diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -83,7 +83,7 @@ In the realm of RNAseq analysis, various key experimental parameters play a cruc
 
 ## Getting started
 
-[Download the vignette](https://gitbio.ens-lyon.fr/aduvermy/HTRfit/-/raw/master/vignettes/HTRfit.html?ref_type=heads&inline=false) for more in-depth information.
+[Download the vignette](https://gitbio.ens-lyon.fr/aduvermy/HTRfit/-/raw/master/vignettes/HTRfit.html?ref_type=heads&inline=false) for more in-depth information about how to use HTRfit.
 
 
 ### Init a design and simulate RNAseq data
@@ -122,7 +122,7 @@ The `fitModelParallel()` function in **HTRfit** provides a powerful way to fit m
 
 ### Diagnostic metrics
 
-The `diagnostic_plot()` function allows to plot a diagnostic plot of AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), logLik (log-likelihood), deviance, df.resid (residual degrees of freedom), and dispersion. These metrics provide insights into how well the model fits the data and help in comparing different models. By examining these metrics, users can quickly identify any anomalies or potential issues in the fitting process
+The `diagnostic_plot()` function allows to plot a diagnostic plot of AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), logLik (log-likelihood), deviance, df.resid (residual degrees of freedom), and dispersion. These metrics provide insights into how well the model fits the data and help in comparing different models. By examining these metrics, users can quickly identify any anomalies or potential issues in the fitting process.
 
 ```
 ## -- plot all metrics
@@ -148,14 +148,14 @@ The identity plot, generated by the `evaluation_report()` function, provides a v
 
 The dispersion plot, generated by the `evaluation_report()` function, offers a visual comparison of the dispersion parameters used in the simulation $\alpha_i$ with those estimated by the model. This graphical representation provides an intuitive way to assess the alignment between the simulated dispersion values and the model-inferred values, enabling a visual evaluation of how well the model captures the underlying data characteristics.
 
-The evaluation of model performance in distinguishing between differentially expressed and non-differentially expressed genes relies on the area under the ROC curve (AUC), a comprehensive metric offering a singular summary of the model's overall effectiveness. A higher AUC is indicative of superior model performance. It is noteworthy that we not only calculate the AUC for the ROC curve but also extend this assessment to the PR curve. In the case of the ROC curve, this AUC should be compared to the value of 0.5, representing a random classifier. On the other hand, for the PR curve, we compute the pr_AUC_random, serving as a baseline for comparison. This dual evaluation approach ensures a thorough understanding of the model's discrimination capabilities under different scenarios, providing valuable insights into its robustness and reliability.
+The evaluation of model performance in distinguishing between differentially expressed and non-differentially expressed genes relies on the area under the ROC curve (AUC), a comprehensive metric offering a singular summary of the model's overall effectiveness. A higher AUC is indicative of superior model performance. It is noteworthy that we not only calculate the AUC for the ROC curve but also extend this assessment to the Precision-Recall (PR) curve. In the case of the ROC curve, this AUC should be compared to the value of 0.5, representing a random classifier. On the other hand, for the PR curve, we compute the pr_AUC_random, serving as a baseline for comparison. This dual evaluation approach ensures a thorough understanding of the model's discrimination capabilities under different scenarios, providing valuable insights into its robustness and reliability.
 
 In addition to evaluating model performance through the AUC for both ROC and PR curves, we provide access to key classification metrics, including Accuracy, Precision, Recall (or Sensitivity), and Specificity. These metrics offer a comprehensive view of the model's classification capabilities.
 
 
-### Comparison DESeq HTRfit
+### Comparing performances of HTRfit & DESeq2
 
-**HTRfit** offers a wrapper for **DESeq2** outputs. This functionality allows users to seamlessly integrate the results obtained from **DESeq2** into the **HTRfit** analysis 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** analysis 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.