netie: Neoantigen-T cell interaction estimation
In netie: Antigen T Cell Interaction Estimation
Description
Usage
Arguments
Value
Author(s)
References
Examples
Description
The Bayesian Hierarchical Model named Neoantigen-T cell interaction estimation (Netie) is to estimate the history of the immune pressure on the evolution of the tumor clones.
Usage
1
2
Arguments
input_one_patient
a list with each data frame as the data for each patient. Each data frame consists 7 columns and each row is for one mutation. Please refer to https://github.com/tianshilu/Netie for more details.
sigma_square
hyperparameters for prior distributions. Please refer to https://github.com/tianshilu/Netie for more details.
alpha
hyperparameters for prior distributions. Please refer to https://github.com/tianshilu/Netie for more details.
beta
hyperparameters for prior distributions. Please refer to https://github.com/tianshilu/Netie for more details.
sigma_p_sqr
hyperparameters for prior distributions. Please refer to https://github.com/tianshilu/Netie for more details.
sigma_a_sqr
hyperparameters for prior distributions. Please refer to https://github.com/tianshilu/Netie for more details.
max_iter
the iterations of Markov chain Monte Carlo.
multi_sample
use True if one patient has more than one sample.
Value
The output is a list with the information of the anti-tumor selection pressure for each clone ac and for the whole tumor a.
Author(s)
Tianshi Lu
References
https://github.com/tianshilu/Netie
Examples
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##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
data(input_data)
netie(input_data,sigma_square=100000,alpha=10,beta=2,
sigma_p_sqr=0.1,max_iter=1000,multi_sample=TRUE)
## The function is currently defined as
function (input_one_patient, sigma_square, alpha, beta, sigma_p_sqr,
sigma_a_sqr, max_iter, multi_sample = FALSE)
{
if (all(input_one_patient$neo_load[!is.na(input_one_patient$cluster_id)] ==
0)) {
return(NA)
}
input_one_patient = input_one_patient[!is.na(input_one_patient$cluster_id),
]
if (multi_sample == T) {
mutations = unlist(sapply(input_one_patient$mutation_id,
function(x) paste(strsplit(x, " ")[[1]][2], strsplit(x,
" ")[[1]][3])))
input_one_patient$neo_load = unlist(sapply(mutations,
function(x) max(input_one_patient[mutations == x,
"neo_load"])))
phi = "1"
clones = list()
clones[[id = "1"]] = mutations[paste(input_one_patient$sample_id,
input_one_patient$cluster_id) == paste(input_one_patient$sample_id,
input_one_patient$cluster_id)[1]]
for (each_clone in unique(paste(input_one_patient$sample_id,
input_one_patient$cluster_id))[-1]) {
mutations_one_clone = mutations[paste(input_one_patient$sample_id,
input_one_patient$cluster_id) == each_clone]
phi_tmp = unlist(sapply(1:length(clones), function(x) {
uniq_clone = clones[[x]]
shared_mutations = intersect(uniq_clone, mutations_one_clone)
if (length(shared_mutations)/length(uniq_clone) >
0.5 & length(shared_mutations)/length(mutations_one_clone) >
0.5) {
return(names(clones)[x])
}
}), use.names = FALSE)
if (!is.null(phi_tmp)) {
phi = c(phi, phi_tmp)
}
else {
phi_tmp = max(as.numeric(names(clones))) + 1
phi = c(phi, phi_tmp)
clones[[id = as.character(phi_tmp)]] = mutations_one_clone
}
}
names(phi) = unique(paste(input_one_patient$sample_id,
input_one_patient$cluster_id))
}
if (length(unique(input_one_patient$cluster_id)) > 1) {
if (is.null(sigma_a_sqr)) {
non_zero_neo_avg = sapply(unique(input_one_patient$cluster_id),
function(x) mean(input_one_patient[input_one_patient$cluster_id ==
x & input_one_patient$neo_load != 0, "neo_load"]))
non_zero_neo_avg[is.nan(non_zero_neo_avg)] = 0
sigma_a_sqr = sd(log(non_zero_neo_avg + 1))^2 * 10
if (sigma_a_sqr == 0) {
sigma_a_sqr = 1
}
}
}
else {
sigma_a_sqr = 1
}
if (sigma_square < 100 * sigma_a_sqr) {
print("sigma square should be much more larger than sigma a square!")
stop()
}
if (alpha <= beta) {
print("alpha should be larger than beta!")
stop()
}
if (multi_sample == T) {
max_vaf = unlist(sapply(mutations, function(x) max(input_one_patient[mutations ==
x, "variant_allele_frequency"])))
input_one_patient = input_one_patient[max_vaf > 0.05,
]
}
else {
input_one_patient = input_one_patient[input_one_patient$variant_allele_frequency >
0.05, ]
}
tmp = table(input_one_patient$cluster_id[input_one_patient$neo_load >
0])
tmp = names(tmp[tmp >= 1])
input_one_patient = input_one_patient[input_one_patient$cluster_id %in%
tmp, ]
tmp = table(input_one_patient$cluster_id)
tmp = names(tmp[tmp >= 2])
input_one_patient = input_one_patient[input_one_patient$cluster_id %in%
tmp, ]
if (dim(input_one_patient)[1] == 0) {
return(NA)
}
if (multi_sample == T) {
input_one_patient$phi = as.numeric(phi[paste(input_one_patient$sample_id,
input_one_patient$cluster_id)])
input_one_patient$cluster_id = as.numeric(factor(paste(input_one_patient$sample_id,
input_one_patient$cluster_id)))
phi_cluster = input_one_patient[, c("cluster_id", "phi")]
phi_cluster = phi_cluster[!duplicated(phi_cluster$cluster_id),
]
rownames(phi_cluster) = as.character(phi_cluster$cluster_id)
phi_cluster = phi_cluster[as.character(unique(input_one_patient$cluster_id)),
]
}
else {
input_one_patient$cluster_id = as.numeric(factor(input_one_patient$cluster_id))
}
input_one_patient[input_one_patient$neo_load > 150, "neo_load"] = 150
ac = bc = rep(0, length(unique(input_one_patient$cluster_id)))
pi = 0.5
a = 0
zck_list = list()
ac_list = list()
bc_list = list()
acp_rate_ac_list = list()
acp_rate_bc_list = list()
a_all = c()
pi_all = c()
for (iter in 1:max_iter) {
if (iter/1000 == round(iter/1000)) {
cat(paste("Iteration", iter, "\n"))
print(ac)
print(ac)
print(bc)
print(acp_rate_ac)
print(acp_rate_bc)
}
acp_rate_ac = rep(FALSE, length(unique(input_one_patient$cluster_id)))
acp_rate_bc = rep(FALSE, length(unique(input_one_patient$cluster_id)))
zck_df = input_one_patient[, c("mutation_id", "cluster_id")]
zck_df$zck = 1
if (multi_sample == T) {
for (p in 1:length(unique(input_one_patient$phi))) {
input_each_phi = input_one_patient[input_one_patient$phi ==
unique(input_one_patient$phi)[p], ]
for (c in unique(input_each_phi$cluster_id)) {
input_each_clone = input_each_phi[input_each_phi$cluster_id ==
c, ]
vck = input_each_clone$variant_allele_frequency
lambda = exp(ac[c] * vck + bc[c])
nck = input_each_clone$neo_load
r_tmp = pi * (nck == 0)/(pi * (nck == 0) +
(1 - pi) * dpois(nck, lambda, log = F))
r_tmp_deno = pi * (nck == 0) + (1 - pi) * dpois(nck,
lambda, log = F)
r_tmp[r_tmp_deno == 0] = 0
zck = 1 * (runif(length(nck), 0, 1) > r_tmp)
names(zck) = input_each_clone$mutation_id
zck_df$zck[zck_df$mutation_id %in% names(zck)] = zck
bc_prim = rnorm(1, bc[c], sqrt(sigma_p_sqr))
lambda_prim_b = exp(ac[c] * vck + bc_prim)
lambda = exp(ac[c] * vck + bc[c])
tmp_prim = sum((zck == 1) * dpois(nck, lambda_prim_b,
log = T))
tmp = sum((zck == 1) * dpois(nck, lambda, log = T))
llhr_b = exp(tmp_prim - bc_prim^2/(2 * sigma_square) -
tmp + bc[c]^2/(2 * sigma_square))
acceptance_function_b = min(1, llhr_b)
u = runif(1, 0, 1)
if (u <= acceptance_function_b) {
bc[c] = bc_prim
acp_rate_bc[c] = TRUE
}
}
input_each_phi$bc = bc[input_each_phi$cluster_id]
input_each_phi$ac = ac[c]
vck_phi = input_each_phi$variant_allele_frequency
lambda_phi = exp(input_each_phi$ac * vck_phi +
input_each_phi$bc)
nck_phi = input_each_phi$neo_load
zck_phi = zck_df[input_each_phi$mutation_id,
"zck"]
ac_prim = rnorm(1, ac[c], sqrt(sigma_p_sqr))
lambda_prim_a = exp(ac_prim * vck_phi + input_each_phi$bc)
tmp_prim = sum((zck_phi == 1) * dpois(nck_phi,
lambda_prim_a, log = T))
tmp = sum((zck_phi == 1) * dpois(nck_phi, lambda_phi,
log = T))
if (length(table(input_one_patient$cluster_id)) ==
1) {
llhr_a = exp(tmp_prim - ac_prim^2/(2 * sigma_square) -
tmp + ac[c]^2/(2 * sigma_square))
}
else {
llhr_a = exp(tmp_prim - (ac_prim - a)^2/(2 *
sigma_a_sqr) - tmp + (ac[c] - a)^2/(2 * sigma_a_sqr))
}
acceptance_function_a = min(1, llhr_a)
u = runif(1, 0, 1)
if (u <= acceptance_function_a) {
ac[phi_cluster$phi == unique(input_each_clone$phi)] = ac_prim
acp_rate_ac[c] = TRUE
}
}
pi = rbeta(1, alpha + sum((zck_df$zck == 0) * (input_one_patient$neo_load ==
0)), beta + sum(zck_df$zck == 1))
A = 1/sigma_square + length(unique(input_one_patient$phi))/sigma_a_sqr
B = sum(ac[!duplicated(phi_cluster$phi)])/sigma_a_sqr
a = rnorm(1, B/A, sqrt(1/A))
ac_list[[iter]] = ac
bc_list[[iter]] = bc
zck_list[[iter]] = zck_df$zck
acp_rate_ac_list[[iter]] = acp_rate_ac
acp_rate_bc_list[[iter]] = acp_rate_bc
a_all = c(a_all, a)
pi_all = c(pi_all, pi)
}
else {
for (c in 1:length(unique(input_one_patient$cluster_id))) {
input_each_clone = input_one_patient[input_one_patient$cluster_id ==
unique(input_one_patient$cluster_id)[c], ]
vck = input_each_clone$variant_allele_frequency
lambda = exp(ac[c] * vck + bc[c])
nck = input_each_clone$neo_load
r_tmp = pi * (nck == 0)/(pi * (nck == 0) + (1 -
pi) * dpois(nck, lambda, log = F))
r_tmp_deno = pi * (nck == 0) + (1 - pi) * dpois(nck,
lambda, log = F)
r_tmp[r_tmp_deno == 0] = 0
zck = 1 * (runif(length(nck), 0, 1) > r_tmp)
names(zck) = input_each_clone$mutation_id
zck_df$zck[zck_df$mutation_id %in% names(zck)] = zck
ac_prim = rnorm(1, ac[c], sqrt(sigma_p_sqr))
lambda_prim_a = exp(ac_prim * vck + bc[c])
tmp_prim = sum((zck == 1) * dpois(nck, lambda_prim_a,
log = T))
tmp = sum((zck == 1) * dpois(nck, lambda, log = T))
if (length(table(input_one_patient$cluster_id)) ==
1) {
llhr_a = exp(tmp_prim - ac_prim^2/(2 * sigma_square) -
tmp + ac[c]^2/(2 * sigma_square))
}
else {
llhr_a = exp(tmp_prim - (ac_prim - a)^2/(2 *
sigma_a_sqr) - tmp + (ac[c] - a)^2/(2 * sigma_a_sqr))
}
acceptance_function_a = min(1, llhr_a)
u = runif(1, 0, 1)
if (u <= acceptance_function_a) {
ac[c] = ac_prim
acp_rate_ac[c] = TRUE
}
bc_prim = rnorm(1, bc[c], sqrt(sigma_p_sqr))
lambda_prim_b = exp(ac[c] * vck + bc_prim)
lambda = exp(ac[c] * vck + bc[c])
tmp_prim = sum((zck == 1) * dpois(nck, lambda_prim_b,
log = T))
tmp = sum((zck == 1) * dpois(nck, lambda, log = T))
llhr_b = exp(tmp_prim - bc_prim^2/(2 * sigma_square) -
tmp + bc[c]^2/(2 * sigma_square))
acceptance_function_b = min(1, llhr_b)
u = runif(1, 0, 1)
if (u <= acceptance_function_b) {
bc[c] = bc_prim
acp_rate_bc[c] = TRUE
}
}
pi = rbeta(1, alpha + sum((zck_df$zck == 0) * (input_one_patient$neo_load ==
0)), beta + sum(zck_df$zck == 1))
A = 1/sigma_square + length(unique(input_one_patient$cluster_id))/sigma_a_sqr
B = sum(ac)/sigma_a_sqr
a = rnorm(1, B/A, sqrt(1/A))
ac_list[[iter]] = ac
bc_list[[iter]] = bc
zck_list[[iter]] = zck_df$zck
acp_rate_ac_list[[iter]] = acp_rate_ac
acp_rate_bc_list[[iter]] = acp_rate_bc
a_all = c(a_all, a)
pi_all = c(pi_all, pi)
}
}
keep = round(max_iter/2):max_iter
ac_final = Reduce("+", ac_list[keep])/length(keep)
bc_final = Reduce("+", bc_list[keep])/length(keep)
zck_df_final = round(Reduce("+", zck_list[keep])/length(keep))
names(zck_df_final) = zck_df$mutation_id
ac_rate = Reduce("+", acp_rate_ac_list[keep])/length(keep)
bc_rate = Reduce("+", acp_rate_bc_list[keep])/length(keep)
a_final = mean(a_all[keep])
pi_final = mean(pi_all[keep])
if (multi_sample == TRUE) {
final_parameters = list(zck = data.frame(zck_df_final),
ac = ac_final, bc = bc_final, acp_rate_ac = ac_rate,
a = a_final, acp_rate_bc = bc_rate, pi = pi_final,
phi_cluster = phi_cluster)
}
else {
final_parameters = list(zck = data.frame(zck_df_final),
ac = ac_final, bc = bc_final, acp_rate_ac = ac_rate,
a = a_final, acp_rate_bc = bc_rate, pi = pi_final)
}
all_parameters = list(zck = zck_list, ac = ac_list, bc = bc_list,
a = a_all, pi = pi_all)
result = list(all_parameters = all_parameters, final_parameters = final_parameters)
return(result)
}
netie documentation built on Sept. 29, 2021, 9:08 a.m.
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Description Usage Arguments Value Author(s) References Examples
Description
The Bayesian Hierarchical Model named Neoantigen-T cell interaction estimation (Netie) is to estimate the history of the immune pressure on the evolution of the tumor clones.
Usage
1 2 |
Arguments
input_one_patient |
a list with each data frame as the data for each patient. Each data frame consists 7 columns and each row is for one mutation. Please refer to https://github.com/tianshilu/Netie for more details. |
sigma_square |
hyperparameters for prior distributions. Please refer to https://github.com/tianshilu/Netie for more details. |
alpha |
hyperparameters for prior distributions. Please refer to https://github.com/tianshilu/Netie for more details. |
beta |
hyperparameters for prior distributions. Please refer to https://github.com/tianshilu/Netie for more details. |
sigma_p_sqr |
hyperparameters for prior distributions. Please refer to https://github.com/tianshilu/Netie for more details. |
sigma_a_sqr |
hyperparameters for prior distributions. Please refer to https://github.com/tianshilu/Netie for more details. |
max_iter |
the iterations of Markov chain Monte Carlo. |
multi_sample |
use True if one patient has more than one sample. |
Value
The output is a list with the information of the anti-tumor selection pressure for each clone ac and for the whole tumor a.
Author(s)
Tianshi Lu
References
https://github.com/tianshilu/Netie
Examples
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 | ##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
data(input_data)
netie(input_data,sigma_square=100000,alpha=10,beta=2,
sigma_p_sqr=0.1,max_iter=1000,multi_sample=TRUE)
## The function is currently defined as
function (input_one_patient, sigma_square, alpha, beta, sigma_p_sqr,
sigma_a_sqr, max_iter, multi_sample = FALSE)
{
if (all(input_one_patient$neo_load[!is.na(input_one_patient$cluster_id)] ==
0)) {
return(NA)
}
input_one_patient = input_one_patient[!is.na(input_one_patient$cluster_id),
]
if (multi_sample == T) {
mutations = unlist(sapply(input_one_patient$mutation_id,
function(x) paste(strsplit(x, " ")[[1]][2], strsplit(x,
" ")[[1]][3])))
input_one_patient$neo_load = unlist(sapply(mutations,
function(x) max(input_one_patient[mutations == x,
"neo_load"])))
phi = "1"
clones = list()
clones[[id = "1"]] = mutations[paste(input_one_patient$sample_id,
input_one_patient$cluster_id) == paste(input_one_patient$sample_id,
input_one_patient$cluster_id)[1]]
for (each_clone in unique(paste(input_one_patient$sample_id,
input_one_patient$cluster_id))[-1]) {
mutations_one_clone = mutations[paste(input_one_patient$sample_id,
input_one_patient$cluster_id) == each_clone]
phi_tmp = unlist(sapply(1:length(clones), function(x) {
uniq_clone = clones[[x]]
shared_mutations = intersect(uniq_clone, mutations_one_clone)
if (length(shared_mutations)/length(uniq_clone) >
0.5 & length(shared_mutations)/length(mutations_one_clone) >
0.5) {
return(names(clones)[x])
}
}), use.names = FALSE)
if (!is.null(phi_tmp)) {
phi = c(phi, phi_tmp)
}
else {
phi_tmp = max(as.numeric(names(clones))) + 1
phi = c(phi, phi_tmp)
clones[[id = as.character(phi_tmp)]] = mutations_one_clone
}
}
names(phi) = unique(paste(input_one_patient$sample_id,
input_one_patient$cluster_id))
}
if (length(unique(input_one_patient$cluster_id)) > 1) {
if (is.null(sigma_a_sqr)) {
non_zero_neo_avg = sapply(unique(input_one_patient$cluster_id),
function(x) mean(input_one_patient[input_one_patient$cluster_id ==
x & input_one_patient$neo_load != 0, "neo_load"]))
non_zero_neo_avg[is.nan(non_zero_neo_avg)] = 0
sigma_a_sqr = sd(log(non_zero_neo_avg + 1))^2 * 10
if (sigma_a_sqr == 0) {
sigma_a_sqr = 1
}
}
}
else {
sigma_a_sqr = 1
}
if (sigma_square < 100 * sigma_a_sqr) {
print("sigma square should be much more larger than sigma a square!")
stop()
}
if (alpha <= beta) {
print("alpha should be larger than beta!")
stop()
}
if (multi_sample == T) {
max_vaf = unlist(sapply(mutations, function(x) max(input_one_patient[mutations ==
x, "variant_allele_frequency"])))
input_one_patient = input_one_patient[max_vaf > 0.05,
]
}
else {
input_one_patient = input_one_patient[input_one_patient$variant_allele_frequency >
0.05, ]
}
tmp = table(input_one_patient$cluster_id[input_one_patient$neo_load >
0])
tmp = names(tmp[tmp >= 1])
input_one_patient = input_one_patient[input_one_patient$cluster_id %in%
tmp, ]
tmp = table(input_one_patient$cluster_id)
tmp = names(tmp[tmp >= 2])
input_one_patient = input_one_patient[input_one_patient$cluster_id %in%
tmp, ]
if (dim(input_one_patient)[1] == 0) {
return(NA)
}
if (multi_sample == T) {
input_one_patient$phi = as.numeric(phi[paste(input_one_patient$sample_id,
input_one_patient$cluster_id)])
input_one_patient$cluster_id = as.numeric(factor(paste(input_one_patient$sample_id,
input_one_patient$cluster_id)))
phi_cluster = input_one_patient[, c("cluster_id", "phi")]
phi_cluster = phi_cluster[!duplicated(phi_cluster$cluster_id),
]
rownames(phi_cluster) = as.character(phi_cluster$cluster_id)
phi_cluster = phi_cluster[as.character(unique(input_one_patient$cluster_id)),
]
}
else {
input_one_patient$cluster_id = as.numeric(factor(input_one_patient$cluster_id))
}
input_one_patient[input_one_patient$neo_load > 150, "neo_load"] = 150
ac = bc = rep(0, length(unique(input_one_patient$cluster_id)))
pi = 0.5
a = 0
zck_list = list()
ac_list = list()
bc_list = list()
acp_rate_ac_list = list()
acp_rate_bc_list = list()
a_all = c()
pi_all = c()
for (iter in 1:max_iter) {
if (iter/1000 == round(iter/1000)) {
cat(paste("Iteration", iter, "\n"))
print(ac)
print(ac)
print(bc)
print(acp_rate_ac)
print(acp_rate_bc)
}
acp_rate_ac = rep(FALSE, length(unique(input_one_patient$cluster_id)))
acp_rate_bc = rep(FALSE, length(unique(input_one_patient$cluster_id)))
zck_df = input_one_patient[, c("mutation_id", "cluster_id")]
zck_df$zck = 1
if (multi_sample == T) {
for (p in 1:length(unique(input_one_patient$phi))) {
input_each_phi = input_one_patient[input_one_patient$phi ==
unique(input_one_patient$phi)[p], ]
for (c in unique(input_each_phi$cluster_id)) {
input_each_clone = input_each_phi[input_each_phi$cluster_id ==
c, ]
vck = input_each_clone$variant_allele_frequency
lambda = exp(ac[c] * vck + bc[c])
nck = input_each_clone$neo_load
r_tmp = pi * (nck == 0)/(pi * (nck == 0) +
(1 - pi) * dpois(nck, lambda, log = F))
r_tmp_deno = pi * (nck == 0) + (1 - pi) * dpois(nck,
lambda, log = F)
r_tmp[r_tmp_deno == 0] = 0
zck = 1 * (runif(length(nck), 0, 1) > r_tmp)
names(zck) = input_each_clone$mutation_id
zck_df$zck[zck_df$mutation_id %in% names(zck)] = zck
bc_prim = rnorm(1, bc[c], sqrt(sigma_p_sqr))
lambda_prim_b = exp(ac[c] * vck + bc_prim)
lambda = exp(ac[c] * vck + bc[c])
tmp_prim = sum((zck == 1) * dpois(nck, lambda_prim_b,
log = T))
tmp = sum((zck == 1) * dpois(nck, lambda, log = T))
llhr_b = exp(tmp_prim - bc_prim^2/(2 * sigma_square) -
tmp + bc[c]^2/(2 * sigma_square))
acceptance_function_b = min(1, llhr_b)
u = runif(1, 0, 1)
if (u <= acceptance_function_b) {
bc[c] = bc_prim
acp_rate_bc[c] = TRUE
}
}
input_each_phi$bc = bc[input_each_phi$cluster_id]
input_each_phi$ac = ac[c]
vck_phi = input_each_phi$variant_allele_frequency
lambda_phi = exp(input_each_phi$ac * vck_phi +
input_each_phi$bc)
nck_phi = input_each_phi$neo_load
zck_phi = zck_df[input_each_phi$mutation_id,
"zck"]
ac_prim = rnorm(1, ac[c], sqrt(sigma_p_sqr))
lambda_prim_a = exp(ac_prim * vck_phi + input_each_phi$bc)
tmp_prim = sum((zck_phi == 1) * dpois(nck_phi,
lambda_prim_a, log = T))
tmp = sum((zck_phi == 1) * dpois(nck_phi, lambda_phi,
log = T))
if (length(table(input_one_patient$cluster_id)) ==
1) {
llhr_a = exp(tmp_prim - ac_prim^2/(2 * sigma_square) -
tmp + ac[c]^2/(2 * sigma_square))
}
else {
llhr_a = exp(tmp_prim - (ac_prim - a)^2/(2 *
sigma_a_sqr) - tmp + (ac[c] - a)^2/(2 * sigma_a_sqr))
}
acceptance_function_a = min(1, llhr_a)
u = runif(1, 0, 1)
if (u <= acceptance_function_a) {
ac[phi_cluster$phi == unique(input_each_clone$phi)] = ac_prim
acp_rate_ac[c] = TRUE
}
}
pi = rbeta(1, alpha + sum((zck_df$zck == 0) * (input_one_patient$neo_load ==
0)), beta + sum(zck_df$zck == 1))
A = 1/sigma_square + length(unique(input_one_patient$phi))/sigma_a_sqr
B = sum(ac[!duplicated(phi_cluster$phi)])/sigma_a_sqr
a = rnorm(1, B/A, sqrt(1/A))
ac_list[[iter]] = ac
bc_list[[iter]] = bc
zck_list[[iter]] = zck_df$zck
acp_rate_ac_list[[iter]] = acp_rate_ac
acp_rate_bc_list[[iter]] = acp_rate_bc
a_all = c(a_all, a)
pi_all = c(pi_all, pi)
}
else {
for (c in 1:length(unique(input_one_patient$cluster_id))) {
input_each_clone = input_one_patient[input_one_patient$cluster_id ==
unique(input_one_patient$cluster_id)[c], ]
vck = input_each_clone$variant_allele_frequency
lambda = exp(ac[c] * vck + bc[c])
nck = input_each_clone$neo_load
r_tmp = pi * (nck == 0)/(pi * (nck == 0) + (1 -
pi) * dpois(nck, lambda, log = F))
r_tmp_deno = pi * (nck == 0) + (1 - pi) * dpois(nck,
lambda, log = F)
r_tmp[r_tmp_deno == 0] = 0
zck = 1 * (runif(length(nck), 0, 1) > r_tmp)
names(zck) = input_each_clone$mutation_id
zck_df$zck[zck_df$mutation_id %in% names(zck)] = zck
ac_prim = rnorm(1, ac[c], sqrt(sigma_p_sqr))
lambda_prim_a = exp(ac_prim * vck + bc[c])
tmp_prim = sum((zck == 1) * dpois(nck, lambda_prim_a,
log = T))
tmp = sum((zck == 1) * dpois(nck, lambda, log = T))
if (length(table(input_one_patient$cluster_id)) ==
1) {
llhr_a = exp(tmp_prim - ac_prim^2/(2 * sigma_square) -
tmp + ac[c]^2/(2 * sigma_square))
}
else {
llhr_a = exp(tmp_prim - (ac_prim - a)^2/(2 *
sigma_a_sqr) - tmp + (ac[c] - a)^2/(2 * sigma_a_sqr))
}
acceptance_function_a = min(1, llhr_a)
u = runif(1, 0, 1)
if (u <= acceptance_function_a) {
ac[c] = ac_prim
acp_rate_ac[c] = TRUE
}
bc_prim = rnorm(1, bc[c], sqrt(sigma_p_sqr))
lambda_prim_b = exp(ac[c] * vck + bc_prim)
lambda = exp(ac[c] * vck + bc[c])
tmp_prim = sum((zck == 1) * dpois(nck, lambda_prim_b,
log = T))
tmp = sum((zck == 1) * dpois(nck, lambda, log = T))
llhr_b = exp(tmp_prim - bc_prim^2/(2 * sigma_square) -
tmp + bc[c]^2/(2 * sigma_square))
acceptance_function_b = min(1, llhr_b)
u = runif(1, 0, 1)
if (u <= acceptance_function_b) {
bc[c] = bc_prim
acp_rate_bc[c] = TRUE
}
}
pi = rbeta(1, alpha + sum((zck_df$zck == 0) * (input_one_patient$neo_load ==
0)), beta + sum(zck_df$zck == 1))
A = 1/sigma_square + length(unique(input_one_patient$cluster_id))/sigma_a_sqr
B = sum(ac)/sigma_a_sqr
a = rnorm(1, B/A, sqrt(1/A))
ac_list[[iter]] = ac
bc_list[[iter]] = bc
zck_list[[iter]] = zck_df$zck
acp_rate_ac_list[[iter]] = acp_rate_ac
acp_rate_bc_list[[iter]] = acp_rate_bc
a_all = c(a_all, a)
pi_all = c(pi_all, pi)
}
}
keep = round(max_iter/2):max_iter
ac_final = Reduce("+", ac_list[keep])/length(keep)
bc_final = Reduce("+", bc_list[keep])/length(keep)
zck_df_final = round(Reduce("+", zck_list[keep])/length(keep))
names(zck_df_final) = zck_df$mutation_id
ac_rate = Reduce("+", acp_rate_ac_list[keep])/length(keep)
bc_rate = Reduce("+", acp_rate_bc_list[keep])/length(keep)
a_final = mean(a_all[keep])
pi_final = mean(pi_all[keep])
if (multi_sample == TRUE) {
final_parameters = list(zck = data.frame(zck_df_final),
ac = ac_final, bc = bc_final, acp_rate_ac = ac_rate,
a = a_final, acp_rate_bc = bc_rate, pi = pi_final,
phi_cluster = phi_cluster)
}
else {
final_parameters = list(zck = data.frame(zck_df_final),
ac = ac_final, bc = bc_final, acp_rate_ac = ac_rate,
a = a_final, acp_rate_bc = bc_rate, pi = pi_final)
}
all_parameters = list(zck = zck_list, ac = ac_list, bc = bc_list,
a = a_all, pi = pi_all)
result = list(all_parameters = all_parameters, final_parameters = final_parameters)
return(result)
}
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