Delete test.R

test.R

-## distribution reads per gene pour 1 mutant
-y <- rnbinom(n = 6000, mu = 50, size = 5)
-table(y)
-barplot(table(y))
-
-
-
-treatment <- sample(c(0,1), size = 2000, replace = TRUE)
-treatment
-c(0,1)
-
-
-#range_mu = runif(1000, min=50, max=55)
-#range_size = runif(1000, min=5, max=6)
-
-#sample(range_mu, size = 1000, replace = TRUE)
-
-N_gene = 6000
-N_mutant = 1 # for now
-N_environment = 2
-
-
-y_env1 = rnbinom(n = N_gene, mu = 50, size = 5)
-y_env2 = rnbinom(n = N_gene, mu = 2, size = 5) # modif distrib per environment
-y = c(y_env1, y_env2)
-mydta = array(data = y , dim = c(N_gene,N_mutant,N_environment))
-mydta[,,1]
-
-
-dimnames(mydta) <- list("N" = sprintf("gene%d", 1:N_gene),
-                                   "M" = sprintf("mutant%d", 1:N_mutant),
-                                   "K" = sprintf("environment%d", 1:N_environment))
-
-mydta[,,]
-
-lol <- list(structure(list(pair = c("BoneMarrow", "Pulmonary"), genes = "PRR11"),
-                      .Names = c("pair", "genes")),
-            structure(list(pair = c("BoneMarrow", "Umbilical"), genes = "GNB2L1"),
-                      .Names = c("pair", "genes")),
-            structure(list(pair = c("Pulmonary", "Umbilical"), genes = "ATP1B1"), .Names = c("pair","genes")))
-
-lol[[1]]$pair
-library(tidyverse)
-map_dfr(lol, ~as_tibble(.) %>% 
-          mutate(row=paste0("pair", row_number()))%>% 
-          spread(row, pair) %>% 
-          select(pair1, pair2, genes))
-
-
-
-
-#replicate(n=10,rnbinom(n = 1, mu = 50, size = 5))
-##test
-
-set.seed(1982)
-N <- 6000
-M <- 1000
-K <- 2
-example_3d_array <- array(rnbinom(n = 1, mu = 50, size = 5), c(M, K))
-example_3d_array[,,2][3,]
-
-library("dplyr")
-#system.time(expr = {
-  dimnames(example_3d_array) <- list("N" = sprintf("N%d", 1:N),
-                                     "M" = sprintf("M%d", 1:M),
-                                     "K" = sprintf("K%d", 1:K))
-  example_3d_cube <- as.tbl_cube(example_3d_array)
-  example_2d_df <- as_tibble(example_3d_cube)
-#}
-#)
-  
-  
-  
-  #params
-  N_gene = 6000
-  
-  
-  
-  ## function generator
-  rnbinom_generator <- function(n_gene, mu_simul, alpha){
-    
-    my_counts <- rnbinom(n = n_gene, mu = mu_simul, size = alpha)
-    return(my_counts)
-    
-  }
-  
-  
-  
-  ## build my lib counts
-  env1_A <- rnbinom_generator(N_gene, 50, 5)
-  env1_B <- rnbinom_generator(N_gene, 50, 5)
-  
-  env2_A <- rnbinom_generator(N_gene, 100, 5)
-  env2_B <- rnbinom_generator(N_gene, 100, 5)
-  
-  
-  
-  ## export my lib count
-  names(env1_A) <- sprintf("gene%d", 1:N_gene)
-  names(env1_B) <- sprintf("gene%d", 1:N_gene)
-  
-  names(env2_A) <- sprintf("gene%d", 1:N_gene)
-  names(env2_B) <- sprintf("gene%d", 1:N_gene)
-  
-  env1_A %>% data.frame(.) %>% fwrite(., "~/mydatalocal/counts_simulation/results/env1_A.tsv", row.names = T, col.names = F, sep = "\t")
-  env2_A %>% data.frame(.) %>% fwrite(., "~/mydatalocal/counts_simulation/results/env2_A.tsv", row.names = T, col.names = F, sep = "\t")
-  env1_B %>% data.frame(.) %>% fwrite(., "~/mydatalocal/counts_simulation/results/env1_B.tsv", row.names = T, col.names = F, sep = "\t")
-  env2_B %>% data.frame(.) %>% fwrite(., "~/mydatalocal/counts_simulation/results/env2_B.tsv", row.names = T, col.names = F, sep = "\t")
-  
-  
-