forked from agiraldeztrujillo/R_Project
-
Notifications
You must be signed in to change notification settings - Fork 0
/
SpatialSimul2.R
178 lines (147 loc) · 7.87 KB
/
SpatialSimul2.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
library(OncoSimulR)
library(parallel)
## 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)
# Lanzamos la simulación para el primer deme.
osi <- oncoSimulIndiv(fe,
model = "McFL",
onlyCancer = FALSE,
finalTime = 500,
mu = 1e-4,
initSize = 1000,
keepPhylog = FALSE,
seed = NULL,
errorHitMaxTries = FALSE,
errorHitWallTime = FALSE)
osi
grid1 <- data.frame(Genotype = osi$GenotypesLabels,
N = osi$pops.by.time[nrow(osi$pops.by.time), -1],
Coordinate_x = as.integer(rep(0, length(osi$GenotypesLabels))),
Coordinate_y = as.integer(rep(0, length(osi$GenotypesLabels))))
grid2 <- data.frame(Genotype = osi$GenotypesLabels,
N = osi$pops.by.time[nrow(osi$pops.by.time), -1],
Coordinate_x = as.integer(rep(0, length(osi$GenotypesLabels))),
Coordinate_y = as.integer(rep(0, length(osi$GenotypesLabels))))
## Posible función para simulación en primer deme y resto de demes.
oncoSimulIndiv_grid <- function(grid, iter,...){
if (iter == 1){
grid1 <- oncoSimulIndiv(...)
init_grid <- data.frame(Genotype = grid1$GenotypesLabels,
N = init_grid$pops.by.time[nrow(grid1$pops.by.time), -1],
Coordinate_x = as.integer(rep(0, length(grid1$GenotypesLabels))),
Coordinate_y = as.integer(rep(0, length(grid1$GenotypesLabels))))
class(init_grid) <- 'SpatialOncosimul'
} else{
Ngenotypes <- grid$N
Genotypes <- grid$Genotypes
next_grid <- oncoSimulIndiv(initSize = Ngenotypes,
initMutant = Genotypes)
next_grid <- data.frame(Genotype = next_grid$GenotypesLabels,
N = next_grid$pops.by.time[nrow(next_grid$pops.by.time), -1],
Coordinate_x = grid$Coordinate_x,
Coordinate_y = grid$Coordinate_y)
class(next_grid) <- 'SpatialOncosimul'
}}
# La función OncoSimulIndiv_grid habría que pasarla con mclapply a una lista que
# contenga los demes creados hasta el momento, para que la aplique sobre cada deme.
# Y nos devolvería una nueva lista con los nuevos demes (cada uno 1 dataframe)
# tras la simulación. Los argumentos de la función OncoSimulIndiv_grid son los
# mismos que para la función OncoSimulIndiv, añadiendo el grid y la iteración en
# la que nos encontramos dentro de la simulación espacial (podemos sumar 1 a iter
# cada vez que terminamos una fase interdeme).
# FUNCIÓN FASE INTERDEME (PARA 2D).
SimulMigration <- function(grid, migrationProb = 0.5,
largeDistMigrationProb = 1e-6,
maxMigrationPercentage = 0.2){
# Create a new data.frame with the genotypes and population number of mutant
# cells in the grid.
TotalPopSize <- sum(grid$N)
finalpopcomp_mut <- grid[which(grid$Genotype != "" &
grid$N > 0),]
# Modify the previous dataframe so instead of mutant population number in the
# grid, it contains the frequency of each mutant cell.
finalpopcomp_mut$N <- finalpopcomp_mut$N/sum(finalpopcomp_mut$N)
# Number and genotypes of cells that migrate to adjacent spaces (finalpop_near
# migration dataa.frame).
pop_nearmigration <- sample(seq(from = 1, to = TotalPopSize *
migrationProb *
maxMigrationPercentage), 1)
finalpop_nearmigration <- as.data.frame(table(sample(finalpopcomp_mut$Genotype,
pop_nearmigration,
replace = TRUE,
prob = finalpopcomp_mut$N)))
colnames(finalpop_nearmigration) <-c("Genotype", "N")
# Number of cells that migrate to remote spaces.
pop_remotemigration <- sample(seq(from = 1, to = TotalPopSize *
largeDistMigrationProb *
maxMigrationPercentage), 1)
finalpop_remotemigration <- as.data.frame(table(sample(finalpopcomp_mut$Genotype,
pop_remotemigration,
replace = TRUE,
prob = finalpopcomp_mut$N)))
colnames(finalpop_remotemigration) <-c("Genotype", "N")
init_coordinates <- c(grid$Coordinate_x[1], grid$Coordinate_y[1])
coord_nearmmigration <- init_coordinates
coord_remotemigration <- init_coordinates
# This if statement ensures that only positive coordinates were selected for
# cells that migrate to adjacent spaces.
if (0 %in% (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_nearmmigration, init_coordinates)){
coord_nearmmigration <- c(sample(seq(init_coordinates[1],
init_coordinates[1] + 1), 1),
sample(seq(init_coordinates[2],
init_coordinates[2] + 1), 1))
}
} else {
while (identical(coord_nearmmigration, init_coordinates)){
coord_nearmmigration <- c(sample(seq(init_coordinates[1] - 1,
init_coordinates[1] + 1), 1),
sample(seq(init_coordinates[2] - 1,
init_coordinates[2] + 1), 1))
}}
# This while statement ensures that only positive coordinates were selected for
# cells that migrate to remote spaces.
while (identical(coord_remotemigration, init_coordinates) ||
any(coord_remotemigration < 0)){
coord_remotemigration <- c(sample(c(init_coordinates[1] -
sample(seq(10, 35), 1),
init_coordinates[1] +
sample(seq(10, 35), 1)), 1),
sample(c(init_coordinates[2] -
sample(seq(10, 35), 1),
init_coordinates[2] +
sample(seq(10, 35), 1)), 1))
}
finalpop_nearmigration$Coordinate_x <- coord_nearmmigration[1]
finalpop_nearmigration$Coordinate_y <- coord_nearmmigration[2]
finalpop_remotemigration$Coordinate_x <- coord_remotemigration[1]
finalpop_remotemigration$Coordinate_y <- coord_remotemigration[2]
total_migration <- rbind(finalpop_nearmigration, finalpop_remotemigration)
for (gen in unique(total_migration$Genotype)) {
Nmigration_per_genotype <- sum(total_migration$N[which(
total_migration$Genotype == gen)])
grid$N[which(grid$Genotype == gen)] <- (grid$N[which(grid$Genotype == gen)]
- Nmigration_per_genotype)
}
return (list(grid, total_migration))
}
SimulMigration(grid1)
grid_list <- list(grid1, grid2)
y <- mclapply(grid_list, SimulMigration)
#z <- lapply(y, function (x) x[[1]])
#m <- lapply(y, function (x) x[[2]])
#c(z, m)
##1. De la lista que devuelva el mcapply (donde cada elemento es un dataframe
# de células que migran), unir todos los dataframes en un único dataframe.
## Para unir utilizar alguna función como merge para unir por coordenadas
# y genotipos en un solo deme.
## Buscar las coordenadas que no tengan genotipo WT o "".