diff --git a/vignettes/03-rnaseq_analysis.Rmd b/vignettes/03-rnaseq_analysis.Rmd
index 6fdbe763605e49a6e28b7b3be895f6558f80e167..bbbc423b13ba144619f7d291acfef5e36652193a 100644
--- a/vignettes/03-rnaseq_analysis.Rmd
+++ b/vignettes/03-rnaseq_analysis.Rmd
@@ -96,7 +96,7 @@ l_tmb <- fitModelParallel(
## Use mixed effect in your model
-**HTRfit** uses the **glmmTMB** functions for model fitting algorithms. This choice allows for the utilization of random effects within your formula design. For further details on how to specify your model, please refer to the [mixed model documentation](https://rdrr.io/cran/glmmTMB/man/glmmTMBControl.html).
+HTRfit uses the **glmmTMB** functions for model fitting algorithms. This choice allows for the utilization of random effects within your formula design. For further details on how to specify your model, please refer to the [mixed model documentation](https://rdrr.io/cran/glmmTMB/man/glmmTMBControl.html).
```{r example-fitModelParallel_mixed, warning = FALSE, message = FALSE}
l_tmb <- fitModelParallel(
diff --git a/vignettes/04-htrfit_vs_deseq2.Rmd b/vignettes/04-htrfit_vs_deseq2.Rmd
index df62dc17794b98cd882c46abbe783892d036e32c..f76456f96cd12bf080da96b73a35e51057310a8b 100644
--- a/vignettes/04-htrfit_vs_deseq2.Rmd
+++ b/vignettes/04-htrfit_vs_deseq2.Rmd
@@ -22,7 +22,7 @@ library(DESeq2)
```
-**HTRfit** offers a wrapper for **DESeq2** outputs. This functionality allows users to seamlessly integrate the results obtained from **DESeq2** into the **HTRfit** evaluation pipeline. By doing so, you can readily compare the performance of **HTRfit** with **DESeq2** on your RNAseq data. This comparative analysis aids in determining which tool performs better for your specific research goals and dataset
+HTRfit offers a wrapper for DESeq2 outputs. This functionality allows users to seamlessly integrate the results obtained from DESeq2 into the HTRfit evaluation pipeline. By doing so, you can readily compare the performance of HTRfit with DESeq2 on your RNAseq data. This comparative analysis aids in determining which tool performs better for your specific research goals and dataset
## Simulation
diff --git a/vignettes/05-list_var_object.Rmd b/vignettes/05-list_var_object.Rmd
index 6c6734c418daa9e870c1267042d5144ab28dc8e2..17b14d48719891d1bb00c3ca71fa5751ec55c098 100644
--- a/vignettes/05-list_var_object.Rmd
+++ b/vignettes/05-list_var_object.Rmd
@@ -21,7 +21,7 @@ library(HTRfit)
The `init_variable()` function is used for defining the variables in your experimental design. Sizes of effects for each variable and interaction term can be defined in two different ways: 1) The user can manually sets values of all levels of a variable, in which case the effects are necessarily considered fixed in the model; 2) The effects can be randomly picked in a normal distribution with mean and standard deviation defined by the user, in which case the user can decide whether the effects are considered fixed or random in the model.
-It is crucial to highlight that the choice of these parameters plays a critical role in the simulation process. For optimal results, we recommend basing these decisions on real data, as outlined in the [Tutorial](articles/tutorial.html)
+It is crucial to highlight that the choice of these parameters plays a critical role in the simulation process. For optimal results, we recommend basing these decisions on real data, as outlined in the [Simulation tutorial](articles/02-tutorial.html)
## Manually init a single variable
@@ -100,7 +100,7 @@ mock_data <- mock_rnaseq(list_var,
max_replicates = 4)
```
-Observe the 0.99 correlation in the `mock_obj`:
+Observe the -0.99 correlation in the `mock_obj`:
```{r simu_corr_var}
plot(mock_data$groundTruth$effects$varA,
@@ -111,7 +111,7 @@ plot(mock_data$groundTruth$effects$varA,
## Structure of list_var object
-The list_var object collected all the information needed to generate a [`mock_rnaseq` object](mock_rnaseq_object.html).
+The list_var object collected all the information needed to generate a [`mock_rnaseq` object](06-mock_rnaseq_object.html).
```{r example-str_obj_init, warning = FALSE, message = FALSE}
str(list_var)
diff --git a/vignettes/06-mock_rnaseq_object.Rmd b/vignettes/06-mock_rnaseq_object.Rmd
index 2a288a4a7a0b4fc69b12c419ccea4464d27d607f..18dc58bd781529ed150b7345bb238edab15d3df6 100644
--- a/vignettes/06-mock_rnaseq_object.Rmd
+++ b/vignettes/06-mock_rnaseq_object.Rmd
@@ -1,8 +1,8 @@
---
-title: "Mock rnaseq object"
+title: "Object - Mock rnaseq"
output: rmarkdown::html_vignette
vignette: >
- %\VignetteIndexEntry{Mock rnaseq object}
+ %\VignetteIndexEntry{Object - Mock rnaseq }
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
@@ -20,7 +20,7 @@ knitr::opts_chunk$set(
library(HTRfit)
```
-In this section, you will explore how to generate RNAseq data based on the [`list var`](list_var_object.html) object. The `mock_rnaseq()` function enables you to manage parameters in your RNAseq design, including number of genes, minimum and maximum number of replicates within your experimental setup, sequencing depth, basal expression of each gene, and dispersion of gene expression used for simulating counts.
+In this section, you will explore how to generate RNAseq data based on the [`list var`](05-list_var_object.html) object. The `mock_rnaseq()` function enables you to manage parameters in your RNAseq design, including number of genes, minimum and maximum number of replicates within your experimental setup, sequencing depth, basal expression of each gene, and dispersion of gene expression used for simulating counts.
## Minimal example