diff --git a/docs/bulk_RNAseq-IOC/28_1_manipulation_edgeR_data.md b/docs/bulk_RNAseq-IOC/28_1_manipulation_edgeR_data.md
new file mode 100644
index 000000000..0ef2c03d7
--- /dev/null
+++ b/docs/bulk_RNAseq-IOC/28_1_manipulation_edgeR_data.md
@@ -0,0 +1,416 @@
+# Manipulation of edgeR data for visualisation and comparisons
+
+Now we would like to extract the most differentially expressed genes in the various
+conditions, and then visualize them using an heatmap of the normalized counts for each
+sample.
+
+## Extract the most differentially expressed genes (PRJNA630433 / edgeR)
+
+Basically, we navigate in the edgeR history of the PRJNA630433 use-case and we repeat a
+edgeR run, asking in addition for a file containing the normalised counts, these are in
+**log2 counts per million (logCPM)**.
+
+Note the difference with DESeq2 which instead return **rLog-Normalized counts**. Both
+transformation give very similar results except for low counts that show more dispersion
+with the logCPM approach (see an interesting comparison
+[here](https://idepsite.wordpress.com/pre-process/){:target="_blank"})
+
+??? info "![](images/tool_small.png){width="25" align="absbottom"} `edgeR` settings"
+    - Count Files or Matrix?
+        
+        --> Separate Count Files
+    - 1: Factor/Name
+        
+        --> Tissue
+    - 1: Factor/1: Group
+        
+        Note that there will be three Groups (ie factor levels) in this analysis: Dc, Mo and Oc.
+        
+        --> Oc
+        
+    - Counts file(s)
+        
+        --> select the data collection icon, then `15: Oc FeatureCounts counts`
+    - 2: Factor/2: Group
+        
+        --> Mo
+        
+    - Counts file(s)
+        
+        --> select the data collection icon, then `10: Mo FeatureCounts counts`
+    - 3: Factor level (you must click on :heavy_plus_sign: `Insert Group`)
+        
+        --> Dc
+        
+    - Counts file(s)
+        
+        --> select the data collection icon, then `5: Mo FeatureCounts counts`
+    - Use Gene Annotations?
+        
+        --> `No`
+     - Formula for linear model
+       
+       --> Leave empty
+    - Input contrasts manually or through a file
+        
+        --> `manually`
+    - 1: Constrast
+        
+        --> `Mo-Dc`
+    - 2: Constrast (click :heavy_plus_sign: `Insert Contrast`)
+        
+        --> `Oc-Dc`
+    - 3: Constrast (click :heavy_plus_sign: `Insert Contrast`)
+        
+        --> `Oc-Mo`
+    - Filter Low Counts
+        
+        --> No, leave folded
+    - Output options
+        
+        --> `Yes` to `Output Normalised Counts Table?`
+    - Advanced options
+        
+        --> Put `P-Value Adjusted Threshold` to 0.1 (to be consistent with DESeq settings)
+        
+        --> Leave other advanced options unchanged
+    - `Run Tool`
+
+:warning: This time, the normalized counts are returned as a supplementary dataset in the
+collection `edgeR on data ... and others: Tables`.
+
+Indeed this is an issue to have a this collection with heterogenous datasets (3 DE tables + 
+1 normalized count tables, with different number of columns) since in the next step we are
+going to apply a filter to these data.
+
+We thus will fix this issue immediately in three steps (a bit a Galaxy practice...)
+
+- [x] Use the tool ` Extract element identifiers of a list collection` and run it on the
+collection `edgeR on data ... and others: Tables`. (Be careful to select the last
+collection) This will return a single dataset with the names of the collections elements.
+- [x] Deploy this dataset and click at its bottom to the visualisation icon
+    
+    ![](images/visualisation.png){width="300"}
+    
+    In the central panel that opens up, click the `Editor, Manually edit text`, remove the
+    last line ("edgeR_normcounts"), check that you have **4** line remaining (3 lines plus
+    one empty) and click the light blue button `export` (:warning: this button is not easy
+    to see depending on your screen settings).
+
+- [x] Now use the tool `Filter collection`, select the collection `edgeR on data ... and
+  others: Tables` as input collection, `Remove if identifiers are ABSENT from the file`, and
+  the manually edited dataset `Extract element identifiers on ..., and others (modified)`.
+- [x] This will return 2 collections. Rename immediately the "(filtered)" collection as
+  `edgeR DE tables` and the (discarded) collection as `Log2CPM edgeR_normcounts`. :warning:
+  Note that this latter is a **collection** but with a single element...
+
+
+Beside, `edgeR_normcounts.tsv` also show up as a html link in the dataset `edgeR on
+data 4, data 3, and others: Report`, that download directly to your local computer if you
+click it.
+
+:warning: Keep the 1-element collection `Log2CPM edgeR_normcounts` for latter, we will use
+it for the clustered heatmap.
+
+## Generate top lists of EdgeR DE genes
+
+### Select genes with |log2FC > 2| and p-adj < 0.01 with ![](images/tool_small.png){width="30" align="absbottom"}`Filter data on any column using simple expressions`
+
+!!! info "![](images/tool_small.png){width="25" align="absbottom"} `Filter data on any column...` settings"
+    - Filter
+        
+        --> edgeR DE tables (:warning: this is a collection)
+    - With following condition
+        
+        --> `abs(c2) > 2 and c6 < 0.01` :warning: this expression is different from the one
+        used for DESeq2 tables because the column structure is different.
+    - Number of header lines to skip
+        
+        --> `1` (these tables have an added header !)
+    - Click the `Run Tool` button
+
+:warning: Rename the "filter on..." collection to `edgeR Top gene lists`
+
+### Compute a boolean value by row
+
+This is to determine whether genes in the lists are up or down-regulated
+
+!!! info "![](images/tool_small.png){width="25" align="absbottom"} `Compute on rows` settings"
+    - Input file
+        
+        --> `edgeR Top gene lists` (:warning: collection !)
+    - Input has a header line with column names?
+        
+        --> `Yes`
+    - 1: Expressions
+    - Add expression
+        
+        --> `c2 > 0` :warning: this expression is different from the one used for DESeq2
+        tables
+    - Mode of the operation
+        
+        --> `Append`
+    - The new column name
+        
+        --> `Regulation`
+    - Avoid scientific notation in any newly computed columns
+        
+        --> `No`
+    - Click the `Run Tool` button
+
+:warning: Look at the effect of evaluating the expression `c2 > 0` in the new column
+`expression` in the output datasets.
+
+### Transform `True` and `False` values to `up` and `down`, respectively
+
+!!! info "![](images/tool_small.png){width="25" align="absbottom"} `Column Regex Find And Replace` settings"
+    - Select cells from
+        
+        --> `Compute on collection 40 (or so)`
+    - using column
+        
+        --> `7`
+    - Check
+        
+        --> click the button :heavy_plus_sign:`Insert Check`
+    - Find Regex
+        
+        --> `False`
+    - Replacement
+        
+        --> `down`
+    - Check
+        
+        --> click another time the button :heavy_plus_sign:`Insert Check`
+    - Find Regex
+        
+        --> `True`
+    - Replacement
+        
+        --> `up`
+    - Click the `Run Tool` button
+
+:warning: rename the collection `Column Regex Find And Replace on collection 44` with
+`top gene lists - oriented`
+
+### Split the lists in `up` and `down` regulated lists
+
+This will be performed through 2 successive runs of the
+![](images/tool_small.png){width="25" align="absbottom"} tool `Select lines that match an
+expression`
+
+!!! info "![](images/tool_small.png){width="25" align="absbottom"} `Select lines that match an expression` settings"
+    - Select lines from
+        
+        --> `top gene lists - oriented`
+    - that
+        
+        --> `matching`
+    - the pattern
+        
+        --> `\tup` (a tabulation immediately followed by the string *up*)
+    - Keep header line
+        
+        --> `Yes`
+    - Click the `Run Tool` button
+
+:warning: Immediately rename the collection `Select on collection...` to `edgeR top up-regulated
+gene lists`
+
+Redo exactly the same operation with a single change in the setting of the
+![](images/tool_small.png){width="25" align="absbottom"} tool `Select lines that match an
+expression`
+
+??? info "![](images/tool_small.png){width="25" align="absbottom"} `Select lines that match an expression` settings"
+    - Select lines from
+        
+        --> `top gene lists - oriented`
+    - that
+        
+        --> `matching`
+    - the pattern
+        
+        --> `\tdown` (a tabulation immediately followed by the string *down*)
+    - Keep header line
+        
+        --> `Yes`
+    - Click the `Run Tool` button
+
+:warning: Rename the collection `Select on collection...` to `edgeR top down-regulated
+gene lists`
+
+:warning: keep the last three generated collections for later comparison with edgeR and
+limma tools
+
+## Plotting an heatmap of the most significantly de-regulated genes
+
+For this, we are going to collect and gather all significantly de-regulated genes in any of the
+3 conditions, and to intersect (join operation) this list with the rLog normalized count
+table precedently generated.
+
+### Use ![](images/tool_small.png){width="25" align="absbottom"}`Advanced cut` to select the list of deregulated genes in all three comparisons
+
+!!! info "![](images/tool_small.png){width="25" align="absbottom"} `advanced cut` settings"
+    - File to cut
+        
+        --> `edgeR Top gene lists` (this is a collection)
+    - Operation
+        
+        --> `Keep`
+    - Delimited by
+        
+        --> `Tab`
+    - Cut by
+        
+        --> `fields`
+    - List of Fields
+        
+        --> `Column 1`
+    - First line is a header line
+    - Click the `Run Tool` button
+
+:warning: Rename this collection of single column datasets `edgeR top genes names`
+### Next we concatenate the three datasets of the previous collection in a single dataset
+
+We do that using the ![](images/tool_small.png){width="25" align="absbottom"}
+`Concatenate multiple datasets tail-to-head while specifying how` tool
+
+!!! info "![](images/tool_small.png){width="25" align="absbottom"} `Concatenate multiple datasets tail-to-head while specifying how` settings"
+    - What type of data do you wish to concatenate?
+        
+        --> `Single datasets`
+    - Concatenate Datasets
+        
+        --> :warning: Click on the collection icon and select `edgeR top genes names`
+    - Include dataset names?
+        
+        --> `No`
+    - Number of lines to skip at the beginning of each concatenation:
+        
+        --> `1`
+    - Click the `Run Tool` button
+
+:warning: Rename the return single dataset as `edgeR Pooled top genes`
+
+### Next we extract *Uniques* gene names from the `Pooled top genes` dataset
+
+You probably agree that the same gene may be deregulated in the three pair-wise comparisons
+which we have performed with DESeq2.
+
+Thus we need to eliminate the redundancy, using the tool
+![](images/tool_small.png){width="25" align="absbottom"}`Unique
+occurrences of each record`.
+
+!!! info "![](images/tool_small.png){width="25" align="absbottom"} `Unique occurrences of each record` settings"
+    - File to scan for unique values
+        
+        --> `edgeR Pooled top genes`
+    - Ignore differences in case when comparing
+        
+        --> `No`
+    - Column only contains numeric values
+        
+        --> `No`
+    - Advanced Options
+        
+        --> Leave as `Hide Advanced Options`
+    - Click the `Run Tool` button
+
+### Add a header the list of unique gene names associated we significant DE in any of the comparisons
+
+We do this with the tools `Add Header`
+
+!!! info "![](images/tool_small.png){width="25" align="absbottom"} `Add Header` settings"
+    - List of Column headers (comma delimited, e.g. C1,C2,...)
+        
+        --> `edgeR_All_DE_genes`
+    - Data File (tab-delimted)
+        
+        --> `Unique on data 7x...`
+    - Click the `Run Tool` button
+
+:warning: Rename the generated dataset `edgeR_All_DE_genes`
+
+### Intersection (join operation) between the list of unique gene name associated with DE and the rLog-Normalized counts file.
+
+This is the moment when we are going to use the single-element `Log2CPM edgeR_normcounts`
+collection and intersect it (join operation) with the list of DE genes in all three condition.
+
+To do this, we are going to use the tool
+![](images/tool_small.png){width="25" align="absbottom"}`Join two files`
+
+!!! info "![](images/tool_small.png){width="25" align="absbottom"} `Join two files` settings"
+    - 1st file
+        
+        --> click on the collection icon and select `log2CPM edgeR_normcounts`
+    - Column to use from 1st file
+        
+        --> `1`
+    - 2nd File
+        
+        --> `edgeR_All_DE_genes`
+    - Column to use from 2nd file
+        
+        --> `1`
+    - Output lines appearing in
+        
+        --> `Both 1st and 2nd files`
+    - First line is a header line
+        
+        --> `Yes`
+    - Ignore case
+        
+        --> `No`
+    - Value to put in unpaired (empty) fields
+        
+        --> `NA`
+    - Click the `Run Tool` button
+
+:warning: Rename the single-element output collection `edgeR Log2CPM Normalized counts of DE genes`
+
+### Plot a heatmap of the Log2CPM Normalized counts of edgeR DE genes in all three conditions
+
+We do this using the ![](images/tool_small.png){width="25" align="absbottom"}`Plot
+heatmap with high number of rows` tool
+
+!!! info "![](images/tool_small.png){width="25" align="absbottom"} `Plot heatmap with high number of rows` settings"
+    - Input should have column headers - these will be the columns that are plotted
+        
+        --> `rLog-Normalized counts of DE genes`
+    - Data transformation
+        
+        --> `Plot the data as it is`
+    - Enable data clustering
+        
+        --> `Yes`
+        
+    - Clustering columns and rows
+        
+        --> `Cluster rows and not columns`
+    - Distance method
+        
+        --> `Euclidean`
+        
+    - Clustering method
+        
+        --> `Complete`
+    - Labeling columns and rows
+        
+        --> `Label columns and not rows`
+        
+    - Coloring groups
+        
+        --> `Blue to white to red`
+    - Data scaling
+        
+        --> `Scale my data by row`
+    - tweak plot height
+        
+        --> `35`
+    - tweak row label size
+        
+        --> `1`
+    - tweak line height
+        
+        --> `24`
+    - `Run Tool`
diff --git a/docs/bulk_RNAseq-IOC/28_manipulation_DE_data.md b/docs/bulk_RNAseq-IOC/28_manipulation_DEseq2_data.md
similarity index 93%
rename from docs/bulk_RNAseq-IOC/28_manipulation_DE_data.md
rename to docs/bulk_RNAseq-IOC/28_manipulation_DEseq2_data.md
index 4a26cc167..55da68f43 100644
--- a/docs/bulk_RNAseq-IOC/28_manipulation_DE_data.md
+++ b/docs/bulk_RNAseq-IOC/28_manipulation_DEseq2_data.md
@@ -1,8 +1,8 @@
-# Manipulation of differential expression data for visualisation and comparisons
+# Manipulation of DESeq2 data for visualisation and comparisons
 
 Now we would like to extract the most differentially expressed genes in the various
-conditions, and then visualize them using an heatmap of the normalized counts or computed
-z-score for each sample.
+conditions, and then visualize them using an heatmap of the normalized counts for each
+sample.
 
 We will proceed in several steps:
 
@@ -13,8 +13,6 @@ We will proceed in several steps:
   given p-adjusted value (let's say p-adj < 0.01). We will keep these gene lists apart to
   build latter a venn diagram for comparison of the three tools.
 - [x] Plot heatmaps of normalized counts
-- [x] Compute Z score of the normalized counts
-- [x] Plot heatmaps of the Z score of the normalized counts
 
 ## Extract the most differentially expressed genes (PRJNA630433 / DESeq2)
 
@@ -108,7 +106,28 @@ Replace` and `Filter data on any column using simple expressions`
 
 This is to determine whether genes in the lists are up or down-regulated
 
- 
+!!! info "![](images/tool_small.png){width="25" align="absbottom"} `Compute on rows` settings"
+    - Input file
+        
+        --> `Top gene lists` (:warning: collection !)
+    - Input has a header line with column names?
+        
+        --> `Yes`
+    - 1: Expressions
+    - Add expression
+        
+        --> `c3 > 0`
+    - Mode of the operation
+        
+        --> `Append`
+    - The new column name
+        
+        --> `Regulation`
+    - Avoid scientific notation in any newly computed columns
+        
+        --> `No`
+    - Click the `Run Tool` button
+
 :warning: Look at the effect of evaluating the expression `c3 > 0` in the new column
 `expression` in the output datasets.
 
@@ -144,7 +163,7 @@ This is to determine whether genes in the lists are up or down-regulated
 :warning: rename the collection `Column Regex Find And Replace on collection 40` with
 `top gene lists - oriented`
 
-### Split the list in `up` and `down` regulated lists
+### Split the lists in `up` and `down` regulated lists
 
 This will be performed through 2 successive runs of the
 ![](images/tool_small.png){width="25" align="absbottom"} tool `Select lines that match an
@@ -280,6 +299,8 @@ We do this with the tools `Add Header`
         --> `Unique on data 1xx...`
     - Click the `Run Tool` button
 
+:warning: Rename the generated dataset `All_DE_genes`
+
 ### Intersection (join operation) between the list of unique gene name associated with DE and the rLog-Normalized counts file.
 
 This is the moment when we are going to use the `rLog-Normalized counts file on data...`
diff --git a/docs/bulk_RNAseq-IOC/images/cluster.pdf b/docs/bulk_RNAseq-IOC/images/cluster.pdf
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diff --git a/docs/bulk_RNAseq-IOC/images/cluster.png b/docs/bulk_RNAseq-IOC/images/cluster.png
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diff --git a/docs/bulk_RNAseq-IOC/images/visualisation.png b/docs/bulk_RNAseq-IOC/images/visualisation.png
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diff --git a/mkdocs.yml b/mkdocs.yml
index bb4b0aaa4..51272d0c4 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -84,7 +84,9 @@ nav:
             - DESeq2: bulk_RNAseq-IOC/26_deseq2.md
             - edgeR: bulk_RNAseq-IOC/27_edgeR.md
             - limma: bulk_RNAseq-IOC/27_1_limma.md
-            - DE_manipulations: bulk_RNAseq-IOC/28_manipulation_DE_data.md
+            - DE_manipulations:
+              - DESeq2: bulk_RNAseq-IOC/28_manipulation_DEseq2_data.md
+              - edgeR: bulk_RNAseq-IOC/28_1_manipulation_edgeR_data.md
             - Volcano plot: bulk_RNAseq-IOC/29_volcano.md
 
         - Week 5: