Finalised clusterProfiler

Author: calizilla

11-gprofiler2.Rmd

@@ -37,33 +37,39 @@ Since we are doing ORA, we will need a filtered gene list, and a background gene
 
 <p>&nbsp;</p>  <!-- insert blank line -->
 
-## End of activity summary
-
-- We have extracted a gene list and background gene list from a DE dataset and run ORA with `gprofiler2` `gost` function
-- We have plotted the data with `gostplot` Manhattan plots and `ggplot2` dotplots
-- We have run a `gost` multi-query separating up and down regulated genes
-- We have verified that `gprofiler2` results match the results from `g:Profiler` web
-- We have captured all version details relevant to the session within the R notebook
-
-<p>&nbsp;</p>  <!-- insert blank line -->
-
-The last task is to `knit` the notebook. Our notebook is editable, and can be changed. Deleting code deletes the output, so we could lose valuable details. If we knit the notebook to HTML, we have a permanent static copy of the work. 
-
+**To finish up:** The last task is to `knit` the notebook. Our notebook is editable, and can be changed. Deleting code deletes the output, so we could lose valuable details. If we knit the notebook to HTML, we have a permanent static copy of the work. 
 
 &#x27A4; Knit the notebook to HTML 
 
 On the `editor` pane toolbar, under `Preview`, select `Knit to HTML`.
 
 If you have already run `Preview`, you will see `Knit` instead of `Preview`. 
 
-The HTML file will be saved in the same directory as the notebook, and with the same filename, but the `.Rmd` prefix will be replaced by `.html`. The knit HTML will typically open automatically once complete. If you receive a popup blocker error, click `cancel`, and in the `Files` pane of RStudio, single click the `gprofiler.html` file and select `View in Web Browser`. 
+The HTML file will be saved in the same directory as the notebook, and with the same filename, but the `.Rmd` prefix will be replaced by `.html`. The knit HTML will typically open automatically once complete. 
+
+❗If you receive a popup blocker error, click `cancel` (not `try again`), and in the `Files` pane of RStudio, single click the `gprofiler.html` file and select `View in Web Browser`. 
 
 <img src="images/open-html-rstudio.png" style="border: none; box-shadow: none; background: none; ">
 
 <p>&nbsp;</p>  <!-- insert blank line -->
 
 Note that the notebook will only successsfully knit if there are <span style="color: red;">no errors</span> in the code. You can 'preview' HTML with code errors. 
 
-✋ **If your knit fails, please ask for assistance resolving the errors**  
+✋ **If your knit fails, please ask for assistance resolving the errors**
+
+## End of activity summary
+
+- We have extracted a gene list and background gene list from a DE dataset and run ORA with `gprofiler2` `gost` function
+- We have plotted the data with `gostplot` Manhattan plots and `ggplot2` dotplots
+- We have run a `gost` multi-query separating up and down regulated genes
+- We have verified that `gprofiler2` results match the results from `g:Profiler` web
+- We have saved a rendered HTML document as a static record of our analysis including all code executed, parameters applied, as well as version information for R, RStudio, R packages and gProfiler database to ensure reproducibility
+
+<p>&nbsp;</p>  <!-- insert blank line -->
+
+
+
+
+  
 
 

12-clusterprofiler.Rmd

@@ -1,7 +1,7 @@
 # clusterProfiler and enrichplot
 
 
-[clusterProfiler](https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html) is a comprehensive suite of enrichment tools. It has functions to run ORA or GSEA over commonly used databases (GO, KEGG, KEGG Modules, DAVID, Pathway Commons, WikiPathways) as well as universal enrichment functions to perform ORA or GSEA with custom gene sets. We will use these universal tools in the final activity of this workshop, focusing on the supported organisms and datbases for the present activity. 
+[clusterProfiler](https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html) is a comprehensive suite of enrichment tools. It has functions to run ORA or GSEA over commonly used databases (GO, KEGG, KEGG Modules, DAVID, Pathway Commons, WikiPathways) as well as universal enrichment functions to perform ORA or GSEA with novel species or custom gene sets. We will use these universal tools in the final activity of this workshop, focusing on the supported organisms and datbases for the present activity. 
 
 One of the key advantages of using R over web tools is flexibility with visualisations. 
 
@@ -46,7 +46,7 @@ Since we have covered ORA with `gprofiler`, we will perform a GSEA with `cluster
 3. Extract the gene IDs and sort by log2 fold change to create the GSEA ranked gene list R object
 4. Use `bitr` to convert gene IDs from ENSEMBL to ENTREZ for compatability with `gseKEGG`
 5. Perform GSEA with `gseKEGG`
-6. Visualise results with `enrichplot`
+6. Visualise results with many different plot types from `enrichplot`
 
 <p>&nbsp;</p>  <!-- insert blank line -->
 
@@ -69,10 +69,10 @@ Let's head over to RStudio now and try out some functions! 🏃
 
 ## End of activity summary
 
-- We have explored the supported organisms and namespaces of the `clusterProfiler` enrichment functions 
+- We have explored the supported organisms, namespaces and databases of the `clusterProfiler` enrichment functions 
 - We have extracted a ranked gene list for GSEA and converted the gene IDs for compatability with `gseKEGG`
 - We have performed GSEA on the KEGG database with `gseKEGG` and visualised the results with multiple plot types 
-- We have captured all version details relevant to the session within the R notebook
+- We have captured all version details relevant to the session within the R notebook and knit the file to HTML for record keeping
 
 <p>&nbsp;</p>  <!-- insert blank line -->