Merge pull request #1493 from mashehu/fix-de-tutorial

fix rendering of DE tutorial

Author: LorenzoS96

docs/usage/DEanalysis/de_rstudio.md renamed to docs/usage/differential_expression_analysis/de_rstudio.md

@@ -1,5 +1,6 @@
 ---
 order: 4
+shortTitle: RStudio
 ---
 
 # Differential Analysis with DESeq2
@@ -33,9 +34,7 @@ As in all analysis, firstly we need to create a new project:
 
 2. Select **New Directory**, **New Project**, name the project as shown below and click on **Create Project**;
 
-<figure markdown="span">
-  ![r_project](./img/project_R.png){ width="400" }
-</figure>
+![r_project](../differential_expression_analysis/img/project_R.png)
 
 3. The new project will be automatically opened in RStudio.
 
@@ -48,9 +47,7 @@ To store our results in an organized way, we will create a folder named **de_res
 
 and save the file as **de_script.R**. From now on, each command described in the tutorial can be added to your script. The resulting working directory should look like this:
 
-<figure markdown="span">
-  ![work_dir](./img/workdir_RStudio.png){ width="600" }
-</figure>
+![work_dir](../differential_expression_analysis/img/workdir_RStudio.png)
 
 The analysis requires several R packages. To utilise them, we need to load the following libraries:
 
@@ -162,9 +159,7 @@ design(dds_new) # to check the design formula
 
 Comparing the structure of the newly created dds (`dds_new`) with the one automatically produced by the pipeline (`dds`), we can observe the differences:
 
-<figure markdown="span">
-  ![comparison_dds](./img/dds_comparison.png){ width="400" }
-</figure>
+![comparison_dds](../differential_expression_analysis/img/dds_comparison.png)
 
 Before running the different steps of the analysis, a good practice consists in pre-filtering the genes to remove those with very low counts. This is useful to improve computional efficiency and enhance interpretability. In general, it is reasonable to keep only genes with a sum counts of at least 10 for a minimal number of 3 samples:
 
@@ -438,7 +433,7 @@ plotCounts(dds_final, gene = "ENSG00000142192")
 dev.off()
 ```
 
-**heatmap**: plot of the normalised counts for all the significant genes obtained with the `pheatmap()` function. The heatmap provides insights into genes and sample relationships that may not be apparent from individual gene plots alone.
+- **heatmap**: plot of the normalised counts for all the significant genes obtained with the `pheatmap()` function. The heatmap provides insights into genes and sample relationships that may not be apparent from individual gene plots alone.
 
 ```r
 #### Heatmap ####