From 6f1f3e36a54545f2b2a17b48e2e6526068c03432 Mon Sep 17 00:00:00 2001
From: aduvermy <arnaud.duvermy@ens-lyon.fr>
Date: Fri, 3 Nov 2023 10:19:03 +0100
Subject: [PATCH] update readme

---
 README.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/README.md b/README.md
index f31cc9c..73466ea 100644
--- a/README.md
+++ b/README.md
@@ -162,6 +162,11 @@ resSimu <- simulationReport(mock_data,
 
 ```
 
+The identity plot, generated by the `simulationReport()` function, provides a visual means to compare the effects used in the simulation (actual effects) with those inferred by the model. This graphical representation facilitates the assessment of the correspondence between the values of the simulated effects and those estimated by the model, allowing for a visual analysis of the model’s goodness of fit to the simulated data.
+
+The dispersion plot, generated by the `simulationReport()` 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 Receiver Operating Characteristic (ROC) curve is a valuable tool for assessing the performance of classification models, particularly in the context of identifying differentially expressed genes. It provides a graphical representation of the model’s ability to distinguish between genes that are differentially expressed and those that are not, by varying the `coeff_threshold` and the `alt_hypothesis` parameters. The area under the ROC curve (AUC) provides a single metric that summarizes the model’s overall performance in distinguishing between differentially expressed and non-differentially expressed genes. A higher AUC indicates better model performance.
 
 
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