diff --git a/dev/flat_full.Rmd b/dev/flat_full.Rmd
index 93da9e57fd7e2eccfa87d3b09158497abd6516bb..b3f61718e98c11165a41c2c8abb66bdce0896f99 100644
--- a/dev/flat_full.Rmd
+++ b/dev/flat_full.Rmd
@@ -308,7 +308,7 @@ plot_proba <- function(x, proba, sex = "XY") {
                 proba_m = proba[, 3],
                 clust_proba = rgb(proba_f, proba_m, proba_a, maxColorValue = 1),
             ) %>% 
-            sample_n(100000) %>% 
+            sample_n(min(c(100000, nrow(x)))) %>% 
             ggplot(aes(x = count_m, y = count_f, color = clust_proba)) +
             geom_point() +
             scale_color_identity() +
@@ -321,7 +321,7 @@ plot_proba <- function(x, proba, sex = "XY") {
                 proba_f = proba[, 2],
                 clust_proba = rgb(proba_f, 0,  proba_a, maxColorValue = 1),
             ) %>% 
-            sample_n(100000) %>% 
+            sample_n(min(c(100000, nrow(x)))) %>% 
             ggplot(aes(x = count_m, y = count_f, color = clust_proba)) +
             geom_point() +
             scale_color_identity() +
@@ -525,35 +525,6 @@ data %>%
     plot_proba(model_XY$proba)
 ```
 
-```{r clustering_XY_flexmix}
-library(flexmix)
-m_xy <- flexmix(
-  log1p(count_m) ~ I(log1p(count_f) + I(log1p(count_f))),
-  k = 3,
-  data = data %>%
-    dplyr::select(count_m, count_f),
-  # model = FLXglm(family = "poisson")
-  )
-summary(m_xy)
-parameters(m_xy, component = 1)
-parameters(m_xy, component = 2)
-parameters(m_xy, component = 3)
-plot(m_xy)
-rm_xy <- refit(m_xy)
-summary(rm_xy)
-data %>% 
-  mutate(
-    proba1 = m_xy@posterior$scaled[, 1],
-    proba2 = m_xy@posterior$scaled[, 2],
-    proba3 = m_xy@posterior$scaled[, 3]
-  ) %>% 
-  pivot_longer(cols = c(proba1, proba2, proba3)) %>% 
-  ggplot(aes(x = log1p(count_m), y = log1p(count_f), color = value)) +
-  geom_point() +
-  facet_wrap(~name) +
-  theme_bw()
-```
-    
 # clustering XO
 
 ```{r clustering_XO}
@@ -568,29 +539,6 @@ data %>%
     plot_proba(model_XO$proba, sex = "X0")
 ```
 
-```{r clustering_X0_flexmix}
-m_xo <- flexmix(
-  count_m ~ I(count_f + I(count_f)),
-  k = 2,
-  data = data %>%
-    dplyr::select(count_m, count_f)
-  )
-summary(m_xo)
-parameters(m_xo, component = 1)
-parameters(m_xo, component = 2)
-plot(m_xo)
-rm_xo <- refit(m_xo)
-summary(rm_xo)
-data %>% 
-  mutate(
-    proba1 = m_xo@posterior$scaled[, 1],
-    proba2 = m_xo@posterior$scaled[, 2],
-  ) %>% 
-  ggplot(aes(x = count_m, y = count_f, color = proba2)) +
-  geom_point() +
-  theme_bw()
-```
-
 # LRT
 
 ## For XY