiveDescend <- function( descend ) {

-    # Check next node, 

-    next_v <- which(descend$adjM[descend$row_v,] == 1)

-    #if not than return

+    # Check next node,

+    next_v <- which(descend$adjM[descend$row_v,] > 0)

+    # If not than return

     if(length(next_v)==0) {

         descend$max_deep <- max(descend$l_path)

         return(descend)

@@ -249,18 +253,22 @@ recursiveDescend <- function( descend ) {

             descend$row_v = v

             descend$ind_lPath = descend$ind_lPath + 1

             descend$l_path[descend$ind_lPath] = curr_level + 1

+            descend$mut_list <- c(descend$mut_list, as.numeric(v))

             descend <- recursiveDescend(descend = descend)

         }

     } else  {

         descend$l_path[descend$ind_lPath] = descend$l_path[descend$ind_lPath] + 1

         descend$row_v = next_v

+        descend$mut_list <- c(descend$mut_list, as.numeric(next_v))

         descend <- recursiveDescend(descend = descend)

     }

     descend$max_deep <- max(descend$l_path)

     return(descend)

+

 }

-recursiveLongitudinalLayout <- function( idx_Vc, Xc, Yc, cl_df, adjMatrix_base, adjMatrix_overall, mut_TP, labels_show ) {

+

+recursiveLongitudinalLayout <- function( idx_Vc, Xc, Yc, cl_df, adjMatrix_base, adjMatrix_overall, mut_TP, labels_show, label_offset) {

     cl_vertex_idx_Vc <- which(cl_df$cl_vertex$names == colnames(adjMatrix_overall)[idx_Vc])

@@ -268,59 +276,62 @@ recursiveLongitudinalLayout <- function( idx_Vc, Xc, Yc, cl_df, adjMatrix_base,

     cl_df$cl_vertex$coord.x[cl_vertex_idx_Vc] <- Xc

     cl_df$cl_vertex$coord.y[cl_vertex_idx_Vc] <- Yc

-    idx_next_v <- which(adjMatrix_overall[idx_Vc,] != "")

-    

+    idx_next_v <- which(adjMatrix_overall[idx_Vc,] != 0)

     if(length(idx_next_v) == 0) {

         return(cl_df)

     } else if(length(idx_next_v) > 1){

-        # BRANCH

         deep <- c()

-        

-        for(v in idx_next_v){

-            if(adjMatrix_overall[idx_Vc,v] == "persistence") {

-                deep <- c(deep, 0)

-            } else {

-                descend = list(

-                    adjM = adjMatrix_base,

-                    l_path = c(1),

-                    ind_lPath = 1,

-                    row_v = which(colnames(adjMatrix_base) == cl_df$cl_vertex$last_mutation[cl_df$cl_vertex$names == colnames(adjMatrix_overall)[v]])

-                )

-                deep_tmp <- recursiveDescend(descend)

-                deep <- c(deep, deep_tmp$max_deep)

-                

-            }

+        for(v in idx_next_v) {

+            descend = list(

+                adjM = adjMatrix_overall,

+                l_path = c(1),

+                ind_lPath = 1,

+                mut_list = c(v),

+                row_v = v

+            )

+            deep_tmp <- recursiveDescend(descend)

+            dif_clones <- unique(cl_df$cl_vertex$clone[cl_df$cl_vertex$names %in% colnames(adjMatrix_overall)[deep_tmp$mut_list]])

+            deep <- c(deep, length(dif_clones))

         }

+        ord <- order(-cl_df$cl_vertex$TP[match(names(idx_next_v),cl_df$cl_vertex$names)], deep, decreasing = F)

-        idx_next_v <- idx_next_v[order(deep, decreasing = F)]

-        deep <- deep[order(deep, decreasing = F)]

-        #deep <- cumsum(deep)

-        

+        idx_next_v <- idx_next_v[ord]

+        deep <- deep[ord]

+

         offset_X = 0

-        offset_label = -3

         for(i in 1:length(idx_next_v)){

-            if(adjMatrix_overall[idx_Vc,idx_next_v[i]] == "persistence") {

+            

+            Vn_name <- colnames(adjMatrix_overall)[idx_next_v[i]]

+            

+            if(adjMatrix_overall[idx_Vc,idx_next_v[i]] == 2) {

                 X = Xc

-                Y = mut_TP[as.character(cl_df$cl_vertex$TP[cl_vertex_idx_Vc])] + 1

+                Y = mut_TP[as.character((cl_df$cl_vertex$TP[cl_df$cl_vertex$names == Vn_name]-1))] + 1

+                offset_X = offset_X + deep[i] - 1

             } else {

-                Y = Yc + 1

+                

+                

+                

+                if(cl_df$cl_vertex$TP[cl_vertex_idx_Vc] < cl_df$cl_vertex$TP[cl_df$cl_vertex$names == Vn_name]) {

+                    Y = mut_TP[as.character((cl_df$cl_vertex$TP[cl_df$cl_vertex$names == Vn_name]-1))] + 1

+                } else {

+                    Y = Yc + 1

+                }

                 X = (Xc + offset_X + 1)

+

                 offset_X = offset_X + deep[i]

-                

-                #offset_label = -2*offset_label 

-                Vn_name <- colnames(adjMatrix_overall)[idx_next_v[i]]

+

                 idx_row_edges = which(cl_df$cl_edges$from == cl_df$cl_vertex$names[cl_vertex_idx_Vc] & cl_df$cl_edges$to == Vn_name)

                 mutation_name <- cl_df$cl_vertex$last_mutation[cl_df$cl_vertex$names == Vn_name]

                 clone_fake_edge <- data.frame(from = cl_df$cl_edges$from[idx_row_edges],

-                                                  to = paste0('H_',mutation_name),

-                                                  type = "Parental",

-                                                  extincion = FALSE,

-                                                  label = "", 

-                                                  name = "",

-                                                  lty = 1)

+                                              to = paste0('H_',mutation_name),

+                                              type = "Parental",

+                                              extincion = FALSE,

+                                              label = "", 

+                                              name = "",

+                                              lty = 1)

                 fake_mutation_edge <- data.frame(from = paste0('H_',mutation_name),

                                                  to = mutation_name,

@@ -339,39 +350,39 @@ recursiveLongitudinalLayout <- function( idx_Vc, Xc, Yc, cl_df, adjMatrix_base,

                                                   lty = 1)

                 fake_vertex <- data.frame(names = paste0('H_',mutation_name), 

-                                              branch_level = NA,

-                                              branch = NA,

-                                              label = "",

-                                              last_mutation = "",

-                                              TP = cl_df$cl_vertex$TP[cl_df$cl_vertex$names == Vn_name],

-                                              clone = cl_df$cl_vertex$clone[cl_df$cl_vertex$names == Vn_name],

-                                              prevalance = NA,

-                                              size = 0,

-                                              size2 = 0,

-                                              shape = "none",

-                                              label.dist = 0,

+                                          branch_level = NA,

+                                          branch = NA,

+                                          label = "",

+                                          last_mutation = "",

+                                          TP = NA, 

+                                          clone = cl_df$cl_vertex$clone[cl_vertex_idx_Vc],

+                                          prevalance = NA,

+                                          size = 0,

+                                          size2 = 0,

+                                          shape = "none",

+                                          label.dist = 0,

                                           label.degree = 0,

-                                              extincion = 0,

-                                              coord.x = X,

-                                              coord.y = Yc,

-                                              color = NA)

+                                          extincion = 0,

+                                          coord.x = X,

+                                          coord.y = Yc,

+                                          color = NA)

                 mutation_vertex <- data.frame(names = mutation_name, 

                                               branch_level = NA,

                                               branch = NA,

                                               label = ifelse(test = labels_show %in% c("both", "mutations"), yes = mutation_name, no = ""),

                                               last_mutation = "",

-                                              TP = cl_df$cl_vertex$TP[cl_df$cl_vertex$names == Vn_name],

-                                              clone = cl_df$cl_vertex$clone[cl_df$cl_vertex$names == Vn_name],

+                                              TP = NA, 

+                                              clone = cl_df$cl_vertex$clone[cl_vertex_idx_Vc],

                                               prevalance = NA,

                                               size = 6,

                                               size2 = 6,

                                               shape = "square",

-                                              label.dist = 4,

+                                              label.dist = label_offset,

                                               label.degree = 0,

                                               extincion = 0,

                                               coord.x = X,

-                                              coord.y = (Yc + 0.33),

+                                              coord.y = (Yc + Y)*0.5,

                                               color = cl_df$cl_vertex$color[cl_df$cl_vertex$names == Vn_name])

                 cl_df$cl_edges <- cl_df$cl_edges[-idx_row_edges,]

@@ -380,20 +391,25 @@ recursiveLongitudinalLayout <- function( idx_Vc, Xc, Yc, cl_df, adjMatrix_base,

                 cl_df$cl_vertex <- rbind(cl_df$cl_vertex, fake_vertex, mutation_vertex)

             }

-            cl_df <- recursiveLongitudinalLayout(idx_next_v[i], X, Y, cl_df, adjMatrix_base, adjMatrix_overall, mut_TP, labels_show)

+            cl_df <- recursiveLongitudinalLayout(idx_next_v[i], X, Y, cl_df, adjMatrix_base, adjMatrix_overall, mut_TP, labels_show, label_offset)

         }

     } else {

         # NO BRANCH

-        if(adjMatrix_overall[idx_Vc,idx_next_v] == "persistence") {

+        Vn_name <- colnames(adjMatrix_overall)[idx_next_v]

+        

+        if(adjMatrix_overall[idx_Vc,idx_next_v] == 2) {

             X = Xc

-            Y = mut_TP[as.character(cl_df$cl_vertex$TP[cl_vertex_idx_Vc])] + 1

+            Y = mut_TP[as.character((cl_df$cl_vertex$TP[cl_df$cl_vertex$names == Vn_name]-1))] + 1

         } else {

-            Y = Yc + 1

+            if(cl_df$cl_vertex$TP[cl_vertex_idx_Vc] < cl_df$cl_vertex$TP[cl_df$cl_vertex$names == Vn_name]) {

+                Y = mut_TP[as.character((cl_df$cl_vertex$TP[cl_df$cl_vertex$names == Vn_name]-1))] + 1

+            } else {

+                Y = Yc + 1

+            }

             X = Xc + 1

-            Vn_name <- colnames(adjMatrix_overall)[idx_next_v]

             idx_row_edges = which(cl_df$cl_edges$from == cl_df$cl_vertex$names[cl_vertex_idx_Vc] & cl_df$cl_edges$to == Vn_name)

             mutation_name <- cl_df$cl_vertex$last_mutation[cl_df$cl_vertex$names == Vn_name]

             clone_fake_edge <- data.frame(from = cl_df$cl_edges$from[idx_row_edges],

@@ -425,8 +441,8 @@ recursiveLongitudinalLayout <- function( idx_Vc, Xc, Yc, cl_df, adjMatrix_base,

                                       branch = NA,

                                       label = "",

                                       last_mutation = "",

-                                      TP = cl_df$cl_vertex$TP[cl_df$cl_vertex$names == Vn_name],

-                                      clone = cl_df$cl_vertex$clone[cl_df$cl_vertex$names == Vn_name],

+                                      TP = NA,#cl_df$cl_vertex$TP[cl_vertex_idx_Vc],

+                                      clone = cl_df$cl_vertex$clone[cl_vertex_idx_Vc],

                                       prevalance = NA,

                                       size = 0,

                                       size2 = 0,

@@ -443,29 +459,28 @@ recursiveLongitudinalLayout <- function( idx_Vc, Xc, Yc, cl_df, adjMatrix_base,

                                           branch = NA,

                                           label = ifelse(test = labels_show %in% c("both", "mutations"), yes = mutation_name, no = ""),

                                           last_mutation = "",

-                                          TP = cl_df$cl_vertex$TP[cl_df$cl_vertex$names == Vn_name],

-                                          clone = cl_df$cl_vertex$clone[cl_df$cl_vertex$names == Vn_name],

+                                          TP = NA,#cl_df$cl_vertex$TP[cl_vertex_idx_Vc],

+                                          clone = cl_df$cl_vertex$clone[cl_vertex_idx_Vc],

                                           prevalance = NA,

                                           size = 6,

                                           size2 = 6,

                                           shape = "square",

-                                          label.dist = 4,

+                                          label.dist = label_offset,

                                           label.degree = 0,

                                           extincion = 0,

                                           coord.x = X,

-                                          coord.y = (Yc + 0.33),

+                                          coord.y = (Yc + Y)*0.5,

                                           color = cl_df$cl_vertex$color[cl_df$cl_vertex$names == Vn_name])

             cl_df$cl_edges <- cl_df$cl_edges[-idx_row_edges,]

             cl_df$cl_edges <- rbind(cl_df$cl_edges, clone_fake_edge, fake_mutation_edge, mutation_clone_edge)

             cl_df$cl_vertex <- rbind(cl_df$cl_vertex, fake_vertex, mutation_vertex)

-

-

+            

         }

-        cl_df <- recursiveLongitudinalLayout(idx_next_v, X, Y, cl_df, adjMatrix_base, adjMatrix_overall, mut_TP, labels_show)

+        cl_df <- recursiveLongitudinalLayout(idx_next_v, X, Y, cl_df, adjMatrix_base, adjMatrix_overall, mut_TP, labels_show, label_offset)

     }

     return(cl_df)

-

+    

 }

@@ -10,6 +10,7 @@

 #'                        legend_position = "topleft")

 #'

 #' @param inference Results of the inference by LACE.

+#' @param rem_unseen_leafs If TRUE (default) remove all the leafs that have never been observed (prevalence = 0 in each time point)

 #' @param show_plot If TRUE (default) output the longitudinal tree to the current graphical device.

 #' @param filename Specify the name of the file where to save the longitudinal tree. Dot or graphml formats are supported and are chosen based on the extenction of the filename (.dot or .xml).

 #' @param labels_show Specify which type of label should be placed on the tree; options are, 

@@ -20,7 +21,6 @@

 #' @param clone_labels Character vector that specifies the name of the nodes (genotypes). If it is NULL (default), nodes will be labeled as specified by "label" parameter.

 #' @param show_prev If TRUE (default) add to clones label the correspongind prevalance.

 #' @param label.cex Specify the size of the labels.

-#' @param iter_max Maximum number of iteration to be used to remove intersecting edges.

 #' @param size Specify size of the nodes. The final area is proportional with the node prevalence.

 #' @param size2 Specify the size of the second dimension of the nodes. If NULL (default), it is set equal to "size".

 #' @param tk_plot If TRUE, uses tkplot function from igraph library to plot an interactive tree. Default is FALSE.

@@ -29,6 +29,7 @@

 #' @param tp_mark_alpha Specify the alpha value of the area drawed when tp_mark = TRUE.

 #' @param legend If TRUE (default) a legend will be displayed on the plot.

 #' @param legend_position Specify the legend position.

+#' @param label_offset Move the mutation labels horizontally (default = 4)

 #' @param legend_cex Specify size of the legend text.

 #' @return An igraph object g with the longitudinal tree inferred by LACE. 

 #' @export longitudinal.tree.plot

@@ -39,13 +40,13 @@

 #' @import utils

 #'

 longitudinal.tree.plot <- function( inference, 

+                                    rem_unseen_leafs = TRUE,

                                     show_plot = TRUE, 

                                     filename = "lg_output.xml", 

                                     labels_show = "mutations", 

                                     clone_labels = NULL, 

                                     show_prev = TRUE, 

                                     label.cex = 1, 

-                                    iter_max = 100, 

                                     size = 500, 

                                     size2 = NULL, 

                                     tk_plot = FALSE, 

@@ -54,33 +55,48 @@ longitudinal.tree.plot <- function( inference,

                                     tp_mark_alpha = 0.5, 

                                     legend = TRUE, 

                                     legend_position = "topright", 

+                                    label_offset = 4, 

                                     legend_cex = 0.8 ) {

     if(is.null(size2)) {

         size2 <- size

     }

-    # 

-    # root <- list()

-    # root$prev <- sum(unlist(lapply(inference$C, function(x){sum(x == 0)}))) /

-    #     sum(unlist(lapply(inference$C, function(x){length(x)})))

-    # 

-    # root$prev <- round(root$prev, digits = 2)

-    # 

-    # root$size <- 2*sqrt(size*root$prev/pi)

-    # root$size2 <- 2*sqrt(size2*root$prev/pi)

-    # 

-    # root$label <- "Root"

-    # 

-    # if(show_prev) {

-    #     num_str <- sub("^(-?)0.", "\\1.", sprintf("%.2f", root$prev))

-    #     root$label <- paste0(root$label, " (", num_str , ")")

-    # }

+

+    adjMatrix_base <- as.adj.matrix.unsorted(inference$B, root = TRUE)

+    

+    M_leafs <- which(apply(X = adjMatrix_base, MARGIN = 1, FUN = sum)==0)

+    Clone_Mut <- sapply(inference$clones_summary, function(x){tail(x,1)}, USE.NAMES = T)

+    C_leafs <- Clone_Mut[match(names(M_leafs), Clone_Mut)]

+    

+    

+    if(rem_unseen_leafs == TRUE) {

+

+        uns_cl_mut <- C_leafs[which(inference$clones_prevalence[match(names(C_leafs),rownames(inference$clones_prevalence)), "Total"] == 0)]

+

+            while(length(uns_cl_mut) > 0) {

+                

+                # Delete each clone and corresponding mutation from B, clone_prevalence and clone_summary

+                del_mut <- as.character(uns_cl_mut)

+                del_clone <- names(uns_cl_mut)

+                

+                inference$B <- inference$B[!(rownames(inference$B) %in% del_clone), !(colnames(inference$B) %in% del_mut)]

+                inference$clones_prevalence <- inference$clones_prevalence[!(rownames(inference$clones_prevalence) %in% del_clone),]

+                inference$clones_summary <- inference$clones_summary[!(names(inference$clones_summary) %in% del_clone)]

+                

+                # Repeate

+                adjMatrix_base <- as.adj.matrix.unsorted(inference$B, root = TRUE)

+                M_leafs <- which(apply(X = adjMatrix_base, MARGIN = 1, FUN = sum)==0)

+                Clone_Mut <- sapply(inference$clones_summary, function(x){tail(x,1)}, USE.NAMES = T)

+                C_leafs <- Clone_Mut[match(names(M_leafs), Clone_Mut)]

+                uns_cl_mut <- C_leafs[which(inference$clones_prevalence[match(names(C_leafs),rownames(inference$clones_prevalence)), "Total"] == 0)]

+            }

+    }

     cl_vertex <- data.frame(prevalance = as.vector(inference$clones_prevalence[,-ncol(inference$clones_prevalence)]))

     cl_vertex$clone <- rep(c(0:(nrow(inference$clones_prevalence)-1)),ncol(inference$clones_prevalence)-1)

     cl_vertex$TP <- rep(1:(ncol(inference$clones_prevalence)-1), each = nrow(inference$clones_prevalence))

-    cl_vertex$last_mutation <- rep(unlist(lapply(c("Root",inference$clones_summary), function(x){tail(x, 1)})), ncol(inference$clones_prevalence)-1)

-    

+    cl_vertex$last_mutation <- rep(c("Root",Clone_Mut), ncol(inference$clones_prevalence)-1)

+

     if(!is.null(clone_labels)) {

         if(length(clone_labels)!=length(inference$clones_summary)) {

             warning("Label number is different from the number of clones.")

@@ -100,7 +116,7 @@ longitudinal.tree.plot <- function( inference,

     cl_vertex$branch_level <- 0

     cl_vertex$names <- paste0("T", cl_vertex$TP, "-", cl_vertex$last_mutation)

-    # names must be the first column

+    # Names must be the first column

     cl_vertex <- cl_vertex[,order(ncol(cl_vertex):1)]

     cl_edges <- data.frame(stringsAsFactors = FALSE)

@@ -132,13 +148,12 @@ longitudinal.tree.plot <- function( inference,

             }

         }

     }

+

+    # Processing parental relations

+    cl_edges_parental <- data.frame(stringsAsFactors = FALSE)

-    # Processing parental relations    

-    adjMatrix_base <- as.adj.matrix.unsorted(inference$B, root = TRUE)

-    

-    # Start from one leaf

-    B_leaf <- which(apply(X = adjMatrix_base, MARGIN = 1, FUN = sum)==0)

-    for(cs_id in B_leaf) {

+    # Start from one leaf of M_leafs

+    for(cs_id in M_leafs) {

         curTP <- max(cl_vertex$TP)

         cp_id <- which(adjMatrix_base[,cs_id]==1)

         while(length(cp_id)>0) {

@@ -148,8 +163,7 @@ longitudinal.tree.plot <- function( inference,

             if(length(TPs)==0) {

                 cs_tp <- curTP

-            }

-            else {

+            } else {

                 cs_tp <- min(TPs)

             }

@@ -157,21 +171,20 @@ longitudinal.tree.plot <- function( inference,

             TPs <- cl_vertex$TP[cl_vertex$clone == (cp_id-1) & cl_vertex$prevalance > 0 & cl_vertex$TP <= cs_tp]

             if(length(TPs) == 0) {

-                # If the parental clone has ever had prevalence == 0 -> I use the same time point of the son clone

+                # If the parental clone has ever prevalence == 0 -> I use the same time point of the son clone

                 cp_tp = cs_tp

-            }

-            else {

+            } else {

                 cp_tp <- max(TPs)

             }

             from_cc <- cl_vertex$names[cl_vertex$clone == (cp_id-1) & cl_vertex$TP == cp_tp]

             to_cc <- cl_vertex$names[cl_vertex$clone == (cs_id-1) & cl_vertex$TP == cs_tp]

             mut <- cl_vertex$last_mutation[cl_vertex$clone == (cs_id-1) & cl_vertex$TP == cs_tp]

-            cl_edges <- rbind(cl_edges, data.frame(from = from_cc, 

-                                                   to = to_cc, 

-                                                   type = "parental", 

-                                                   extincion = FALSE, 

-                                                   stringsAsFactors = FALSE))

+            cl_edges_parental <- rbind(cl_edges_parental, data.frame(from = from_cc, 

+                                                                     to = to_cc, 

+                                                                     type = "parental", 

+                                                                     extincion = FALSE, 

+                                                                     stringsAsFactors = FALSE))

             # Now son clone becomes parental clone

             curTP <- cp_tp

@@ -181,30 +194,45 @@ longitudinal.tree.plot <- function( inference,

         }

     }

-    cl_edges <- cl_edges[!duplicated.data.frame(cl_edges[,c("from", "to", "type")]),]

+    # Fix duplicate parental relation (keep earliest) ones

+    

+    cl_edges_parental$from_cl <- cl_vertex$clone[match(cl_edges_parental$from, cl_vertex$names)]

+    cl_edges_parental$to_cl <- cl_vertex$clone[match(cl_edges_parental$to, cl_vertex$names)]

+    cl_edges_parental$to_tp <- cl_vertex$TP[match(cl_edges_parental$to, cl_vertex$names)]

+    

+    cl_edges_parental <- cl_edges_parental[order(cl_edges_parental$to_tp),]

+    

+    cl_edges_parental <- cl_edges_parental[!duplicated(cl_edges_parental[,c("from_cl", "to_cl")], fromLast = F), c("from","to","type","extincion")]

+    

+    cl_edges <- rbind(cl_edges, cl_edges_parental)

+    

+    # Fixing missing persistence relations

+    fixing_clones <- data.frame(names = unique(as.character(cl_edges$from)))

+    

+    fixing_clones$clones <- cl_vertex$clone[match(fixing_clones$names,cl_vertex$names)]

+    fixing_clones$TP <- cl_vertex$TP[match(fixing_clones$names,cl_vertex$names)]

-    # Fixing missing prevalence relations

-    included_clones_names <- unique(c(as.character(cl_edges$from), as.character(cl_edges$to)))

-    for(icn in included_clones_names) {

-        ic <- cl_vertex$clone[cl_vertex$names == icn]

-        ic_tp <- cl_vertex$TP[cl_vertex$names == icn]

+    fixing_clones <- fixing_clones[duplicated(fixing_clones$clones) | duplicated(fixing_clones$clones, fromLast = T),]

+    fixing_clones <- fixing_clones[order(fixing_clones$TP),]

+    

+    for(fxc in unique(fixing_clones$clones)){

-        next_cln <- cl_vertex$names[cl_vertex$clone == ic & cl_vertex$TP == (ic_tp + 1)]

-        if(length(next_cln) == 0) {

-            next

-        }

-        else {

-            if(sum(cl_edges$from == icn & cl_edges$to == next_cln) == 0 && next_cln %in% included_clones_names) {

-                cl_edges <- rbind(cl_edges, data.frame(from = icn, 

-                                                       to = next_cln, 

-                                                       type = "persistence", 

-                                                       extincion = FALSE, 

-                                                       stringsAsFactors = FALSE))

+        fixing_clones_i <- fixing_clones[fixing_clones$clones == fxc,]

+        for(i in 1:(nrow(fixing_clones_i)-1)) {

+            

+            if(sum(cl_edges$from == fixing_clones_i$names[i] & cl_edges$to == fixing_clones_i$names[i+1] & cl_edges$type ==  "persistence") == 0) {

+                cl_edges <- rbind(cl_edges, data.frame(from = fixing_clones_i$names[i], 

+                                                       to = fixing_clones_i$names[i+1],

+                                                       type = "persistence",

+                                                       extincion = FALSE,

+                                                       stringsAsFactors = FALSE)

+                )

             }

+            

         }

     }

-    

+

     # Setting edges labels

     cl_edges$label <- ""

     cl_edges$name <- ""

@@ -233,7 +261,7 @@ longitudinal.tree.plot <- function( inference,

     # Setting the size of the extincted clones

     cl_vertex$size[cl_vertex$names %in% as.character(cl_edges$to[cl_edges$extincion])] <- 2*sqrt(size*0.01)

     cl_vertex$size2[cl_vertex$names %in% as.character(cl_edges$to[cl_edges$extincion])] <- 2*sqrt(size2*0.01)

-    cl_vertex$shape[cl_vertex$names %in% as.character(cl_edges$to[cl_edges$extincion])] <- "rectangle"

+    cl_vertex$shape[cl_vertex$names %in% as.character(cl_edges$to[cl_edges$extincion])] <- "vrectangle"

     cl_vertex$label.dist <- -1

     cl_vertex$label.degree <- 0

@@ -256,6 +284,7 @@ longitudinal.tree.plot <- function( inference,

     if(show_prev) {

         num_str <- sub("^(-?)0.", "\\1.", sprintf("%.2f", cl_vertex$prevalance))

         cl_vertex$label <- paste0(cl_vertex$label, " (", num_str , ")")

+        cl_vertex$label[cl_vertex$prevalance == 0.0] <- ""

     }

@@ -272,13 +301,7 @@ longitudinal.tree.plot <- function( inference,

     cl_edges$lty <- ifelse(cl_edges$type == "persistence", yes = 2, no = 1)

     g <- igraph::graph_from_data_frame(cl_edges, directed=TRUE, vertices=cl_vertex)

-    

-    # get coordinate for each vertex

-    # org_coordinates <- layout_(g, as_tree())

-    # 

-    # cl_vertex$coord.x <- round(org_coordinates[,1], digits = 3)

-    # cl_vertex$coord.y <- round(org_coordinates[,2], digits = 3)

-    

+

     parental_clones <- which(apply(X = adjMatrix_base, MARGIN = 2, function(x) sum(x == 1))==0)

     c_br <- 1

@@ -345,110 +368,18 @@ longitudinal.tree.plot <- function( inference,

     igraph::vertex_attr(graph = g, name = "color") <- cl_vertex$color

     igraph::delete_edge_attr(graph = g, name = "extincion")

-    

-    

-    ### NEW LAYOUT

-    

+

     cl_vertex$coord.x <- NA

     cl_vertex$coord.y <- NA

-    

-    # Adding root

-    # cl_vertex <- rbind(data.frame(names = "Root", 

-    #                               branch_level = 0,

-    #                               branch = 0,

-    #                               label = root$label,

-    #                               last_mutation = "",

-    #                               TP = 1,

-    #                               clone = 0,

-    #                               prevalance = root$prev,

-    #                               size = root$size,

-    #                               size2 = root$size2,

-    #                               shape = "circle",

-    #                               label.dist = -2,

-    #                               label.degree = 45,

-    #                               extincion = 0,

-    #                               coord.x = 0,

-    #                               coord.y = 0,

-    #                               color = "#DDDDDD"

-    # ),cl_vertex)

-    

-    # #ADDING ROOT IN EACH TIME POINT

-    # root_vertexes <- data.frame()

-    # 

-    # for(TP in 1:length(inference$C)) {

-    #     root_vertexes <- rbind(root_vertexes, 

-    #                            data.frame(names = paste0("Root_t", TP), 

-    #                                       branch_level = 0,

-    #                                       branch = 0,

-    #                                       label = root$label,

-    #                                       last_mutation = "",

-    #                                       TP = TP,

-    #                                       clone = 0,

-    #                                       prevalance = root$prev,

-    #                                       size = root$size,

-    #                                       size2 = root$size2,

-    #                                       shape = "circle",

-    #                                       label.dist = -2,

-    #                                       label.degree = 45,

-    #                                       extincion = 0,

-    #                                       coord.x = 0,

-    #                                       coord.y = TP-1,

-    #                                       color = "#DDDDDD"

-    #                            ))

-    #     

-    # }

-    # 

-    # cl_vertex <- rbind(root_vertexes,cl_vertex)

-    # 

-    # root_edges <- data.frame()

-    # for(TP in 1:(length(inference$C)-1)) {

-    #     root_edges <- rbind(root_edges,

-    #                         data.frame(from = paste0("Root_t", TP), 

-    #                                    to = paste0("Root_t", (TP+1)),

-    #                                    type = "persistence",

-    #                                    extincion = FALSE,

-    #                                    label = "",

-    #                                    name = "",

-    #                                    lty = 2

-    #                         ))

-    # }

-    # 

-    # cl_edges <- rbind(root_edges,cl_edges)

-    # 

-    # adjMatrix_overall <- igraph::get.adjacency(g, sparse = F, attr = "type", names = T)

-    # ancestral_nodes <- colnames(adjMatrix_overall)[which(colSums(adjMatrix_overall != "")==0)] 

-    # 

-    # for(an in ancestral_nodes) {

-    #     cl_edges <- rbind(data.frame(from = "Root_t1",

-    #                                  to = an,

-    #                                  type = "Parental",

-    #                                  extincion = FALSE,

-    #                                  label = cl_vertex$last_mutation[cl_vertex$names == an],

-    #                                  name = cl_vertex$last_mutation[cl_vertex$names == an],

-    #                                  lty = 1

-    #     ),cl_edges)

-    # }

-    # 

-    # adjMatrix_overall <- igraph::get.adjacency(g, sparse = F, attr = "type", names = T)

-    # ancestral_nodes <- colnames(adjMatrix_overall)[which(colSums(adjMatrix_overall != "")==0)] 

-    # 

-    # for(an in ancestral_nodes) {

-    #     cl_edges <- rbind(data.frame(from = "Root",

-    #                                  to = an,

-    #                                  type = "Parental",

-    #                                  extincion = FALSE,

-    #                                  label = cl_vertex$last_mutation[cl_vertex$names == an],

-    #                                  name = cl_vertex$last_mutation[cl_vertex$names == an],

-    #                                  lty = 1

-    #     ),cl_edges)

-    # }

-    

-    

-    adjMatrix_base <- as.adj.matrix.unsorted(inference$B, root = T)

     g <- igraph::graph_from_data_frame(cl_edges, directed=TRUE, vertices=cl_vertex)

     adjMatrix_overall <- igraph::get.adjacency(g, sparse = F, attr = "type", names = TRUE)

-

+    

+    adjMatrix_overall[which(adjMatrix_overall=="")] <- 0

+    adjMatrix_overall[which(adjMatrix_overall=="persistence")] <- 2

+    adjMatrix_overall[which(adjMatrix_overall=="parental")] <- 1

+    storage.mode(adjMatrix_overall) <- "integer"

+    

     # Count total level number in each time point

     timepoints <- unique(cl_vertex$TP)

@@ -471,6 +402,7 @@ longitudinal.tree.plot <- function( inference,

                     adjM = adjMatrix_base_tp,

                     l_path = 0,

                     ind_lPath = 1,

+                    mut_list = NA,

                     row_v = idx

                 )

                 deep_tmp <- recursiveDescend(descend)$max_deep

@@ -479,11 +411,13 @@ longitudinal.tree.plot <- function( inference,

             mut_TP <- c(mut_TP, max_deep_tp)

         }

     }

+    

+    

     mut_TP <- cumsum(mut_TP) + 1:length(mut_TP)

     names(mut_TP) <- timepoints

     cl_df <- list(cl_vertex = cl_vertex, cl_edges = cl_edges)

-    idx_next_v <-  which(colSums(adjMatrix_overall != "") == 0)

+    idx_next_v <-  which(colSums(adjMatrix_overall != 0) == 0)

     # Root

     Xc = 0

@@ -497,99 +431,22 @@ longitudinal.tree.plot <- function( inference,

                                          adjMatrix_base=adjMatrix_base,

                                          adjMatrix_overall=adjMatrix_overall,

                                          mut_TP=mut_TP,

-                                         labels_show=labels_show)

+                                         labels_show=labels_show,

+                                         label_offset=label_offset)

     cl_vertex <- cl_df$cl_vertex[order(cl_df$cl_vertex$TP, cl_df$cl_vertex$coord.y),]

     cl_edges <- cl_df$cl_edges

-    g_mod <- igraph::graph_from_data_frame(d = cl_edges, directed=TRUE, vertices=cl_vertex)

-    # # fixing clone y position based on the time point 

-    # if(length(unique(cl_vertex$TP))>1) {

-    #     for(tp in seq(from = 1, to = max(cl_vertex$TP)-1)) {

-    #         m_c <- min(cl_vertex$coord.y[which(cl_vertex$TP == tp)]) - 1

-    #         cl_vertex$coord.y[which(cl_vertex$TP == tp+1 & cl_vertex$coord.y >= m_c)] <- m_c

-    #     }

-    # }

-    # 

-    # # Fixing overlapping points 

-    # for(c_x in unique(cl_vertex$coord.x)) {

-    #     idx_coord_y <- which(cl_vertex$coord.x == c_x)[order(org_coordinates[which(cl_vertex$coord.x == c_x,), 2], decreasing = TRUE)]

-    #     if(length(idx_coord_y) < 2) {

-    #         next;

-    #     }

-    #     for(i in 1:(length(idx_coord_y)-1)) {

-    #         if(cl_vertex$coord.y[idx_coord_y[i]] <= cl_vertex$coord.y[idx_coord_y[i+1]]) {

-    #             cl_vertex$coord.y[idx_coord_y[i+1]] = cl_vertex$coord.y[idx_coord_y[i]] - 1

-    #         }

-    #     }

-    # }

-    # 

-    # deltaX <- 2*(sum(abs(range(cl_vertex$coord.x))) / length(cl_vertex$coord.x))

-    # 

-    # # Fixing overlapping edges

-    # found = TRUE

-    # maxIter = iter_max

-    # while(found & maxIter > 0) {

-    #     found = FALSE

-    #     maxIter <- maxIter - 1

-    #     for(i in 1:(nrow(cl_edges)-1)) {

-    #         for(j in 2:nrow(cl_edges)) {

-    #             

-    #             if(i == j) {

-    #                 next;

-    #             }

-    #             

-    #             edgeA_1 = as.character(cl_edges$from[i])

-    #             edgeA_2 = as.character(cl_edges$to[i])

-    #             edgeB_1 = as.character(cl_edges$from[j])

-    #             edgeB_2 = as.character(cl_edges$to[j])

-    #             

-    #             ax1 <- cl_vertex$coord.x[which(cl_vertex$names == edgeA_1)]

-    #             ay1 <- cl_vertex$coord.y[which(cl_vertex$names == edgeA_1)]

-    #             ax2 <- cl_vertex$coord.x[which(cl_vertex$names == edgeA_2)]

-    #             ay2 <- cl_vertex$coord.y[which(cl_vertex$names == edgeA_2)]      

-    #             

-    #             bx1 <- cl_vertex$coord.x[which(cl_vertex$names == edgeB_1)]

-    #             by1 <- cl_vertex$coord.y[which(cl_vertex$names == edgeB_1)]

-    #             bx2 <- cl_vertex$coord.x[which(cl_vertex$names == edgeB_2)]

-    #             by2 <- cl_vertex$coord.y[which(cl_vertex$names == edgeB_2)]   

-    #             

-    #             # Check if two ends are on the same place (if truw, increase the Y values)

-    #             if(ax2 == bx2 && ay2 == by2) {

-    #                 ax2 <- ax2 + deltaX

-    #                 cl_vertex$coord.x[which(cl_vertex$names == edgeA_2)] <- ax2

-    #             }

-    #             

-    #             # Check if parental clone have y values minor respect to the son clones ones

-    #             if(ay1 <= ay2) {

-    #                 ay2 <- ay1 - 1

-    #                 cl_vertex$coord.y[which(cl_vertex$names == edgeA_2)] <- ay2

-    #             }

-    #             if(by1 <= by2) {

-    #                 by2 <- by1 - 1

-    #                 cl_vertex$coord.y[which(cl_vertex$names == edgeB_2)] <- by2

-    #             }      

-    #             

-    #             if(length(unique(c(edgeA_1,edgeA_2,edgeB_1,edgeB_2))) < 4 ) {

-    #                 next;

-    #             }

-    #             

-    #             if(checkSegmentIntersect(ax1,ay1,ax2,ay2,bx1,by1,bx2,by2)) {

-    #                 found = TRUE

-    #                 cl_vertex$coord.x[which(cl_vertex$names == edgeA_2)] <- bx2

-    #                 cl_vertex$coord.x[which(cl_vertex$names == edgeB_2)] <- ax2 

-    #             }

-    #         }

-    #     }

-    # }

+    

+    

+    g_mod <- igraph::graph_from_data_frame(d = cl_edges, directed=TRUE, vertices=cl_vertex)

+

     time_point_grp <- split(cl_vertex$names[cl_vertex$last_mutation!="Root"], cl_vertex$TP[cl_vertex$last_mutation!="Root"])

-    #time_point_grp[[1]] <- NULL

     ratio = par("din")[1] / par("din")[2]

-    #g$layout <- norm_coords(as.matrix(cl_vertex[,c("coord.x", "coord.y")]), xmin = -1*(ratio), xmax = 1*(ratio), ymin = -1, ymax = 1)

     g_mod$layout <- igraph::norm_coords(as.matrix(cl_vertex[,c("coord.x", "coord.y")]), xmin = -1*ratio, xmax = 1*ratio, ymin = 1, ymax = -1)

     if(tk_plot) {

@@ -6,13 +6,13 @@

 \usage{

 longitudinal.tree.plot(

   inference,

+  rem_unseen_leafs = TRUE,

   show_plot = TRUE,

   filename = "lg_output.xml",

   labels_show = "mutations",

   clone_labels = NULL,