update data generation

inst/scripts/d_zibb_4.R

@@ -0,0 +1,174 @@
+set.seed(seed = 12345)
+require(rstan)
+
+# Stan generative model
+sim_stan <- "
+functions {
+  int zibb_rng(int y, int n, real mu, real phi, real kappa) {
+    if (bernoulli_rng(kappa) == 1) {
+      return (0);
+    } else {
+      return (beta_binomial_rng(n, mu * phi, (1 - mu) * phi));
+    }
+  }
+}
+
+data {
+  int<lower=0> N_sample;                   // number of repertoires
+  int<lower=0> N_gene;                     // gene
+  int<lower=0> N_individual;               // number of individuals
+  int<lower=0> N_condition;                // number of conditions
+  array [N_sample] int N;                  // repertoire size
+  array [N_individual] int condition_id;   // id of conditions
+  array [N_sample] int individual_id;      // id of replicate
+  vector [N_gene] alpha;
+  real <lower=0> phi;
+  real <lower=0, upper=1> kappa;
+  array [N_condition] vector [N_gene] beta_condition;
+  vector <lower=0> [N_condition] sigma_condition;
+  vector <lower=0> [N_condition] sigma_individual;
+  real <lower=0> sigma_replicate;
+}
+
+generated quantities {
+  array [N_sample] vector <lower=0, upper=1> [N_gene] theta;
+  array [N_sample] vector [N_gene] beta_sample;
+  array [N_individual] vector [N_gene] beta_individual;
+  
+  // generate usage
+  array [N_gene, N_sample] int Y;
+  
+  for(i in 1:N_individual) {
+    for(j in 1:N_gene) {
+      beta_individual[i][j] = normal_rng(beta_condition[condition_id[i]][j], sigma_individual[condition_id[i]]);
+    }
+  }
+  
+  for(i in 1:N_sample) {
+    for(j in 1:N_gene) {
+      beta_sample[i][j]  = normal_rng(beta_individual[individual_id[i]][j], sigma_replicate);
+      theta[i][j] = inv_logit(alpha[j] + beta_sample[i][j]);
+      Y[j, i] = zibb_rng(Y[j, i], N[i], theta[i][j], phi, kappa);
+    }
+  }
+}
+"
+
+# compile model
+m <- rstan::stan_model(model_code = sim_stan)
+
+
+
+# int<lower=0> N_sample;                   // number of repertoires
+# int<lower=0> N_gene;                     // gene
+# int<lower=0> N_individual;               // number of individuals
+# int<lower=0> N_condition;                // number of conditions
+# array [N_sample] int N;                  // repertoire size
+# array [N_individual] int condition_id;   // id of conditions
+# array [N_sample] int individual_id;      // id of replicate
+# vector [N_gene] alpha;
+# real <lower=0> phi;
+# real <lower=0, upper=1> kappa;
+# array [N_condition] vector [N_gene] beta_condition;
+# vector <lower=0> [N_condition] sigma_condition;
+# vector <lower=0> [N_condition] sigma_individual;
+# real <lower=0> sigma_replicate;
+
+# generate data based on the following parameters parameters
+set.seed(1005001)
+N_gene <- 15
+N_replicates <- 3
+N_individual <- 9
+N_condition <- 3
+N_sample <- N_individual * N_replicates
+
+condition_id <- rep(x = 1:N_condition, each = N_individual/N_condition) 
+
+N <- rep(x = 1000, times = N_sample)
+
+individual_id <- rep(x = 1:N_individual, each = N_replicates)
+
+alpha <- rnorm(n = N_gene, mean = -5, sd = 3)
+
+phi <- 200
+
+kappa <- 0.02
+
+beta_condition <- array(data = 0, dim = c(N_condition, N_gene))
+for(c in 1:N_condition) {
+  for(g in 1:N_gene) {
+    u <- runif(n = 1, min = 0, max = 1)
+    if(u <= 0.95) {
+      beta_condition[c,g] <- rnorm(n = 1, mean = 0, sd = 0.5)
+    } else {
+      beta_condition[c,g] <- rnorm(n = 1, mean = 0, sd = 5)
+    }
+  }
+}
+
+sigma_condition <- rep(x = 1, times = N_condition)
+sigma_individual <- rep(x = 0.5, times = N_condition)
+sigma_replicate <- 0.1
+
+
+l <- list(N_sample = N_sample, 
+          N_gene = N_gene, 
+          N_individual = N_individual,
+          N_condition = N_condition,
+          N = N,
+          condition_id = condition_id,
+          individual_id = individual_id,
+          alpha = alpha,
+          phi = phi,
+          kappa = kappa,
+          beta_condition = beta_condition,
+          sigma_condition = sigma_condition,
+          sigma_individual = sigma_individual,
+          sigma_replicate = sigma_replicate)
+
+# simulate
+sim <- rstan::sampling(object = m,
+                       data = l, 
+                       iter = 1, 
+                       chains = 1, 
+                       algorithm="Fixed_param")
+
+# extract simulation and convert into data frame which can 
+# be used as input of IgGeneUsage
+ysim <- rstan::extract(object = sim, par = "Y")$Y
+ysim <- ysim[1,,]
+
+ysim_df <- reshape2::melt(ysim)
+colnames(ysim_df) <- c("gene_name", "sample_id", "gene_usage_count")
+
+m <- data.frame(sample_id = 1:l$N_sample,
+           individual_id = l$individual_id)
+
+ysim_df <- merge(x = ysim_df, y = m, by = "sample_id", all.x = T)
+
+m <- data.frame(individual_id = 1:l$N_individual,
+                condition_id = l$condition_id)
+
+ysim_df <- merge(x = ysim_df, y = m, by = "individual_id", all.x = T)
+
+
+ysim_df$condition <- paste0("C_", ysim_df$condition_id)
+ysim_df$gene_name <- paste0("G_", ysim_df$gene_name)
+ysim_df$individual_id <- paste0("I_", ysim_df$individual_id)
+
+ysim_df$replicate <- rep(rep(x = c("R_1", "R_2", "R_3"), each = 15), times = 9)
+ysim_df$condition_id <- NULL
+ysim_df$sample_id <- NULL
+ysim_df <- ysim_df[, c("individual_id", "condition", "gene_name", 
+                       "replicate", "gene_usage_count")]
+
+d_zibb_4 <- ysim_df
+
+# save
+save(d_zibb_4, file = "data/d_zibb_4.RData", compress = T)
+
+
+# ggplot(data = d_zibb_4)+
+#   geom_sina(aes(x = gene_name, y = gene_usage_count, col = condition))+
+#   theme_bw(base_size = 10)+
+#   theme(legend.position = "none")

vignettes/User_Manual.Rmd

@@ -543,7 +543,7 @@ g2 <- ggplot(data = stats)+
 ```
 
 
-```{r, fig.weight = 5, fig.height = 6}
+```{r, fig.height = 6, fig.width = 7}
 (g1/g2)
 ```