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SpatialSimul5_Final2.R
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SpatialSimul5_Final2.R
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# if (!require("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#
# BiocManager::install("OncoSimulR")
# install.packages("scatterplot3d")
# install.packages("viridis")
library(viridis)
library(OncoSimulR)
library(parallel)
library(dplyr)
library(scatterplot3d)
## Definimos genotipos:
fe <- allFitnessEffects(
data.frame(parent = c("Root", "Root", "i"),
child = c("u" , "i" , "v"),
s = c(0.1 , -0.05 , 0.25),
sh = -1,
typeDep = "MN"),
epistasis = c("u:i" = -1,"u:v" = -1))
evalAllGenotypes (fe , order = FALSE, addwt = TRUE)
## Function for creating the output of SpatialOncoSimul and the SpatialOncoSimul
## object type.
to_SpatialOncoSimul <- function(x, spatialIterMax, ...){
UseMethod("to_SpatialOncoSimul")
}
## Take a list and return (if possible) a SpatialOncoSimul object.
to_SpatialOncoSimul.list <- function (finalgrid_list, spatialIterMax, ...){
JoinGrids <- bind_rows(finalgrid_list)
TotalPop <- sum(JoinGrids$N)
TotalDems <- length(finalgrid_list)
TotalIte <- spatialIterMax - 1
PopGenotypes <- aggregate(N ~ Genotype, data = JoinGrids, sum)
ListGenotypes <- PopGenotypes$Genotype
PopPerGenotypes <- PopGenotypes$N
DemsGenotype <- c()
for (Genotype in ListGenotypes){
DemsPerGenotype <- nrow(JoinGrids[which(JoinGrids$Genotype == Genotype
& JoinGrids$N != 0), ])
DemsGenotype <- c(DemsGenotype, DemsPerGenotype)
}
print(paste0("Final Population: ", TotalPop, " (", TotalDems, " demes ",
TotalIte, " iterations)"))
for (g in 1:length(ListGenotypes)){
if (ListGenotypes[g] != "" && PopPerGenotypes[g] != 0) {
print(paste0("Genotype: ", ListGenotypes[g], " --- Population: ",
PopPerGenotypes[g], " --- Demes: ", DemsGenotype[g]))
}
}
finalgridspatial <- list(Final_Population = TotalPop, Total_Demes = TotalDems,
Total_Iterations = TotalIte,
List_of_Genotypes = PopGenotypes,
Grids = finalgrid_list)
finalgrid_spatial <- structure(finalgridspatial, class = "SpatialOncoSimul")
return(finalgrid_spatial)
}
## Final function for Spatial Simulation (new arguments have been included
## with respect to OncoSimulIndiv).
SpatialOncoSimul <- function(fp, model = "McFL",
numPassengers = 0, mu = 1e-6, muEF = NULL,
detectionSize = 1e8, detectionDrivers = 4,
detectionProb = NA,
sampleEvery = ifelse(model %in% c("Bozic", "Exp"),
1, 0.025),
initSize = 500, s = 0.1, sh = -1,
K = sum(initSize)/(exp(1) - 1),
keepEvery = sampleEvery,
minDetectDrvCloneSz = "auto",
extraTime = 0,
finalTime = 0.25 * 25 * 365, onlyCancer = FALSE,
keepPhylog = FALSE,
mutationPropGrowth = ifelse(model == "Bozic",
FALSE, TRUE),
max.memory = 2000, max.wall.time = 200,
max.num.tries = 500,
errorHitWallTime = TRUE,
errorHitMaxTries = TRUE,
verbosity = 0,
initMutant = NULL,
AND_DrvProbExit = FALSE,
fixation = NULL,
seed = NULL,
interventions = NULL,
userVars = NULL,
rules = NULL,
migrationProb = 0.5,
largeDistMigrationProb = 1e-6,
maxMigrationPercentage = 0.2,
SpatialModel = "3D",
spatialIterMax = 20,
mc.cores = detectCores() - 1,
SpatialPlot = TRUE) {
if(.Platform$OS.type == "windows") {
if(mc.cores != 1)
message("You are running Windows. Setting mc.cores = 1")
mc.cores <- 1
}
# 1. Function for tumor porgression simulation in a grid.
oncoSimulIndiv_grid <- function(grid){
# If all the cells from a grid disappear N will be 0, so this grid
# will be deleted from the list of current grids. The growth simulation
# inside a grid will only be performed in grids with N > (exp(1) - 1) to
# calculate K initial population equilibrium size.
if (sum(grid$N) > (exp(1) - 1)){
grid <- grid[which(grid$N > 0),]
simul_grid <- invisible(oncoSimulIndiv(fp = fp, model = model,
numPassengers = numPassengers, mu = mu,
muEF = muEF,
detectionSize = detectionSize,
detectionDrivers = detectionDrivers,
detectionProb = detectionProb,
sampleEvery = sampleEvery,
initSize = grid$N,
initMutant = grid$Genotype,
s = s, sh = sh,
K = K, keepEvery = keepEvery,
minDetectDrvCloneSz = minDetectDrvCloneSz,
extraTime = extraTime,
finalTime = finalTime,
onlyCancer = onlyCancer,
keepPhylog = keepPhylog,
mutationPropGrowth = mutationPropGrowth,
max.memory = max.memory,
max.wall.time = max.wall.time,
max.num.tries = max.num.tries,
errorHitWallTime = errorHitWallTime,
errorHitMaxTries = errorHitMaxTries,
verbosity = verbosity,
initMutant = grid$Genotype,
AND_DrvProbExit = AND_DrvProbExit,
fixation = fixation,
seed = seed,
interventions = interventions,
userVars = userVars,
rules = rules))
simul_grid <- data.frame(Genotype = simul_grid$GenotypesLabels,
N = simul_grid$pops.by.time[
nrow(simul_grid$pops.by.time), -1],
Coordinates = I(rep(list(
grid$Coordinates[[1]]),
length(simul_grid$GenotypesLabels))))
# After running OncoSimulIndiv over a grid, if not wild-type
# genotypes are in the results, we add them with a population number
# of 0. This will let us to distiguish between old grids and newly
# created grids in the migration phase.
if (! "" %in% simul_grid$Genotype){
simul_grid <- rbind(simul_grid,
data.frame(Genotype = "",
N = 0,
Coordinates = I(
list(grid$Coordinates[[1]]))))
}
return (simul_grid)
}
}
## Function for selecting the random coordinates for near and remote
## migration.
SimulMigration <- function(grid, migrationProb = 0.5,
largeDistMigrationProb = 1e-6,
maxMigrationPercentage = 0.2){
# Create a function to define random coordinates for near and remote
# migrating cells.
randomcoordinates <- function(grid, MigrationType = "Near"){
init_coordinates <- grid$Coordinates[[1]]
coord_nearmigration <- init_coordinates
coord_remotemigration <- init_coordinates
# While loop to guarantee that coordinates for migrating cells are
# different from the ones of the grid they belong to.
while ((identical(coord_nearmigration, init_coordinates))
& MigrationType == "Near"){
coord_nearmigration <- c()
for (coord in init_coordinates){
new_coord <- as.numeric(sample(seq(coord - 1, coord + 1), 1))
coord_nearmigration <- c(coord_nearmigration, new_coord)
}
}
while ((identical(coord_remotemigration, init_coordinates))
& MigrationType == "Remote"){
coord_remotemigration <- c()
for (coord in init_coordinates){
new_coord <- as.numeric(sample(c(coord - sample(seq(10, 35), 1),
coord +
sample(seq(10, 35), 1)), 1))
coord_remotemigration <- c(coord_remotemigration, new_coord)
}
}
if (MigrationType == "Near"){
return (list(coord_nearmigration))
} else if (MigrationType == "Remote"){
return (list(coord_remotemigration))
}
}
TotalPopSize <- sum(grid$N)
# If the total population corresponds to wild-type ("") genotype, migration
# will not be performed, as only tumor cells are allowed to move.
if ("" %in% grid$Genotype &&
TotalPopSize == grid$N[which(grid$Genotype == "")]){
return (grid)
} else {
near_probtest <- sample(c("Migration", "NoMigration"), 1,
prob = c(migrationProb, 1 - migrationProb))
remote_probtest <- sample(c("Migration", "NoMigration"), 1,
prob = c(largeDistMigrationProb,
1 - largeDistMigrationProb))
## IF THERE IS ANY KIND KIND OF MIGRATION:
if ((near_probtest == "Migration") || (remote_probtest == "Migration"))
{
finalpopcomp_mut <- grid[which((grid$Genotype != "")
& (grid$N > 0)),]
finalpopcomp_mut$N <- finalpopcomp_mut$N/sum(finalpopcomp_mut$N)
MigPopulation <- sample(seq(from = 1, to = TotalPopSize *
maxMigrationPercentage/100), 1)
if (xor((near_probtest == "Migration"),
(remote_probtest == "Migration"))) {
MigPopulationcomp <- as.data.frame(table(
sample(finalpopcomp_mut$Genotype,
MigPopulation,
replace = TRUE,
prob = finalpopcomp_mut$N)))
colnames(MigPopulationcomp) <-c("Genotype", "N")
if (near_probtest == "Migration"){
MigPopulationcomp$Coordinates <- randomcoordinates(
grid = grid, "Near")
} else if (remote_probtest == "Migration"){
MigPopulationcomp$Coordinates <- randomcoordinates(
grid = grid, "Remote")
}
} else if ((near_probtest == "Migration" &
remote_probtest == "Migration")) {
# When both migration probabilities are satisfied for a grid,
# the number of cells that migrate to near and remote places are
# randomly determined (at least one of the migrating cells must
# go to each site).
NearMigPop_number <- sample (from = 1, to = MigPopulation - 1,
1)
RemoteMigPop_number <- MigPopulation - NearMigPop_number
NearMigPopulationcomp <- as.data.frame(table(
sample(finalpopcomp_mut$Genotype,
NearMigPop_number,
replace = TRUE,
prob = finalpopcomp_mut$N)))
RemoteMigPopulationcomp <- as.data.frame(table(
sample(finalpopcomp_mut$Genotype,
RemoteMigPop_number,
replace = TRUE,
prob = finalpopcomp_mut$N)))
colnames(NearMigPopulationcomp) <-c("Genotype", "N")
colnames(RemoteMigPopulationcomp) <-c("Genotype", "N")
NearMigPopulationcomp$Coordinates <- randomcoordinates(
grid = grid, "Near")
RemoteMigPopulationcomp$Coordinates <- randomcoordinates(
grid = grid, "Remote")
MigPopulationcomp <- rbind(NearMigPopulationcomp,
RemoteMigPopulationcomp$Coordinates)
}
# For each genotype of migrating cells, the corresponding number of
# migrating cells is substrated from the original grid.
for (gen in unique(MigPopulationcomp$Genotype)) {
Nmigration_per_genotype <- sum(MigPopulationcomp$N[which(
MigPopulationcomp$Genotype == gen)])
grid$N[which(grid$Genotype == gen)] <- (
grid$N[which(grid$Genotype == gen)] - Nmigration_per_genotype)
}
return (list(grid, MigPopulationcomp))
} else {
# The original grid is returned without any change in
# genotype populations if there is no migration.
return (grid)
}
}
}
## Function for adding wild-type cells to newly created demes.
AddWT_newgrid <- function(grid, initSize) {
# When a new grid is created, it initializes with initSize wild-type
# cells plus the arriving mutant cells. Already existing demes will have
# an empty string in one of the genotypes, indicating the WT (although the
# WT population could be 0).
if (! "" %in% grid$Genotype) {
new_grid <- rbind(grid, data.frame(Genotype = "",
N = initSize,
Coordinates = I(list(
grid$Coordinates[[1]]))))
return (new_grid)
} else {
return (grid)
}
}
## Function for generating the plots corresponding to the spatial simulation.
Simulationplot <- function(SpatialOncoSimGrid, dim){
grid <- SpatialOncoSimGrid$Grids
maxgen <- lapply(grid, function(x) x[x$N == max(x$N), ])
joinmaxgen <- bind_rows(maxgen)
joinmaxgen <- joinmaxgen[joinmaxgen$Genotype != "", ]
maxgencoor <- select(joinmaxgen, c("Genotype", "Coordinates"))
if (dim == "3D"){
coordX <- sapply(maxgencoor$Coordinates, function(x) x[1])
coordY <- sapply(maxgencoor$Coordinates, function(x) x[2])
coordZ <- sapply(maxgencoor$Coordinates, function(x) x[3])
Edgencoord <- data.frame(Genotype = maxgencoor$Genotype,
X = coordX, Y = coordY, Z = coordZ)
scattercolorsu <- plasma(length(unique(Edgencoord$Genotype)))
scattercolors <- scattercolorsu[as.numeric(as.factor(Edgencoord$Genotype))]
s3d <- scatterplot3d(Edgencoord[,2:4], pch = 16, color = scattercolors)
s3d
legend(s3d$xyz.convert((max(coordX)*1.2), (max(coordY)/2),
(max(coordZ)/2)),
legend = levels(as.factor(Edgencoord$Genotype)),
col = scattercolorsu,
pch = 16)
}
if (dim == "2D"){
coordX <- sapply(maxgencoor$Coordinates, function(x) x[1])
coordY <- sapply(maxgencoor$Coordinates, function(x) x[2])
Zdgencoord <- data.frame(Genotype = maxgencoor$Genotype,
X = coordX, Y = coordY)
scattercolorsu <- plasma(length(unique(Zdgencoord$Genotype)))
scattercolors <- scattercolorsu[as.factor(Zdgencoord$Genotype)]
plot(Zdgencoord$X, Zdgencoord$Y, pch = 16, col = scattercolors,
xlab = "X", ylab = "Y")
legend((max(coordX))*0.9, (max(coordY)),
legend = levels(as.factor(Zdgencoord$Genotype)),
col = scattercolorsu,
pch = 16)
}
if (dim == "1D"){
coordX <- sapply(maxgencoor$Coordinates, function(x) x[1])
Idgencoord <- data.frame(Genotype = maxgencoor$Genotype,
X = coordX)
scattercolorsu <- plasma(length(unique(Idgencoord$Genotype)))
scattercolors <- scattercolorsu[as.factor(Idgencoord$Genotype)]
plot(Idgencoord$X, rep(0, length(Idgencoord$X)),
xlab = "", ylab = "",
pch = 16, col = scattercolors)
legend((max(coordX))*0.9, 1,
legend = levels(as.factor(Idgencoord$Genotype)),
col = scattercolorsu,
pch = 16)
}
}
iter <- 1
finalgrid_list <- list()
spatialIterMax <- spatialIterMax + 1
while (iter <= spatialIterMax) {
# 1. Simulation inside each grid. The simulation for each grid is done
# during specific time units (finalTime)
if (length(finalgrid_list) == 0) {
simul_grid <- oncoSimulIndiv(fp = fp, model = model,
numPassengers = numPassengers,
mu = mu,
muEF = muEF,
detectionSize = detectionSize,
detectionDrivers = detectionDrivers,
detectionProb = detectionProb,
sampleEvery = sampleEvery,
initSize = initSize, s = s, sh = sh,
K = K, keepEvery = keepEvery,
minDetectDrvCloneSz = minDetectDrvCloneSz,
extraTime = extraTime,
finalTime = finalTime,
onlyCancer = onlyCancer,
keepPhylog = keepPhylog,
mutationPropGrowth = mutationPropGrowth,
max.memory = max.memory,
max.wall.time = max.wall.time,
max.num.tries = max.num.tries,
errorHitWallTime = errorHitWallTime,
errorHitMaxTries = errorHitMaxTries,
verbosity = verbosity,
initMutant = initMutant,
AND_DrvProbExit = AND_DrvProbExit,
fixation = fixation,
seed = seed,
interventions = interventions,
userVars = userVars,
rules = rules)
simul_grid <- data.frame(Genotype = simul_grid$GenotypesLabels,
N = simul_grid$pops.by.time[
nrow(simul_grid$pops.by.time), -1])
if (SpatialModel == "1D"){
simul_grid$Coordinates <- rep(list(0), length(
simul_grid$Genotype))
} else if (SpatialModel == "2D"){
simul_grid$Coordinates <- rep(list(c(0,0)), length(
simul_grid$Genotype))
} else if (SpatialModel == "3D"){
simul_grid$Coordinates <- rep(list(c(0,0,0)), length(
simul_grid$Genotype))
}
simul_grid <- AddWT_newgrid(simul_grid, initSize = 0)
intragrid_list <- list(simul_grid)
} else {
intragrid_list <- mclapply(finalgrid_list, function(x)
oncoSimulIndiv_grid(grid = x),
mc.cores = mc.cores)
}
# 2. After the individual simulation for each grid is completed, the
# migration operations will be performed.
intragrid_list <- Filter(Negate(is.null), intragrid_list)
if (iter < spatialIterMax & length(intragrid_list) > 0){
migration_out_list <- mclapply(
intragrid_list,
function(x)
SimulMigration(
x, migrationProb = migrationProb,
largeDistMigrationProb = largeDistMigrationProb,
maxMigrationPercentage = maxMigrationPercentage),
mc.cores = mc.cores)
migration_out_df <- suppressWarnings(bind_rows(migration_out_list))
if (SpatialModel == "1D"){
migration_out_df$Coordinates <- unlist(migration_out_df$Coordinates)
join_grid <- aggregate(N ~ Genotype + Coordinates,
data = migration_out_df, FUN = sum)
join_grid <- join_grid[,c("Genotype", "N", "Coordinates")]
grid_list <- split(join_grid, f = list(join_grid$Coordinates,
drop = TRUE))
conv_to_list <- function(x) {x$Coordinates <- rep(
list(x$Coordinates[1]),
length(x$Genotype))
return (x)}
grid_list <- mclapply(grid_list, function(x) conv_to_list(x),
mc.cores = mc.cores)
} else if (SpatialModel == "2D") {
coord_matrix <- do.call(rbind, migration_out_df$Coordinates)
colnames(coord_matrix) <- c("Coordinate_x", "Coordinate_y")
migration_out_df <- cbind(migration_out_df, coord_matrix)
join_grid <- aggregate(N ~ Genotype + Coordinate_x + Coordinate_y,
data = migration_out_df, FUN = sum)
Coordinates <- matrix(c(join_grid$Coordinate_x,
join_grid$Coordinate_y),
ncol = 2)
join_grid$Coordinates <- split(Coordinates, row(Coordinates))
grid_list <- split(join_grid, f = list(join_grid$Coordinate_x,
join_grid$Coordinate_y),
drop = TRUE)
grid_list <- mclapply(grid_list, function(x) x[,-c(2,3)],
mc.cores = mc.cores)
} else if (SpatialModel == "3D") {
coord_matrix <- do.call(rbind, migration_out_df$Coordinates)
colnames(coord_matrix) <- c("Coordinate_x", "Coordinate_y",
"Coordinate_z")
migration_out_df <- cbind(migration_out_df, coord_matrix)
join_grid <- aggregate(N ~ Genotype + Coordinate_x + Coordinate_y
+ Coordinate_z, data = migration_out_df,
FUN = sum)
Coordinates <- matrix(c(join_grid$Coordinate_x,
join_grid$Coordinate_y,
join_grid$Coordinate_z), ncol = 3)
join_grid$Coordinates <- split(Coordinates, row(Coordinates))
grid_list <- split(join_grid, f = list(join_grid$Coordinate_x,
join_grid$Coordinate_y,
join_grid$Coordinate_z),
drop = TRUE)
grid_list <- mclapply(grid_list, function(x) x[,-c(2:4)],
mc.cores = mc.cores)
}
finalgrid_list <- mclapply(grid_list, function(x) AddWT_newgrid(
x, initSize = initSize),mc.cores = mc.cores)
} else if (length(intragrid_list) > 0) {
finalgrid_list <<- intragrid_list
}
names(finalgrid_list) <- paste0("Grid", seq(1, length(finalgrid_list)))
if (iter <= spatialIterMax - 1){
cat(paste0("Iteration ", iter, " (", length(finalgrid_list), " demes)\n"))
}
iter <- iter + 1
}
FinalObject <- to_SpatialOncoSimul(finalgrid_list, spatialIterMax)
if (SpatialPlot == TRUE) {
Simulationplot(FinalObject, dim = SpatialModel)}
return (FinalObject)
}
#png("50_iterations.png", width = 500, height = 480)
z <- SpatialOncoSimul(fp = fe,
onlyCancer = FALSE,
finalTime = 500,
mu = 1e-4,
initSize = 1000,
keepPhylog = FALSE,
seed = NULL,
errorHitMaxTries = FALSE,
errorHitWallTime = FALSE, initMutant = c("i"),
spatialIterMax = 50, SpatialModel = "3D")
#dev.off()