Fix boxplot with no significance

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: sccomp
 Title: Robust Outlier-aware Estimation of Composition and Heterogeneity for Single-cell Data
-Version: 1.9.3
+Version: 1.9.5
 Authors@R: c(person("Stefano", "Mangiola", email = "mangiolastefano@gmail.com",
                   role = c("aut", "cre"))
                   )

R/utilities.R

@@ -544,6 +544,12 @@ fit_model = function(
   # Fit
   mod = load_model(model_name, threads = cores)
 
+  # Avoid 0 proportions
+  if(data_for_model$is_proportion && min(data_for_model$y_proportion)==0){
+    warning("sccomp says: your proportion values include 0. Assuming that 0s derive from a precision threshold (e.g. deconvolution), 0s are converted to the smaller non 0 proportion value.")
+    data_for_model$y_proportion[data_for_model$y_proportion==0] =
+      min(data_for_model$y_proportion[data_for_model$y_proportion>0])
+  }
 
   if(inference_method == "hmc"){
 
@@ -1318,11 +1324,6 @@ data_spread_to_model_input =
       y_proportion = y[0,,drop = FALSE]
     }
 
-    if(is_proportion && min(y_proportion)==0){
-      warning("sccomp says: your proportion values include 0. Assuming that 0s derive from a precision threshold (e.g. deconvolution), 0s are converted to the smaller non 0 proportion value.")
-      y_proportion[y_proportion==0] = min(y_proportion[y_proportion>0])
-     }
-
 
     data_for_model =
       list(
@@ -2118,7 +2119,7 @@ plot_boxplot = function(
       )
   }
 
-  if(nrow(significance_colors) == 0 |
+  if(nrow(significance_colors) == 0 ||
      length(intersect(
        significance_colors |> pull(!!as.symbol(factor_of_interest)),
        data_proportion |> pull(!!as.symbol(factor_of_interest))
@@ -2133,8 +2134,7 @@ plot_boxplot = function(
         outlier.shape = NA, outlier.color = NA,outlier.size = 0,
         data =
           data_proportion |>
-          mutate(!!as.symbol(factor_of_interest) := as.character(!!as.symbol(factor_of_interest))) %>%
-          left_join(significance_colors, by = c(quo_name(.cell_group), factor_of_interest)),
+          mutate(!!as.symbol(factor_of_interest) := as.character(!!as.symbol(factor_of_interest))) ,
         fatten = 0.5,
         lwd=0.5,
       )

tests/testthat/test-sccomp_.R

@@ -629,6 +629,27 @@ test_that("sccomp_proportional_fold_change",{
 
 })
 
+test_that("plotting for no significance",{
+
+  skip_cmdstan()
+
+
+  no_significance_df |>
+    mutate(count = count |> as.integer()) |> 
+    sccomp_estimate(formula_composition = ~ condition,
+                    .sample = sample,
+                    .cell_group = cell_group,.abundance = count,
+                    bimodal_mean_variability_association = TRUE,      
+                    cores = 1
+) |>
+    sccomp_test() |> 
+    sccomp_boxplot("condition") |> 
+    expect_no_error()
+
+
+
+
+})
 
 # fit = 
 # 	seurat_obj |>