update readme - add png for simuStep ressources

README.md

@@ -86,18 +86,35 @@ In the realm of RNAseq analysis, various key experimental parameters play a cruc
 
 ## Getting started
 
+
+### Init a design and simulate RNAseq data
+
 ```
 library('HTRfit')
 ## -- init a design 
-input_var_list <- init_variable( name = "varA", mu = 0, sd = 0.29, level = 60) %>%
+input_var_list <- init_variable( name = "varA", mu = 0, sd = 0.29, level = 2000) %>%
                   init_variable( name = "varB", mu = 0.27, sd = 0.6, level = 2) %>%
                     add_interaction( between_var = c("varA", "varB"), mu = 0.44, sd = 0.89)
 ## -- simulate RNAseq data 
 mock_data <- mock_rnaseq(input_var_list, 
-                         n_genes = 30,
-                         min_replicates  = 10,
-                         max_replicates = 10, 
-                         basal_expression = 5 )
+                         n_genes = 30000,
+                         min_replicates  = 4,
+                         max_replicates = 4 )
+```
+
+
+The simulation process in HTRfit has been optimized to generate RNAseq counts for 30,000 genes and 4,000 experimental conditions, each replicated 4 times, resulting in a total of 16,000 samples, in less than 5 minutes. However, the object generated by the framework under these conditions can consume a significant amount of RAM, approximately 50 GB. For an equivalent simulation with 6,000 genes, less than a minute and 10 GB of RAM are required.
+
+
+<div id="bg"  align="center">
+  <img src="./vignettes/figs/simulation_step.png" width="500" height="300">
+</div> 
+
+
+
+### Fit your model
+
+```
 ## -- prepare data & fit a model with mixed effect
 data2fit = prepareData2fit(countMatrix = mock_data$counts, 
                            metadata =  mock_data$metadata, 
@@ -106,8 +123,12 @@ l_tmb <- fitModelParallel(formula = kij ~ varB + (varB | varA),
                           data = data2fit, 
                           group_by = "geneID",
                           family = glmmTMB::nbinom2(link = "log"), 
-                          log_file = "log.txt",
                           n.cores = 1)
+```
+
+### Evalutation
+
+```
 ## -- evaluation
 resSimu <- simulationReport(mock_data, 
                             list_tmb = l_tmb,