Merge branch 'binomial_noise' of https://github.com/caravagnalab/mobster

into binomial_noise
caravagnalab · Nov 7, 2023 · 2acb0e2 · 2acb0e2
2 parents 496b28d + 61bb3e5
commit 2acb0e2
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Showing 2 changed files with 11 additions and 11 deletions.
diff --git a/R/assign_mutation_posteriori.R b/R/assign_mutation_posteriori.R
@@ -17,8 +17,8 @@ assign_mutations_posteriori <- function(x) {
 
   to_assign <- x$data %>% filter(is.na(cluster), 
                                  karyotype %in% names(x$model_parameters),
-                                 VAF <= x$run_parameters$min_vaf,
-                                 NV <= x$run_parameters$NV_filter) 
+                                 VAF >= x$run_parameters$min_vaf,
+                                 NV >= x$run_parameters$NV_filter) 
 
   if(nrow(to_assign) == 0) {
     cli::cli_alert_info("No mutations to assign, returning input object!")

diff --git a/R/evodynamics.R b/R/evodynamics.R
@@ -613,8 +613,8 @@ get_genome_length = function(fit, exome = FALSE, build = "hg38", karyotypes = NU
 
   if(!is.null(karyotypes)) seg %>% filter(karyotype %in% karyotypes)
 
-  lengths = seg  %>% group_by(karyotype) %>% 
-    summarize(length = sum(to-from)) 
+  lengths = seg  %>% dplyr::group_by(karyotype) %>% 
+    dplyr::summarize(length = sum(to-from)) 
 
   return(lengths)
 
@@ -644,19 +644,19 @@ get_genome_length = function(fit, exome = FALSE, build = "hg38", karyotypes = NU
 s_posterior = function(fit,N_max,ncells = 1,u = 0,sigma = 1){
 
 
-  if(class(fit$best) == "dbpmmh"){
+  if(class(fit) == "dbpmmh"){
 
-    m = fit$best$data %>% filter(cluster == 'S1',karyotype == "1:1") %>% nrow()
-    mu = mu_posterior(fit$best,ncells = ncells) %>% pull(mean)
-    ccf = (fit$best$data %>% filter(cluster == 'S1',karyotype == "1:1") %>% pull(VAF) %>% mean())*2
-    length_diploid_genome = get_genome_length(fit$best) %>% filter(karyotype == "1:1") %>% 
+    m = fit$data %>% filter(cluster == 'S1',karyotype == "1:1") %>% nrow()
+    mu = mu_posterior(fit,ncells = ncells) %>% pull(mean)
+    ccf = (fit$data %>% filter(cluster == 'S1',karyotype == "1:1") %>% pull(VAF) %>% mean())*2
+    length_diploid_genome = get_genome_length(fit) %>% filter(karyotype == "1:1") %>% 
       pull(length)
 
   }else{
 
-    m = fit$best$data %>% filter(cluster == 'C2') %>% nrow()
+    m = fit$data %>% filter(cluster == 'C2') %>% nrow()
     mu = mutationrate(fit,ncells = ncells)/(2*3*10^9)
-    ccf = (fit$best$data %>% filter(cluster == 'C2') %>% pull(VAF) %>% mean())*2
+    ccf = (fit$data %>% filter(cluster == 'C2') %>% pull(VAF) %>% mean())*2
     length_diploid_genome = 3*10^9
 
 }