R/LACEview2.R
In BIMIB-DISCo/LACE: Longitudinal Analysis of Cancer Evolution (LACE)
Defines functions
renderLACE
LACEOutput
## version 1118
### LACEview2.R
###
### Add lace_interface function
#' @title LACE Interface
#' @description This function generates a longitudinal clonal tree and a
#' graphic interface to explore the data using as input the clonal tree
#' formatted in the same way as the one produced by LACE during the imputation
#' steps
#'
#' @param B_mat (Required). B is the clonal tree matrix where columns are the
#' clonal mutations and and rows are the clones. The clonal tree matrix should
#' contain a column and a row named "Root" representing the root of the tree and
#' the wild type, respectively.
#' B is a binary matrix where 1 are the mutations associated to the clones.
#' The wild type column has all ones
#'
#' @param clones_prevalence (Required) The clonal prevalence matrix
#'
#' @param C_mat (Required) The corrected clonal attachment
#'
#' @param error_rates (Required) The false positive alpha and false negative beta
#' error rates used to infer the clonal tree
#'
#' @param info (Optional). HTML formatted text with information regarding
#' the experiments
#'
#' @param width (optional) Size of the window interafce
#'
#' @param height (optional) Size of the window interafce
#'
#' @param elementId (optional) Element id
#'
#' @return An implementation of the htmlwidgets
#'
#' @importFrom Matrix rankMatrix
#' @importFrom jsonlite toJSON
#' @importFrom grDevices colorRampPalette
#' @importFrom purrr is_null
#' @importFrom tidyr gather
#' @importFrom purrr detect_index
#' @import stringr
#' @import stringi
#' @import RColorBrewer
#'
#'
#'
# #' @export
lace_interface <- function (B_mat,
clones_prevalence,
C_mat,
error_rates,
width = NULL,
height = NULL,
elementId = NULL,
info = '') {
## library(purrr, quietly = T)
## library(stringr, quietly = T)
## library(stringi, quietly = T)
## library(Matrix, quietly = T)
if (!exists('width')) {width=NULL}
if (!exists('height')) {height=NULL}
if (!exists('elementId')) {elementId=NULL}
if (!exists('info')) {info=''}
if (is_null(height)) {
height=900
height_fishplot=300
} else if (height<800) {
height_fishplot=300
} else {
height_fishplot=300
}
if (is_null(width)) {
width=900
width_fishplot=width/2
} else if (width<900) {
width_fishplot=width/2
} else {
width_fishplot=width/2
}
if (is_null(info)) {
info = 'No dataset or experimental info available'
}
B = B_mat
B
make_binary<-function(B) {
if (!is.numeric(B)){
log2_print('B is not a valid matrix')
return(NULL)
}
is_binary=sum(B==1)+sum(B==0)==ncol(B)*nrow(B)
if (!is_binary) {
log2_print('B is not binary')
B[B==1]=1
B[B==0]=0
}
else {
log2_print('B is already binary')
}
return(B)
}
find_roots <- function(B) {
#find roots
set_of_roots = rownames(B)
set_of_roots
for (i in set_of_roots) {
tmp_set_of_roots=setdiff(set_of_roots,i)
for (j in tmp_set_of_roots) {
proj=sum(B[i,]*B[j,])
if (sum(B[i,])==proj) {
set_of_roots=setdiff(set_of_roots,j)
log2_print(paste(i,'is parent of', j))
#print(set_of_roots)
}
if (sum(B[j,])==proj) {
set_of_roots=setdiff(set_of_roots,i)
log2_print(paste(j,'is parent of', i))
#print(set_of_roots)
}
}
}
set_of_roots
return(set_of_roots)
}
forest_check <- function(B) {
#check if it is a forest
check_forest=matrix(data = 0, nrow=nrow(B),ncol=1)
check_forest
check_forest_control=matrix(data = 1, nrow=nrow(B),ncol=1)
check_forest_control[1,1]=0
for(i in 1:nrow(B)) {
check_forest_tmp_=0
for(j in i:nrow(B)) {
check_forest_tmp=(sum(abs(B[j,]-B[i,]))==1)
check_forest[j,1]=check_forest[j,1]+check_forest_tmp
#check_forest_tmp_ = check_forest_tmp_+ check_forest_tmp
log2_print(paste(rownames(B)[i], rownames(B)[j], check_forest[j,1]))
}
#check_forest=rbind(check_forest, check_forest_tmp_)
}
log2_print(check_forest)
log2_print(check_forest_control)
log2_print(all(check_forest==check_forest_control))
is_forest=all(check_forest==check_forest_control)
if (is_forest) {
log2_print("B represents a forest")
} else {
log2_print("B is not a forest or a tree")
}
return(is_forest)
}
add_unique_root <- function(B, root_name='Root') {
#add unique root if necessary
B_rank=rankMatrix(B)[1]
set_of_roots = find_roots(B)
#check if it has one root
num_roots=length(set_of_roots)
if (num_roots==1) {
log2_print("nodes in B are sprouting from a single node")
}
else {
log2_print("there are multiple roots")
}
if (B_rank==nrow(B) || B_rank==ncol(B)) {
log2_print('B is a full-rank matrix.')
if (forest_check(B)) {
log2_print('B has a forest like structure')
if (nrow(B)>ncol(B)) {
log2_print('B is not a square matrix. B has not enough mutations.')
#print('B is not a full rank matrix. B has not enough mutations.')
if (nrow(B)==ncol(B)+1) {
#check if there is a root
if (num_roots!=1 && root_col_name!=root_name) {
root_col_name=root_name
B = cbind(xxx=1,B)
colnames(B)[colnames(B) == 'xxx'] <- root_name
}
}
} else if (nrow(B)<ncol(B)) {
log2_print('B is not a square matrix. B has not enough clones.')
if (nrow(B)+1==ncol(B)) {
lower_tri_column_index=sort(colSums(B),decreasing = TRUE)
#number of clones with such mutation
clones_per_mutation=table(lower_tri_column_index)
max_clones_per_mutation_idx=(
max(as.integer(names(clones_per_mutation))))
max_clones_per_mutation_idx
if (max_clones_per_mutation_idx<B_rank && root_row_name!=root_name) {
root_row_name='Root'
B = rbind(xxx=0,B)
rownames(B)[rownames(B) == 'xxx'] <- root_name
B[1,1]=1
}
}
}
}
}
B_rank=rankMatrix(B)[1]
B_rank
if (!(B_rank==ncol(B) && B_rank==nrow(B))) {
log2_print('Adding a root does not resolve the problem, B is till not a full rank matrix')
}
return(B)
}
make_lowtri<-function(B) {
#make B lower triangular
B_tmp=matrix(nrow=0, ncol=ncol(B))
colnames(B_tmp)=colnames(B)
B_tmp
B2=B #B_row_reduced
B3=B2 #B_row_reduced_col_ordered
B4=B3 #B_row_col_reduced_col_ordered
set_of_roots = rownames(B2)
for(i in set_of_roots) {
i=nrow(B)-length(set_of_roots)+1
i
first_r=names(which.min(rowSums(B4)))
first_r
B_tmp=rbind(B_tmp, B3[first_r, ,drop=FALSE])
B_tmp
if (i==nrow(B)){
break
}
if (i>1) {
one_col=B_tmp[first_r,-1:-i+1]==1
zero_col=B_tmp[first_r,-1:-i+1]==0
} else {
one_col=B_tmp[first_r,]==1
zero_col=B_tmp[first_r,]==0
}
one_col=names(one_col[one_col])
one_col
zero_col=names(zero_col[zero_col])
zero_col
B2=B2[rownames(B2) !=first_r, , drop=FALSE]
B2
B3=cbind(B2[,colnames(B_tmp[,1:i-1,drop=FALSE]),drop=FALSE],
B2[,one_col,drop=FALSE],B2[,zero_col,drop=FALSE])
B3
B4=cbind(B2[,one_col,drop=FALSE],B2[,zero_col,drop=FALSE])
B4
#B2=B2[,-1]
B_tmp=cbind(B_tmp[,1:i-1,drop=FALSE], B_tmp[,one_col,drop=FALSE],
B_tmp[,zero_col,drop=FALSE])
B_tmp
set_of_roots=setdiff(set_of_roots,first_r)
}
B=B_tmp
return(B)
}
#####
B=make_binary(B)
#B_rank=rankMatrix(B)[1]
#B_rank
#is_forest = forest_check(B)
#is_forest
lower_tri_column_index=sort(colSums(B),decreasing = TRUE)
root_col_name=names(which.max(lower_tri_column_index))
root_row_name=names(which.min(sort(rowSums(B),decreasing = TRUE)))
lower_tri_column_index
root_col_name
root_row_name
B<-add_unique_root(B)
B<-make_lowtri(B)
lower_tri_column_index=sort(colSums(B),decreasing = TRUE)
root_col_name=names(which.max(lower_tri_column_index))
root_row_name=names(which.min(sort(rowSums(B),decreasing = TRUE)))
lower_tri_column_index
root_col_name
root_row_name
#check all
is_binary = sum(B==1)+sum(B==0)==ncol(B)*nrow(B)
is_square_mat = ncol(B)==nrow(B)
is_full_rank = rankMatrix(B)[1]==nrow(B)
is_forest = forest_check(B)
has_single_root = length(find_roots(B))==1
if (is_binary) {log2_print('B is a binary matrix')}
if (is_square_mat) {log2_print('B is a square matrix')}
if (is_full_rank) {log2_print('B is a full rank matrix')}
if (is_forest) {log2_print('B is a forest ')}
#browser()
if (has_single_root) {log2_print('B has single root')}
if (is_binary && is_square_mat && is_full_rank && is_forest && has_single_root) {
log2_print('all checks done!')
log2_print('continue...')
}
#this if else is not necessary in this form. Instead, it should set root names
if ("Root" %in% rownames(clones_prevalence)){
prevalence=clones_prevalence
} else {
prevalence=rbind(Root=0,clones_prevalence)
}
prevalence=prevalence[rownames(B),]
clone_labels=as.list(str_split(colnames(B), pattern='_', n=2, simplify = TRUE)[,1])
##clone_labels=str_split(colnames(B), pattern='_', n=2, simplify = T)[,1]
names(clone_labels)<-rownames(B)
clone_labels
#library("RColorBrewer")
#colours = as.list(brewer.pal(n = nrow(prevalence), name = "Paired"))
colours = as.list(colorRampPalette(brewer.pal(12, "Paired"))(nrow(prevalence)))
names(colours)<-rownames(B)
colours
#create adjacency matrix
adj_matrix = array(0L, c((dim(B)[1]), (dim(B)[2])))
rownames(adj_matrix) <- colnames(B)[(1:ncol(B))]
colnames(adj_matrix) <- colnames(B)[(1:ncol(B))]
for (rP in seq(1, nrow(B), by=1) ) {
for (rC in seq((rP + 1), nrow(B), length = max(0,nrow(B)-(rP + 1)+1))) {
if (all(B[rP, 1:rP] == B[rC, 1:rP]) && sum(B[rP, ]) == (sum(B[rC, ]) - 1)) {
adj_matrix[(rP ), (rC )] <- 1
}
}
}
log2_print('clonal tree:')
log2_print(adj_matrix)
adj_to_B <- function( adj_matrix, clone_labels ) {
B=matrix(0, nrow=nrow(adj_matrix), ncol=ncol(adj_matrix))
colnames(B)=colnames(adj_matrix)
rownames(B)=rownames(adj_matrix)
B[1,1]=1
for (s in rownames(adj_matrix)) {
for (t in colnames(adj_matrix)) {
if (adj_matrix[s,t]==1) {
B[t,]=B[s,]
B[t,t]=1
}
}
}
rownames(B)=names(clone_labels)
return(B)
}
log2_print(adj_to_B(adj_matrix, clone_labels))
#adjMatrix_base <- adj_matrix
####
#it resolves all the incongruences resulting from not considering the time at all
chrono_occurences <- function(adj_matrix, prevalence) {
#create first time mutations appearance based on clonal prevalence only
times= apply((prevalence)>0, 1, function(x) detect_index(x, ~ .x == TRUE) )
#times=times+1 #test
times[1]=1 #setting root
times_pre<-times
adj_matrix_t_pre<-adj_matrix
colnames(adj_matrix_t_pre) <-paste0(rownames(prevalence), '_t', times)
rownames(adj_matrix_t_pre) <-paste0(rownames(prevalence), '_t', times)
#create first time mutations appearance based on chronological order
sources=apply( t(adj_matrix)>0, 1, function(x) detect_index(x, ~ .x == TRUE) )
sources[1]=1
for(target in 1: length(rownames(prevalence))) {
source=sources[target]
if (times[target] < times[source]) {
log2_print(times[target],times[source])
times[source]=times[target]
}
}
adj_matrix_t<-adj_matrix
colnames(adj_matrix_t) <-paste0(rownames(prevalence), '_t', times)
rownames(adj_matrix_t) <-paste0(rownames(prevalence), '_t', times)
if (all(times==times_pre)) {
log2_print('No incongruences found due to the chronological order')
log2_print('first time mutation occurrences based the chronological order of samples:')
log2_print(times)
log2_print('adjacent matrix with first time occurrences:')
log2_print(adj_matrix_t)
} else {
log2_print('The longitudinal clonal tree has been corrected')
log2_print('pre-computation of first time mutation occurrences based only on prevalence values different from zero:')
log2_print(times_pre)
log2_print('adjacent matrix with pre-computed first time occurrences:')
log2_print(adj_matrix_t_pre)
log2_print('first time mutation occurrences based the chronological order of samples:')
log2_print(times)
log2_print('adjacent matrix with first time occurrences:')
log2_print(adj_matrix_t)
}
return(times)
}
times <- chrono_occurences(adj_matrix,prevalence)
adj_matrix_t<-adj_matrix
colnames(adj_matrix_t) <-paste0(rownames(prevalence), '_t', times)
rownames(adj_matrix_t) <-paste0(rownames(prevalence), '_t', times)
## adding the other elements of the direct product
times_n= 1:(length(colnames(prevalence))-1)
times_n
for (t in times_n) {
for (col in 1:ncol(adj_matrix)) {
col_ = paste0(rownames(prevalence)[col],'_t',t)
if (!(col_ %in% colnames(adj_matrix_t))) {
adj_matrix_t=cbind(adj_matrix_t, xxx=0)
colnames(adj_matrix_t)[colnames(adj_matrix_t) == 'xxx'] <- col_
}
}
for (row in 1: nrow(adj_matrix)) {
row_ = paste0(rownames(prevalence)[row],'_t',t)
if (!(row_ %in% rownames(adj_matrix_t))) {
adj_matrix_t=rbind(adj_matrix_t, xxx=0)
rownames(adj_matrix_t)[rownames(adj_matrix_t) == 'xxx'] <- row_
}
}
}
## Root as wild type should be considered present in all the next time steps:
## hence an accessory self link is used
adj_matrix_t['Root_t1','Root_t1']=1
## setting links of clones in following times after first occurrence
for (t_1 in times_n[1:(length(times_n)-1)]) {
log2_print(c('time=',t_1))
for (source in 1:nrow(adj_matrix)) {
for (target in 1:ncol(adj_matrix)) {
for (t_2 in times_n[t_1:(length(times_n)-1)]) { # r fa cacare
#source mut_time
source_t1 = paste0(rownames(prevalence)[source],'_t',t_1)
#target mut_time
target_t2 = paste0(rownames(prevalence)[target],'_t',t_2)
#print(c(source_t1, target_t2, adj_matrix_t[source_t1,target_t2]))
#source mut_time
source_t2 = paste0(rownames(prevalence)[target],'_t',t_2)
#target mut_time+1
target_t2_next=paste0(rownames(prevalence)[target],'_t',t_2+1)
if (adj_matrix_t[source_t1,target_t2]>=1 && adj_matrix_t[source_t1,target_t2]<=3) {
if (target==source && prevalence[target,t_2]==0){
value=3 #for dashed in case undetected clones
log2_print(paste0(source_t1, "->", target_t2,"=", adj_matrix_t[source_t1,target_t2], ", prev_",target_t2, "=", prevalence[target,t_2], ": ", source_t1, "->", target_t2, "=", value))
adj_matrix_t[source_t1, target_t2]<-value # we set the link before t2
log2_print(paste0(source_t1, "->", target_t2,"=", adj_matrix_t[source_t1,target_t2], ", prev_",target_t2, "=", prevalence[target,t_2], ": ", source_t2, "->", target_t2_next, "=", value))
adj_matrix_t[source_t2, target_t2_next]<-value # we set the link after t2
}
else {
value=2 #for time persistence
log2_print(paste0("1st ",source_t1, "->", target_t2,"=", adj_matrix_t[source_t1,target_t2], ", prev_",target_t2, "=", prevalence[target,t_2], ": ", source_t2, "->", target_t2_next, "=", value))
adj_matrix_t[source_t2,target_t2_next]<-value
}
if (target==source && t_2+1==length(times_n) && prevalence[target,t_2+1]==0){
value=3 #for dashed in case undetected clones
log2_print(paste0(source_t1, "->", target_t2,"=", adj_matrix_t[source_t1,target_t2], ", prev_",target_t2, "=", prevalence[target,t_2], ": ", source_t2, "->", target_t2_next, "=", value))
adj_matrix_t[source_t2, target_t2_next]<-value # we set the link after t2
}
}
#if (target==source && t_1+1==t_2 && prevalence[target,t_2]==0) {
#target_t1 = paste0(rownames(prevalence)[target],'_t',t_1) #t1=t2-1
# = paste0(rownames(prevalence)[source],'_t',t_2)
#target_t2 = paste0(rownames(prevalence)[target],'_t',t_2)
#target mut_time+1
#target_t2_next=paste0(rownames(prevalence)[target],'_t',t_2+1)
#value=3 #for dashed in case undetected clones
# print(c(source_t1, target_t2, source_t2, target_t2_next, value))
# print(c(source_t1, target_t2, source_t1, target_t2, value))
# adj_matrix_t[source_t2, target_t2_next]<-value # we set the link after t2
# adj_matrix_t[source_t1, target_t2]<-value # we set the link before t2
#}
}
}
}
}
#dead breaches
#in time reversal
#prevalence=prevalence
for (t_1 in times_n[(length(times_n)):1]) { # instead of 2 as smaller point
log2_print(c('time=',t_1))
for (source in 1:nrow(adj_matrix)) {
target=source # correlation only at distance 0
source_t1_prev =paste0(rownames(prevalence)[source],'_t',t_1-1)
target_t1 = paste0(rownames(prevalence)[target],'_t',t_1)
source_t1 =paste0(rownames(prevalence)[source],'_t',t_1)
target_t1_next = paste0(rownames(prevalence)[target],'_t',t_1+1)
if (t_1==times_n[(length(times_n))] && prevalence[target,t_1]==0 ){ #&& prevalence[source,t_1-1]==0) {
value=0 #for dead breaches
log2_print(c(source_t1, target_t1, source_t1_prev, target_t1, value))
adj_matrix_t[source_t1_prev,target_t1]<-value # we set the link after t2
#prevalence[prevalence[,t_1]==0,t_1]=-1
prevalence[target,t_1]=-1
} else if (prevalence[source,t_1]==0 && adj_matrix_t[source_t1,target_t1_next]==0) {
#target mut_time+1
value=0 #for dead breaches
log2_print(c('removed', target_t1))
log2_print(c(source_t1, target_t1, source_t1, target_t1_next, value))
adj_matrix_t[source_t1,target_t1_next]<-value # we set the link after t2
if (t_1!=1){
log2_print(c(source_t1, target_t1, source_t1_prev, target_t1, value))
adj_matrix_t[source_t1_prev,target_t1]<-value # we set the link after t2
}
#prevalence[prevalence[,t_1]==0,t_1]=-1
if (!(stri_detect_fixed(target_t1,"Root") && t_1==1)){
prevalence[target,t_1]=-1
}
}
}
}
## removal of the accessory self link
adj_matrix_t['Root_t1','Root_t1']=0
colnames(prevalence)
elements = list()
elements$nodes = list()
elements$edges = list()
for (row in 1:nrow(adj_matrix_t)) {
for (col in 1:ncol(adj_matrix_t)) {
if (adj_matrix_t[row, col] == 1) {
data = list()
data$data = list()
clone_=stri_reverse(str_split(
stri_reverse(colnames(adj_matrix_t)[col]),pattern='_', n=2)[[1]][2])
clone_idx=match(clone_,rownames(B))
log2_print(c('idx',clone_))
data$data$source = sprintf("%s", rownames(adj_matrix_t)[row])
data$data$target = sprintf("%s", colnames(adj_matrix_t)[col])
data$data$name = sprintf("%s", clone_labels[clone_][[1]])
#data$data$color = colours[clone_idx]
data$data$color = colours[clone_][[1]]
data$data$id = sprintf("%s_%s", data$data$source, data$data$target)
data$data$linestyle = "solid"
elements$edges = c(elements$edges, list(data))
}
if (adj_matrix_t[row, col] == -200) { #if (adj_matrix_t[row, col] == 2) {
data = list()
data$data = list()
clone_=stri_reverse(str_split(stri_reverse(
colnames(adj_matrix_t)[col]),pattern='_', n=2)[[1]][2])
#color_idx=match(clone_,rownames(B))
data$data$source = sprintf("%s", rownames(adj_matrix_t)[row])
data$data$target = sprintf("%s", colnames(adj_matrix_t)[col])
data$data$name = sprintf("")
data$data$color = colours[clone_][[1]]
data$data$id = sprintf("%s_%s", data$data$source, data$data$target)
data$data$linestyle = "solid" # only if next clonal_prev==0 else solid
elements$edges = c(elements$edges, list(data))
}
if (adj_matrix_t[row, col] >= 2) { #if (adj_matrix_t[row, col] == 3) {
data = list()
data$data = list()
clone_=stri_reverse(str_split(stri_reverse(
colnames(adj_matrix_t)[col]),pattern='_', n=2)[[1]][2])
#color_idx=match(clone_,rownames(B))
data$data$source = sprintf("%s", rownames(adj_matrix_t)[row])
data$data$target = sprintf("%s", colnames(adj_matrix_t)[col])
data$data$name = sprintf("")
data$data$color = colours[clone_][[1]]
data$data$id = sprintf("%s_%s", data$data$source, data$data$target)
data$data$linestyle = "dashed" # only if next clonal_prev==0 else solid
elements$edges = c(elements$edges, list(data))
}
}
}
#nodes
for (col in 1:(ncol(prevalence) - 1)) {
data = list()
data$data = list()
data$data$id = sprintf("T%s", col)
data$data$name = sprintf("T%s", col) #experiment2...
data$data$color = "#FFFFFF"
data$data$size = sprintf("%spx", 50)
data$data$prev = sprintf("%s [%s]", data$data$name, round(prevalence[1, col], 2))
parent = data$data$name
elements$nodes = c(elements$nodes, list(data))
for (row in 1:nrow(prevalence)) {
if (prevalence[row,col]!=-1) {
data = list()
data$data = list()
data$data$name = rownames(prevalence)[row]
data$data$color = colours[row][[1]]
data$data$id = sprintf("%s_t%s", rownames(prevalence)[row], col)
data$data$size = sprintf("%spx", 5 + as.integer(prevalence[row, col] * 150))
data$data$prev = sprintf("%s", round(prevalence[row, col], 2))
data$data$parent = parent
elements$nodes = c(elements$nodes, list(data))
}
}
}
#-------------------
#library(tidyr)
if ("Root" %in% rownames(clones_prevalence)){
prevalence=clones_prevalence
} else {
prevalence=rbind(Root=0,clones_prevalence)
}
prevalence=prevalence[rownames(B),]
fishplot_clonal_prev=as.data.frame(prevalence)
fishplot_clonal_prev = fishplot_clonal_prev[,!(names(fishplot_clonal_prev) %in% c("Total"))]
fishplot_clonal_prev=subset(fishplot_clonal_prev)
colnames(fishplot_clonal_prev)=c(paste0('T',times_n))
fishplot_clonal_prev['clone_id']=rownames(fishplot_clonal_prev)
fishplot_clonal_prev
fishplot_clonal_prev=gather(fishplot_clonal_prev,
'timepoint','clonal_prev', -'clone_id' )
fishplot_clonal_prev=fishplot_clonal_prev[c('timepoint',
'clone_id','clonal_prev')]
fishplot_clonal_prev['clonal_prev']=fishplot_clonal_prev['clonal_prev']+0.0000001
fishplot_clonal_prev
fishplot_tree_edges=as.data.frame(adj_matrix)
#fishplot_tree_edges['source']=rownames(fishplot_tree_edges)
fishplot_tree_edges['source']=rownames(prevalence)
colnames(fishplot_tree_edges)=c(fishplot_tree_edges[['source']],'source')
fishplot_tree_edges=gather(fishplot_tree_edges, 'target','value', -source)
fishplot_tree_edges=fishplot_tree_edges[fishplot_tree_edges['value']==1,][c('source', 'target')]
fishplot_tree_edges
fishplot_colour=t(as.data.frame(colours))
fishplot_colour=data.frame(clone_id=rownames(fishplot_colour), colour=fishplot_colour[,1])
log2_print(getwd())
#source(timescape.R)
#source(paste0(basedir,"/peter/LACEView/R/timescape.R"))
x=timescape(clonal_prev = fishplot_clonal_prev,
tree_edges = fishplot_tree_edges, height=height_fishplot,
width=width_fishplot, alpha=0, genotype_position="stack",
clone_colours = fishplot_colour)
tabula=data.frame(T=c(paste0('T',times_n)), alpha=error_rates$alpha,
beta=error_rates$beta,
m_tilde=length(rownames(prevalence))-1,
n=unlist(lapply(C_mat,length)))
rownames(tabula)=c(paste0('T',times_n))
tabula
headings=c("<p style=' font-size: 14px'>T</p>",
"<p style=' font-size: 14px'>alpha</p>
<p style='font-weight:normal; margin-top: -20px; margin-bottom: -20px; font-size: 12px'>est.</p>",
"<p style=' font-size: 14px'>beta</p>
<p style='font-weight:normal; margin-top: -20px; margin-bottom: -20px; font-size: 12px'>est.</p>",
paste("<p style=' font-size: 14px'>m_tilde</p>",
#<p style='font-weight:normal; margin-top: -20px; margin-bottom: -20px; font-size: 12px'>n candidate drivers</p>",
"<p style='font-weight:normal; margin-top: -20px; margin-bottom: -20px; font-size: 12px'>n drivers</p>"),
"<p style=' font-size: 14px'>n</p>
<p style='font-weight:normal; margin-top: -20px; margin-bottom: -20px; font-size: 12px'>single cells</p>"
)
tab={}
tab$headings=headings
tab$data=t(t(tabula))
#library(jsonlite)
toJSON(tab)
#-------------------
clonalprev_df <- prevalence
clonalprev_df = clonalprev_df[,!(colnames(clonalprev_df) %in% c("Total"))]
clonalprev_df <- subset(clonalprev_df)
clonalprev_df_names <- rownames(clonalprev_df)
stream_df <- data.frame(Time = c(1:ncol(clonalprev_df)))
for (k in clonalprev_df_names) {
temp <- data.frame(k = clonalprev_df[k, ], stringsAsFactors = FALSE)
stream_df <- cbind(stream_df, temp)
}
names(stream_df) <- c("Time", clonalprev_df_names)
stream_df_colors = list()
for (K in 1:length(colours)) {
stream_df_colors[[clonalprev_df_names[K]]] = colours[K]
}
log2_print("info")
log2_print(info)
jsdata = list(elements = elements, data = toJSON(stream_df),
columns = toJSON(names(stream_df)),
colors = toJSON(stream_df_colors),
clone_labels = toJSON(unlist(clone_labels)[-1]),
x=x,
info=toJSON(list(info=info)),
tab=toJSON(tab))
results<-list()
results[["prevalence"]] <- prevalence
results[["colours"]] <- colours
results[["html"]] <- htmlwidgets::createWidget(name = "LACE", jsdata, width = width,
height = height, package = "LACE",
elementId = elementId,
sizingPolicy = htmlwidgets::sizingPolicy(
viewer.suppress = TRUE,
knitr.figure = FALSE,
browser.fill = TRUE,
browser.padding = 0,
knitr.defaultWidth = 300,
knitr.defaultHeight = 600)
)
results
}
####################
####################
#add modified LACEview_html function
####################
LACE_html<-function (id, style, class, ...) {
htmltools::tags$div(id = id, class = class, style = style,
htmltools::div(id = "wrapper",
style = "height:100%; width:100%;",
htmltools::div(id = "navbar"),
htmltools::div(id = "container",
style = "flex-flow: row wrap; display: flex; height:800px; width:100%;",
htmltools::div(
style = "flex:0 0 48%; align-items: center; height:64%; margin: 5px; padding:5px; border: 1px solid black;",
id = "cy_",
htmltools::div(
style = "flex:100%; align-items: center; height:5%;",
id = "cy_title",
htmltools::p(
style = "font-size:20px; text-align: center; font-weight: bold;", "Longitudinal clonal tree"
),
),
htmltools::div(
style = "flex:100%; align-items: center; height:95%;",
id = "cy"
),
),
htmltools::div(
style = "flex: 0 0 48%; align-items: center; height:64%; margin: 5px; padding:5px; border: 1px solid black;",
id = "streamgraph_",
htmltools::div(
style = "flex:100%; align-items: center; height:5%;",
id = "streamgraph_title",
htmltools::p(
style = "font-size:20px; text-align: center; font-weight: bold;", "Fishplot"
),
),
htmltools::div(
style = "flex:100%; align-items: center; height:95%; font-size:20px; font-weight: normal;",
id = "streamgraph"
),
),
htmltools::div(
style = "flex:100%; height:8%; display:flex; align-items: center; justify-content:flex-start; margin-left: 40px;",
id = "mutations"
),
htmltools::div(
style = "flex:100%; height:35%; display:flex; align-items: center; justify-content:center;",
id = "lacetable",
htmltools::div(
style = "flex:50%; height:100%; display:flex; align-items: flex-end; justify-content:flex-start; font-size:14px;",
id = "legend"
),
htmltools::div(
style = "flex:50%; height:100%; display:flex; align-items: center; justify-content:center; font-size:14px;",
id = "table",
class='table'
),
htmltools::div(
style = "flex:100%; height:100%; display:none; align-items: flex-end; justify-content:center;",
id = "other"
)
)
)
)
)
}
####################
## Shiny bindings for LACE
##
## Output and render functions for using LACEview within Shiny
## applications and interactive Rmd documents.
# #' @export
LACEOutput <- function(outputId, width = '100%', height = '400px') {
htmlwidgets::shinyWidgetOutput(outputId,
'LACE',
width,
height,
package = 'LACE')
}
# #' @rdname LACE-shiny
# #' @export
renderLACE <- function(expr, env = parent.frame(), quoted = FALSE) {
if (!quoted) { expr <- substitute(expr) } # force quoted
htmlwidgets::shinyRenderWidget(expr, LACEOutput, env, quoted = TRUE)
}
### end of file -- LACEview2.R
BIMIB-DISCo/LACE documentation built on April 24, 2024, 7:22 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions renderLACE LACEOutput
## version 1118
### LACEview2.R
###
### Add lace_interface function
#' @title LACE Interface
#' @description This function generates a longitudinal clonal tree and a
#' graphic interface to explore the data using as input the clonal tree
#' formatted in the same way as the one produced by LACE during the imputation
#' steps
#'
#' @param B_mat (Required). B is the clonal tree matrix where columns are the
#' clonal mutations and and rows are the clones. The clonal tree matrix should
#' contain a column and a row named "Root" representing the root of the tree and
#' the wild type, respectively.
#' B is a binary matrix where 1 are the mutations associated to the clones.
#' The wild type column has all ones
#'
#' @param clones_prevalence (Required) The clonal prevalence matrix
#'
#' @param C_mat (Required) The corrected clonal attachment
#'
#' @param error_rates (Required) The false positive alpha and false negative beta
#' error rates used to infer the clonal tree
#'
#' @param info (Optional). HTML formatted text with information regarding
#' the experiments
#'
#' @param width (optional) Size of the window interafce
#'
#' @param height (optional) Size of the window interafce
#'
#' @param elementId (optional) Element id
#'
#' @return An implementation of the htmlwidgets
#'
#' @importFrom Matrix rankMatrix
#' @importFrom jsonlite toJSON
#' @importFrom grDevices colorRampPalette
#' @importFrom purrr is_null
#' @importFrom tidyr gather
#' @importFrom purrr detect_index
#' @import stringr
#' @import stringi
#' @import RColorBrewer
#'
#'
#'
# #' @export
lace_interface <- function (B_mat,
clones_prevalence,
C_mat,
error_rates,
width = NULL,
height = NULL,
elementId = NULL,
info = '') {
## library(purrr, quietly = T)
## library(stringr, quietly = T)
## library(stringi, quietly = T)
## library(Matrix, quietly = T)
if (!exists('width')) {width=NULL}
if (!exists('height')) {height=NULL}
if (!exists('elementId')) {elementId=NULL}
if (!exists('info')) {info=''}
if (is_null(height)) {
height=900
height_fishplot=300
} else if (height<800) {
height_fishplot=300
} else {
height_fishplot=300
}
if (is_null(width)) {
width=900
width_fishplot=width/2
} else if (width<900) {
width_fishplot=width/2
} else {
width_fishplot=width/2
}
if (is_null(info)) {
info = 'No dataset or experimental info available'
}
B = B_mat
B
make_binary<-function(B) {
if (!is.numeric(B)){
log2_print('B is not a valid matrix')
return(NULL)
}
is_binary=sum(B==1)+sum(B==0)==ncol(B)*nrow(B)
if (!is_binary) {
log2_print('B is not binary')
B[B==1]=1
B[B==0]=0
}
else {
log2_print('B is already binary')
}
return(B)
}
find_roots <- function(B) {
#find roots
set_of_roots = rownames(B)
set_of_roots
for (i in set_of_roots) {
tmp_set_of_roots=setdiff(set_of_roots,i)
for (j in tmp_set_of_roots) {
proj=sum(B[i,]*B[j,])
if (sum(B[i,])==proj) {
set_of_roots=setdiff(set_of_roots,j)
log2_print(paste(i,'is parent of', j))
#print(set_of_roots)
}
if (sum(B[j,])==proj) {
set_of_roots=setdiff(set_of_roots,i)
log2_print(paste(j,'is parent of', i))
#print(set_of_roots)
}
}
}
set_of_roots
return(set_of_roots)
}
forest_check <- function(B) {
#check if it is a forest
check_forest=matrix(data = 0, nrow=nrow(B),ncol=1)
check_forest
check_forest_control=matrix(data = 1, nrow=nrow(B),ncol=1)
check_forest_control[1,1]=0
for(i in 1:nrow(B)) {
check_forest_tmp_=0
for(j in i:nrow(B)) {
check_forest_tmp=(sum(abs(B[j,]-B[i,]))==1)
check_forest[j,1]=check_forest[j,1]+check_forest_tmp
#check_forest_tmp_ = check_forest_tmp_+ check_forest_tmp
log2_print(paste(rownames(B)[i], rownames(B)[j], check_forest[j,1]))
}
#check_forest=rbind(check_forest, check_forest_tmp_)
}
log2_print(check_forest)
log2_print(check_forest_control)
log2_print(all(check_forest==check_forest_control))
is_forest=all(check_forest==check_forest_control)
if (is_forest) {
log2_print("B represents a forest")
} else {
log2_print("B is not a forest or a tree")
}
return(is_forest)
}
add_unique_root <- function(B, root_name='Root') {
#add unique root if necessary
B_rank=rankMatrix(B)[1]
set_of_roots = find_roots(B)
#check if it has one root
num_roots=length(set_of_roots)
if (num_roots==1) {
log2_print("nodes in B are sprouting from a single node")
}
else {
log2_print("there are multiple roots")
}
if (B_rank==nrow(B) || B_rank==ncol(B)) {
log2_print('B is a full-rank matrix.')
if (forest_check(B)) {
log2_print('B has a forest like structure')
if (nrow(B)>ncol(B)) {
log2_print('B is not a square matrix. B has not enough mutations.')
#print('B is not a full rank matrix. B has not enough mutations.')
if (nrow(B)==ncol(B)+1) {
#check if there is a root
if (num_roots!=1 && root_col_name!=root_name) {
root_col_name=root_name
B = cbind(xxx=1,B)
colnames(B)[colnames(B) == 'xxx'] <- root_name
}
}
} else if (nrow(B)<ncol(B)) {
log2_print('B is not a square matrix. B has not enough clones.')
if (nrow(B)+1==ncol(B)) {
lower_tri_column_index=sort(colSums(B),decreasing = TRUE)
#number of clones with such mutation
clones_per_mutation=table(lower_tri_column_index)
max_clones_per_mutation_idx=(
max(as.integer(names(clones_per_mutation))))
max_clones_per_mutation_idx
if (max_clones_per_mutation_idx<B_rank && root_row_name!=root_name) {
root_row_name='Root'
B = rbind(xxx=0,B)
rownames(B)[rownames(B) == 'xxx'] <- root_name
B[1,1]=1
}
}
}
}
}
B_rank=rankMatrix(B)[1]
B_rank
if (!(B_rank==ncol(B) && B_rank==nrow(B))) {
log2_print('Adding a root does not resolve the problem, B is till not a full rank matrix')
}
return(B)
}
make_lowtri<-function(B) {
#make B lower triangular
B_tmp=matrix(nrow=0, ncol=ncol(B))
colnames(B_tmp)=colnames(B)
B_tmp
B2=B #B_row_reduced
B3=B2 #B_row_reduced_col_ordered
B4=B3 #B_row_col_reduced_col_ordered
set_of_roots = rownames(B2)
for(i in set_of_roots) {
i=nrow(B)-length(set_of_roots)+1
i
first_r=names(which.min(rowSums(B4)))
first_r
B_tmp=rbind(B_tmp, B3[first_r, ,drop=FALSE])
B_tmp
if (i==nrow(B)){
break
}
if (i>1) {
one_col=B_tmp[first_r,-1:-i+1]==1
zero_col=B_tmp[first_r,-1:-i+1]==0
} else {
one_col=B_tmp[first_r,]==1
zero_col=B_tmp[first_r,]==0
}
one_col=names(one_col[one_col])
one_col
zero_col=names(zero_col[zero_col])
zero_col
B2=B2[rownames(B2) !=first_r, , drop=FALSE]
B2
B3=cbind(B2[,colnames(B_tmp[,1:i-1,drop=FALSE]),drop=FALSE],
B2[,one_col,drop=FALSE],B2[,zero_col,drop=FALSE])
B3
B4=cbind(B2[,one_col,drop=FALSE],B2[,zero_col,drop=FALSE])
B4
#B2=B2[,-1]
B_tmp=cbind(B_tmp[,1:i-1,drop=FALSE], B_tmp[,one_col,drop=FALSE],
B_tmp[,zero_col,drop=FALSE])
B_tmp
set_of_roots=setdiff(set_of_roots,first_r)
}
B=B_tmp
return(B)
}
#####
B=make_binary(B)
#B_rank=rankMatrix(B)[1]
#B_rank
#is_forest = forest_check(B)
#is_forest
lower_tri_column_index=sort(colSums(B),decreasing = TRUE)
root_col_name=names(which.max(lower_tri_column_index))
root_row_name=names(which.min(sort(rowSums(B),decreasing = TRUE)))
lower_tri_column_index
root_col_name
root_row_name
B<-add_unique_root(B)
B<-make_lowtri(B)
lower_tri_column_index=sort(colSums(B),decreasing = TRUE)
root_col_name=names(which.max(lower_tri_column_index))
root_row_name=names(which.min(sort(rowSums(B),decreasing = TRUE)))
lower_tri_column_index
root_col_name
root_row_name
#check all
is_binary = sum(B==1)+sum(B==0)==ncol(B)*nrow(B)
is_square_mat = ncol(B)==nrow(B)
is_full_rank = rankMatrix(B)[1]==nrow(B)
is_forest = forest_check(B)
has_single_root = length(find_roots(B))==1
if (is_binary) {log2_print('B is a binary matrix')}
if (is_square_mat) {log2_print('B is a square matrix')}
if (is_full_rank) {log2_print('B is a full rank matrix')}
if (is_forest) {log2_print('B is a forest ')}
#browser()
if (has_single_root) {log2_print('B has single root')}
if (is_binary && is_square_mat && is_full_rank && is_forest && has_single_root) {
log2_print('all checks done!')
log2_print('continue...')
}
#this if else is not necessary in this form. Instead, it should set root names
if ("Root" %in% rownames(clones_prevalence)){
prevalence=clones_prevalence
} else {
prevalence=rbind(Root=0,clones_prevalence)
}
prevalence=prevalence[rownames(B),]
clone_labels=as.list(str_split(colnames(B), pattern='_', n=2, simplify = TRUE)[,1])
##clone_labels=str_split(colnames(B), pattern='_', n=2, simplify = T)[,1]
names(clone_labels)<-rownames(B)
clone_labels
#library("RColorBrewer")
#colours = as.list(brewer.pal(n = nrow(prevalence), name = "Paired"))
colours = as.list(colorRampPalette(brewer.pal(12, "Paired"))(nrow(prevalence)))
names(colours)<-rownames(B)
colours
#create adjacency matrix
adj_matrix = array(0L, c((dim(B)[1]), (dim(B)[2])))
rownames(adj_matrix) <- colnames(B)[(1:ncol(B))]
colnames(adj_matrix) <- colnames(B)[(1:ncol(B))]
for (rP in seq(1, nrow(B), by=1) ) {
for (rC in seq((rP + 1), nrow(B), length = max(0,nrow(B)-(rP + 1)+1))) {
if (all(B[rP, 1:rP] == B[rC, 1:rP]) && sum(B[rP, ]) == (sum(B[rC, ]) - 1)) {
adj_matrix[(rP ), (rC )] <- 1
}
}
}
log2_print('clonal tree:')
log2_print(adj_matrix)
adj_to_B <- function( adj_matrix, clone_labels ) {
B=matrix(0, nrow=nrow(adj_matrix), ncol=ncol(adj_matrix))
colnames(B)=colnames(adj_matrix)
rownames(B)=rownames(adj_matrix)
B[1,1]=1
for (s in rownames(adj_matrix)) {
for (t in colnames(adj_matrix)) {
if (adj_matrix[s,t]==1) {
B[t,]=B[s,]
B[t,t]=1
}
}
}
rownames(B)=names(clone_labels)
return(B)
}
log2_print(adj_to_B(adj_matrix, clone_labels))
#adjMatrix_base <- adj_matrix
####
#it resolves all the incongruences resulting from not considering the time at all
chrono_occurences <- function(adj_matrix, prevalence) {
#create first time mutations appearance based on clonal prevalence only
times= apply((prevalence)>0, 1, function(x) detect_index(x, ~ .x == TRUE) )
#times=times+1 #test
times[1]=1 #setting root
times_pre<-times
adj_matrix_t_pre<-adj_matrix
colnames(adj_matrix_t_pre) <-paste0(rownames(prevalence), '_t', times)
rownames(adj_matrix_t_pre) <-paste0(rownames(prevalence), '_t', times)
#create first time mutations appearance based on chronological order
sources=apply( t(adj_matrix)>0, 1, function(x) detect_index(x, ~ .x == TRUE) )
sources[1]=1
for(target in 1: length(rownames(prevalence))) {
source=sources[target]
if (times[target] < times[source]) {
log2_print(times[target],times[source])
times[source]=times[target]
}
}
adj_matrix_t<-adj_matrix
colnames(adj_matrix_t) <-paste0(rownames(prevalence), '_t', times)
rownames(adj_matrix_t) <-paste0(rownames(prevalence), '_t', times)
if (all(times==times_pre)) {
log2_print('No incongruences found due to the chronological order')
log2_print('first time mutation occurrences based the chronological order of samples:')
log2_print(times)
log2_print('adjacent matrix with first time occurrences:')
log2_print(adj_matrix_t)
} else {
log2_print('The longitudinal clonal tree has been corrected')
log2_print('pre-computation of first time mutation occurrences based only on prevalence values different from zero:')
log2_print(times_pre)
log2_print('adjacent matrix with pre-computed first time occurrences:')
log2_print(adj_matrix_t_pre)
log2_print('first time mutation occurrences based the chronological order of samples:')
log2_print(times)
log2_print('adjacent matrix with first time occurrences:')
log2_print(adj_matrix_t)
}
return(times)
}
times <- chrono_occurences(adj_matrix,prevalence)
adj_matrix_t<-adj_matrix
colnames(adj_matrix_t) <-paste0(rownames(prevalence), '_t', times)
rownames(adj_matrix_t) <-paste0(rownames(prevalence), '_t', times)
## adding the other elements of the direct product
times_n= 1:(length(colnames(prevalence))-1)
times_n
for (t in times_n) {
for (col in 1:ncol(adj_matrix)) {
col_ = paste0(rownames(prevalence)[col],'_t',t)
if (!(col_ %in% colnames(adj_matrix_t))) {
adj_matrix_t=cbind(adj_matrix_t, xxx=0)
colnames(adj_matrix_t)[colnames(adj_matrix_t) == 'xxx'] <- col_
}
}
for (row in 1: nrow(adj_matrix)) {
row_ = paste0(rownames(prevalence)[row],'_t',t)
if (!(row_ %in% rownames(adj_matrix_t))) {
adj_matrix_t=rbind(adj_matrix_t, xxx=0)
rownames(adj_matrix_t)[rownames(adj_matrix_t) == 'xxx'] <- row_
}
}
}
## Root as wild type should be considered present in all the next time steps:
## hence an accessory self link is used
adj_matrix_t['Root_t1','Root_t1']=1
## setting links of clones in following times after first occurrence
for (t_1 in times_n[1:(length(times_n)-1)]) {
log2_print(c('time=',t_1))
for (source in 1:nrow(adj_matrix)) {
for (target in 1:ncol(adj_matrix)) {
for (t_2 in times_n[t_1:(length(times_n)-1)]) { # r fa cacare
#source mut_time
source_t1 = paste0(rownames(prevalence)[source],'_t',t_1)
#target mut_time
target_t2 = paste0(rownames(prevalence)[target],'_t',t_2)
#print(c(source_t1, target_t2, adj_matrix_t[source_t1,target_t2]))
#source mut_time
source_t2 = paste0(rownames(prevalence)[target],'_t',t_2)
#target mut_time+1
target_t2_next=paste0(rownames(prevalence)[target],'_t',t_2+1)
if (adj_matrix_t[source_t1,target_t2]>=1 && adj_matrix_t[source_t1,target_t2]<=3) {
if (target==source && prevalence[target,t_2]==0){
value=3 #for dashed in case undetected clones
log2_print(paste0(source_t1, "->", target_t2,"=", adj_matrix_t[source_t1,target_t2], ", prev_",target_t2, "=", prevalence[target,t_2], ": ", source_t1, "->", target_t2, "=", value))
adj_matrix_t[source_t1, target_t2]<-value # we set the link before t2
log2_print(paste0(source_t1, "->", target_t2,"=", adj_matrix_t[source_t1,target_t2], ", prev_",target_t2, "=", prevalence[target,t_2], ": ", source_t2, "->", target_t2_next, "=", value))
adj_matrix_t[source_t2, target_t2_next]<-value # we set the link after t2
}
else {
value=2 #for time persistence
log2_print(paste0("1st ",source_t1, "->", target_t2,"=", adj_matrix_t[source_t1,target_t2], ", prev_",target_t2, "=", prevalence[target,t_2], ": ", source_t2, "->", target_t2_next, "=", value))
adj_matrix_t[source_t2,target_t2_next]<-value
}
if (target==source && t_2+1==length(times_n) && prevalence[target,t_2+1]==0){
value=3 #for dashed in case undetected clones
log2_print(paste0(source_t1, "->", target_t2,"=", adj_matrix_t[source_t1,target_t2], ", prev_",target_t2, "=", prevalence[target,t_2], ": ", source_t2, "->", target_t2_next, "=", value))
adj_matrix_t[source_t2, target_t2_next]<-value # we set the link after t2
}
}
#if (target==source && t_1+1==t_2 && prevalence[target,t_2]==0) {
#target_t1 = paste0(rownames(prevalence)[target],'_t',t_1) #t1=t2-1
# = paste0(rownames(prevalence)[source],'_t',t_2)
#target_t2 = paste0(rownames(prevalence)[target],'_t',t_2)
#target mut_time+1
#target_t2_next=paste0(rownames(prevalence)[target],'_t',t_2+1)
#value=3 #for dashed in case undetected clones
# print(c(source_t1, target_t2, source_t2, target_t2_next, value))
# print(c(source_t1, target_t2, source_t1, target_t2, value))
# adj_matrix_t[source_t2, target_t2_next]<-value # we set the link after t2
# adj_matrix_t[source_t1, target_t2]<-value # we set the link before t2
#}
}
}
}
}
#dead breaches
#in time reversal
#prevalence=prevalence
for (t_1 in times_n[(length(times_n)):1]) { # instead of 2 as smaller point
log2_print(c('time=',t_1))
for (source in 1:nrow(adj_matrix)) {
target=source # correlation only at distance 0
source_t1_prev =paste0(rownames(prevalence)[source],'_t',t_1-1)
target_t1 = paste0(rownames(prevalence)[target],'_t',t_1)
source_t1 =paste0(rownames(prevalence)[source],'_t',t_1)
target_t1_next = paste0(rownames(prevalence)[target],'_t',t_1+1)
if (t_1==times_n[(length(times_n))] && prevalence[target,t_1]==0 ){ #&& prevalence[source,t_1-1]==0) {
value=0 #for dead breaches
log2_print(c(source_t1, target_t1, source_t1_prev, target_t1, value))
adj_matrix_t[source_t1_prev,target_t1]<-value # we set the link after t2
#prevalence[prevalence[,t_1]==0,t_1]=-1
prevalence[target,t_1]=-1
} else if (prevalence[source,t_1]==0 && adj_matrix_t[source_t1,target_t1_next]==0) {
#target mut_time+1
value=0 #for dead breaches
log2_print(c('removed', target_t1))
log2_print(c(source_t1, target_t1, source_t1, target_t1_next, value))
adj_matrix_t[source_t1,target_t1_next]<-value # we set the link after t2
if (t_1!=1){
log2_print(c(source_t1, target_t1, source_t1_prev, target_t1, value))
adj_matrix_t[source_t1_prev,target_t1]<-value # we set the link after t2
}
#prevalence[prevalence[,t_1]==0,t_1]=-1
if (!(stri_detect_fixed(target_t1,"Root") && t_1==1)){
prevalence[target,t_1]=-1
}
}
}
}
## removal of the accessory self link
adj_matrix_t['Root_t1','Root_t1']=0
colnames(prevalence)
elements = list()
elements$nodes = list()
elements$edges = list()
for (row in 1:nrow(adj_matrix_t)) {
for (col in 1:ncol(adj_matrix_t)) {
if (adj_matrix_t[row, col] == 1) {
data = list()
data$data = list()
clone_=stri_reverse(str_split(
stri_reverse(colnames(adj_matrix_t)[col]),pattern='_', n=2)[[1]][2])
clone_idx=match(clone_,rownames(B))
log2_print(c('idx',clone_))
data$data$source = sprintf("%s", rownames(adj_matrix_t)[row])
data$data$target = sprintf("%s", colnames(adj_matrix_t)[col])
data$data$name = sprintf("%s", clone_labels[clone_][[1]])
#data$data$color = colours[clone_idx]
data$data$color = colours[clone_][[1]]
data$data$id = sprintf("%s_%s", data$data$source, data$data$target)
data$data$linestyle = "solid"
elements$edges = c(elements$edges, list(data))
}
if (adj_matrix_t[row, col] == -200) { #if (adj_matrix_t[row, col] == 2) {
data = list()
data$data = list()
clone_=stri_reverse(str_split(stri_reverse(
colnames(adj_matrix_t)[col]),pattern='_', n=2)[[1]][2])
#color_idx=match(clone_,rownames(B))
data$data$source = sprintf("%s", rownames(adj_matrix_t)[row])
data$data$target = sprintf("%s", colnames(adj_matrix_t)[col])
data$data$name = sprintf("")
data$data$color = colours[clone_][[1]]
data$data$id = sprintf("%s_%s", data$data$source, data$data$target)
data$data$linestyle = "solid" # only if next clonal_prev==0 else solid
elements$edges = c(elements$edges, list(data))
}
if (adj_matrix_t[row, col] >= 2) { #if (adj_matrix_t[row, col] == 3) {
data = list()
data$data = list()
clone_=stri_reverse(str_split(stri_reverse(
colnames(adj_matrix_t)[col]),pattern='_', n=2)[[1]][2])
#color_idx=match(clone_,rownames(B))
data$data$source = sprintf("%s", rownames(adj_matrix_t)[row])
data$data$target = sprintf("%s", colnames(adj_matrix_t)[col])
data$data$name = sprintf("")
data$data$color = colours[clone_][[1]]
data$data$id = sprintf("%s_%s", data$data$source, data$data$target)
data$data$linestyle = "dashed" # only if next clonal_prev==0 else solid
elements$edges = c(elements$edges, list(data))
}
}
}
#nodes
for (col in 1:(ncol(prevalence) - 1)) {
data = list()
data$data = list()
data$data$id = sprintf("T%s", col)
data$data$name = sprintf("T%s", col) #experiment2...
data$data$color = "#FFFFFF"
data$data$size = sprintf("%spx", 50)
data$data$prev = sprintf("%s [%s]", data$data$name, round(prevalence[1, col], 2))
parent = data$data$name
elements$nodes = c(elements$nodes, list(data))
for (row in 1:nrow(prevalence)) {
if (prevalence[row,col]!=-1) {
data = list()
data$data = list()
data$data$name = rownames(prevalence)[row]
data$data$color = colours[row][[1]]
data$data$id = sprintf("%s_t%s", rownames(prevalence)[row], col)
data$data$size = sprintf("%spx", 5 + as.integer(prevalence[row, col] * 150))
data$data$prev = sprintf("%s", round(prevalence[row, col], 2))
data$data$parent = parent
elements$nodes = c(elements$nodes, list(data))
}
}
}
#-------------------
#library(tidyr)
if ("Root" %in% rownames(clones_prevalence)){
prevalence=clones_prevalence
} else {
prevalence=rbind(Root=0,clones_prevalence)
}
prevalence=prevalence[rownames(B),]
fishplot_clonal_prev=as.data.frame(prevalence)
fishplot_clonal_prev = fishplot_clonal_prev[,!(names(fishplot_clonal_prev) %in% c("Total"))]
fishplot_clonal_prev=subset(fishplot_clonal_prev)
colnames(fishplot_clonal_prev)=c(paste0('T',times_n))
fishplot_clonal_prev['clone_id']=rownames(fishplot_clonal_prev)
fishplot_clonal_prev
fishplot_clonal_prev=gather(fishplot_clonal_prev,
'timepoint','clonal_prev', -'clone_id' )
fishplot_clonal_prev=fishplot_clonal_prev[c('timepoint',
'clone_id','clonal_prev')]
fishplot_clonal_prev['clonal_prev']=fishplot_clonal_prev['clonal_prev']+0.0000001
fishplot_clonal_prev
fishplot_tree_edges=as.data.frame(adj_matrix)
#fishplot_tree_edges['source']=rownames(fishplot_tree_edges)
fishplot_tree_edges['source']=rownames(prevalence)
colnames(fishplot_tree_edges)=c(fishplot_tree_edges[['source']],'source')
fishplot_tree_edges=gather(fishplot_tree_edges, 'target','value', -source)
fishplot_tree_edges=fishplot_tree_edges[fishplot_tree_edges['value']==1,][c('source', 'target')]
fishplot_tree_edges
fishplot_colour=t(as.data.frame(colours))
fishplot_colour=data.frame(clone_id=rownames(fishplot_colour), colour=fishplot_colour[,1])
log2_print(getwd())
#source(timescape.R)
#source(paste0(basedir,"/peter/LACEView/R/timescape.R"))
x=timescape(clonal_prev = fishplot_clonal_prev,
tree_edges = fishplot_tree_edges, height=height_fishplot,
width=width_fishplot, alpha=0, genotype_position="stack",
clone_colours = fishplot_colour)
tabula=data.frame(T=c(paste0('T',times_n)), alpha=error_rates$alpha,
beta=error_rates$beta,
m_tilde=length(rownames(prevalence))-1,
n=unlist(lapply(C_mat,length)))
rownames(tabula)=c(paste0('T',times_n))
tabula
headings=c("<p style=' font-size: 14px'>T</p>",
"<p style=' font-size: 14px'>alpha</p>
<p style='font-weight:normal; margin-top: -20px; margin-bottom: -20px; font-size: 12px'>est.</p>",
"<p style=' font-size: 14px'>beta</p>
<p style='font-weight:normal; margin-top: -20px; margin-bottom: -20px; font-size: 12px'>est.</p>",
paste("<p style=' font-size: 14px'>m_tilde</p>",
#<p style='font-weight:normal; margin-top: -20px; margin-bottom: -20px; font-size: 12px'>n candidate drivers</p>",
"<p style='font-weight:normal; margin-top: -20px; margin-bottom: -20px; font-size: 12px'>n drivers</p>"),
"<p style=' font-size: 14px'>n</p>
<p style='font-weight:normal; margin-top: -20px; margin-bottom: -20px; font-size: 12px'>single cells</p>"
)
tab={}
tab$headings=headings
tab$data=t(t(tabula))
#library(jsonlite)
toJSON(tab)
#-------------------
clonalprev_df <- prevalence
clonalprev_df = clonalprev_df[,!(colnames(clonalprev_df) %in% c("Total"))]
clonalprev_df <- subset(clonalprev_df)
clonalprev_df_names <- rownames(clonalprev_df)
stream_df <- data.frame(Time = c(1:ncol(clonalprev_df)))
for (k in clonalprev_df_names) {
temp <- data.frame(k = clonalprev_df[k, ], stringsAsFactors = FALSE)
stream_df <- cbind(stream_df, temp)
}
names(stream_df) <- c("Time", clonalprev_df_names)
stream_df_colors = list()
for (K in 1:length(colours)) {
stream_df_colors[[clonalprev_df_names[K]]] = colours[K]
}
log2_print("info")
log2_print(info)
jsdata = list(elements = elements, data = toJSON(stream_df),
columns = toJSON(names(stream_df)),
colors = toJSON(stream_df_colors),
clone_labels = toJSON(unlist(clone_labels)[-1]),
x=x,
info=toJSON(list(info=info)),
tab=toJSON(tab))
results<-list()
results[["prevalence"]] <- prevalence
results[["colours"]] <- colours
results[["html"]] <- htmlwidgets::createWidget(name = "LACE", jsdata, width = width,
height = height, package = "LACE",
elementId = elementId,
sizingPolicy = htmlwidgets::sizingPolicy(
viewer.suppress = TRUE,
knitr.figure = FALSE,
browser.fill = TRUE,
browser.padding = 0,
knitr.defaultWidth = 300,
knitr.defaultHeight = 600)
)
results
}
####################
####################
#add modified LACEview_html function
####################
LACE_html<-function (id, style, class, ...) {
htmltools::tags$div(id = id, class = class, style = style,
htmltools::div(id = "wrapper",
style = "height:100%; width:100%;",
htmltools::div(id = "navbar"),
htmltools::div(id = "container",
style = "flex-flow: row wrap; display: flex; height:800px; width:100%;",
htmltools::div(
style = "flex:0 0 48%; align-items: center; height:64%; margin: 5px; padding:5px; border: 1px solid black;",
id = "cy_",
htmltools::div(
style = "flex:100%; align-items: center; height:5%;",
id = "cy_title",
htmltools::p(
style = "font-size:20px; text-align: center; font-weight: bold;", "Longitudinal clonal tree"
),
),
htmltools::div(
style = "flex:100%; align-items: center; height:95%;",
id = "cy"
),
),
htmltools::div(
style = "flex: 0 0 48%; align-items: center; height:64%; margin: 5px; padding:5px; border: 1px solid black;",
id = "streamgraph_",
htmltools::div(
style = "flex:100%; align-items: center; height:5%;",
id = "streamgraph_title",
htmltools::p(
style = "font-size:20px; text-align: center; font-weight: bold;", "Fishplot"
),
),
htmltools::div(
style = "flex:100%; align-items: center; height:95%; font-size:20px; font-weight: normal;",
id = "streamgraph"
),
),
htmltools::div(
style = "flex:100%; height:8%; display:flex; align-items: center; justify-content:flex-start; margin-left: 40px;",
id = "mutations"
),
htmltools::div(
style = "flex:100%; height:35%; display:flex; align-items: center; justify-content:center;",
id = "lacetable",
htmltools::div(
style = "flex:50%; height:100%; display:flex; align-items: flex-end; justify-content:flex-start; font-size:14px;",
id = "legend"
),
htmltools::div(
style = "flex:50%; height:100%; display:flex; align-items: center; justify-content:center; font-size:14px;",
id = "table",
class='table'
),
htmltools::div(
style = "flex:100%; height:100%; display:none; align-items: flex-end; justify-content:center;",
id = "other"
)
)
)
)
)
}
####################
## Shiny bindings for LACE
##
## Output and render functions for using LACEview within Shiny
## applications and interactive Rmd documents.
# #' @export
LACEOutput <- function(outputId, width = '100%', height = '400px') {
htmlwidgets::shinyWidgetOutput(outputId,
'LACE',
width,
height,
package = 'LACE')
}
# #' @rdname LACE-shiny
# #' @export
renderLACE <- function(expr, env = parent.frame(), quoted = FALSE) {
if (!quoted) { expr <- substitute(expr) } # force quoted
htmlwidgets::shinyRenderWidget(expr, LACEOutput, env, quoted = TRUE)
}
### end of file -- LACEview2.R
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.