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Answers.Rmd
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---
title: "Answers to Course Exercise"
author: "Akshay Bhat"
date: '`r format(Sys.time(), "Last modified: %d %b %Y")`'
output:
html_notebook:
toc: yes
toc_float: yes
css: stylesheets/styles.css
pdf_document:
toc: yes
---
<img src="images/logo-sm.png" style="position:absolute;top:40px;right:10px;" width="200" />
# Answers
## Exercise 2-A
* When the enrichment analysis is complete, a new tab titled **STRING** **Enrichment** will open in the **Table Panel**.<br></br>
<br></br>
<br></br><br></br>
* At the top left of the STRING enrichment tab, click the filter icon `r icons::fontawesome("filter", style = "solid")` . Select **GO Biological Process** and check the **Remove redundant terms check-box**. Then click **OK.**<br></br>
* Next, add a split donut chart to the nodes representing the top terms by clicking on <br></br>
* Explore custom settings via in the top right of the STRING enrichment tab.<br></br>
<br></br><br></br>
### Export your Networks
Cytoscape provides a number of ways to save results and visualizations: <br></br>
* As a session: **File → Save Session, File → Save Session As...** <br></br>
* As an image: **File → Export → Network to Image...** <br></br>
* As a graph format file: **File → Export → Network to File.** <br></br>
* **Formats:** <br></br>
* **CX JSON / CX2 JSON** <br></br>
* **Cytoscape.js JSON** <br></br>
* **GraphML** <br></br>
## Exercise 2-B
* Find significant expression changes in **Cluster 1** <br></br>
**Hint:** We are going to repeat step 4 with "Cluster 1 Mean log2FC" as the column. This represents the mean expression across the samples in cluster 1 (the leftmost cluster in the previous figure).
Open the **Filter** tab and setup two **Column Filters** for the **"Cluster 1 Mean log2FC"** column, one for up and one for down. Select the **"Cluster 1 Mean log2FC"** column and set the values to be between **-5 and-1**, then add a second filter (be sure to specify **OR **at the top) and set the values between **1 and 5**.
This should result in a selection of 192 nodes.
<br></br>
* Download the PPI from STRING
From the above selection filter, we’ve identified 192 nodes that show an average **Log2FC greater than 5 or less than 1**, and selected those nodes. Now, we create a separate network that includes all of those 192 nodes by selecting the **"Create Network from selected nodes and edges option"** and then load the protein-protein interaction data from **STRING.** Note that only the selected nodes are shown in the **Table Panel**.
## **Select gene names from Table Panel.**
* Select everything in the **name** column by clicking into the first cell and then dragging down until you get to the bottom. Then, do a copy **(Control-C or Apple-C).**
<br></br>
* **Paste gene names into STRING network search**. In the **Network** tab of the **Control Panel** at the top should be a text field with an icon at the left. Click on that icon and select **STRING protein query**. (If you don’t see any **STRING** options, the **stringApp** hasn’t been loaded and needs to be loaded from either the App store or Cytoscape options menu.) Then click into the text field and paste the list of genes.
<br></br>
* **Set STRING search parameters.** Next to the text field is a menu with a list of options. Change the **Confidence (score) cutoff** to 0.8 and the **Maximum additional interactors** to 30. This will get only high quality results (80% confidence) and add 30 extra proteins to the network.
<br></br>
## **Create the network.**
Click on the search icon (magnifying glass) to load the network. The network should appear similar to the figure below.
<br></br><br></br>
* Style the network to show differential expression.
In this step, we’ll change the style of the network to highlight the differentially expressed genes.
</div> <br></br>
## Exercise 3-A
# Walk-through exercise
For the exercise, we are going to use mRNA bladder cancer data generated from RNA-Seq platform. The data can be retrived from Robertson et al. Cell 2017 or downloaded from ([Here..](https://github.com/a1aks/Cytoscape_Course/tree/main/Data_Files))
## ClueGO settings
* set the type of analysis: Compare Cluster
* select the organism: Homo Sapiens and the the type of ids used: AccessionID
* load sample gene lists:
* **choose Cluster #1**: select Top_Down_genes_BLCA_Session3.txt - ([Here..](https://raw.githubusercontent.com/a1aks/Cytoscape_Course/main/Data_Files/Top_Down_genes_BLCA_Session3.txt))
* **choose Cluster #2**: Top_Up_genes_BLCA_Session3.txt - ([Here..](https://raw.githubusercontent.com/a1aks/Cytoscape_Course/main/Data_Files/Top_Up_genes_BLCA_Session3.txt))
* select the Ontologies:
* GO BiologicalProcess,
* KEGG Pathways and
* BioCarta pathway reactome
* select the statistical test: **Enrichment/Deplection (Two sided hypergeometric test)**, **FisherExactTest**
* select the correction method: **Bonferroni**
* click Show Advanced Settings
* set GO Tree Level: Min 4 and Max 5
* set the selection criteria for the terms that have associated genes from cluster 1: min 2 genes/term
and minimum 4% from all the Genes associated with the term
* select OR (e.g. min 2 genes from cluster #1 or min 2 genes from cluster #2)
* set is specific to 66% (if 66% or the genes associated with the term are from cluster #1, the term
is considered specific for this cluster)
* set the selection criteria for the terms that have associated genes from cluster 2: min 2 genes/term
and minimum 4% from all the Genes associated with the term
* select Use GO Term Grouping
* select Fix Group coloring
* select Leading Group Term based on Highest Significance
* select Kappa Score grouping with 3 terms in initial group and 50% overlap for groups to merge
* select ShowDifference
* Start
## Customize the network using Cytoscape features
* select **Style** (Cytoscape Control Panel)
* select Node Font Size
* set the value of FALSE (size for the name of the terms) to 0.001 and press Enter
* set the value of TRUE (size for the name of groups) to 26 and press Enter
* select Layout (Cytoscape menu bar)
* set scale to 1/3
* zoom the image
* change the position of the leading terms to make visible the name of the group
* change the possition of notgrouped terms if due to rescaling they are too close to a group
* for visualizing groups press Show Groups
* Using the filters and style options above, develop a network by using cluster compare for the two bladder cancer conditions - Normal VS Tumor.
* Save your network in two formats: (a) as a Cytoscape Session (**.sys ** format), and (b) as a **.png or .pdf** figure.
* The cytoscape session (.sys) will be used in the next section for CluePedia analysis.
More details on using specific options can be achieved by folowing the **ClueGO** documentation: http://www.ici.upmc.fr/cluego/ClueGODocumentation2019.pdf
</div>
<br></br>