ExTopology1 igraph - Revision history

Krivi: /* Part IV. Network clustering */

2025-09-04T12:10:37Z

Part IV. Network clustering

← Older revision		Revision as of 14:10, 4 September 2025
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	[[Image:Office-notes-line_drawing.png\|30px\|left]]		[[Image:Office-notes-line_drawing.png\|30px\|left]]
	'''Report question 9''': Make a plot of the entire network with the resolution you think fits the network best (try a few different).		'''Report question 7''': Make a plot of the entire network with the resolution you think fits the network best (try a few different).

	[[Image:Office-notes-line_drawing.png\|30px\|left]]		[[Image:Office-notes-line_drawing.png\|30px\|left]]
	'''Report question 10''': Extract the sub-network from cluster #2 and calculate the network summary statistics we taugth you.		'''Report question 8''': Extract the sub-network from cluster #2 and calculate the network summary statistics we taugth you.

Krivi: /* Part III. Network statistics */

2025-09-04T12:10:24Z

Part III. Network statistics

@@ Line 74: / Line 74: @@
 '''Report question 6''': Make a plot of the distribution of clustering coefficients and paste this into your report
 Where is TP53 in the clustering coefficient distribution plot? (mark it on the distribution plot). Why do you think P53 has this clustering coefficient? Discuss if this clustering coefficient make p53 an important or less important protein in the network.
 == Part IV. Network clustering ==

Krivi: /* Part III. Network statistics */

2025-08-28T08:58:32Z

Part III. Network statistics

← Older revision		Revision as of 10:58, 28 August 2025
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	Where is P53 in the Node Degree Distribution plot? (mark it on the distribution plot). Why do you think P53 has this node degree? Does it make it an important or less important protein in the network?		Where is P53 in the Node Degree Distribution plot? (mark it on the distribution plot). Why do you think P53 has this node degree? Does it make it an important or less important protein in the network?

	Now calculate the node-wise clustering coefficient. This is done with the transitivity() function, setting the variable type = "local".		Now calculate the node-wise clustering coefficient. This is done with the ''transitivity()'' function, setting the variable type = "local".
	[[Image:Office-notes-line_drawing.png\|30px\|left]]		[[Image:Office-notes-line_drawing.png\|30px\|left]]
	'''Report question 6''': Make a plot of the distribution of clustering coefficients and paste this into your report		'''Report question 6''': Make a plot of the distribution of clustering coefficients and paste this into your report

Krivi: /* Part II. Network layout and Selecting nodes */

2025-08-28T08:58:05Z

Part II. Network layout and Selecting nodes

← Older revision		Revision as of 10:58, 28 August 2025
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	'''TASK: Explore TP53 in the network'''		'''TASK: Explore TP53 in the network'''

	Use the igraph function neighbors() to get a list of first order interaction partners of TP53.		Use the igraph function ''neighbors()'' to get a list of first-order interaction partners of TP53.

	[[Image:Office-notes-line_drawing.png\|30px\|left]]		[[Image:Office-notes-line_drawing.png\|30px\|left]]
	'''Report question 1''': How many proteins interact with TP53?		'''Report question 1''': How many proteins interact with TP53?

	Color the nodes ~~depending on~~ whether they interact with TP53 or not, and ~~change~~ the shape of the TP53 node.		Color the nodes according to whether they interact with TP53 or not, and modify the shape of the TP53 node.

	Hint: one way to do this in R is to add two new node attributes (one for TP53 and one for TP53 interaction). You can either add these node attribute vectors to the node attribute table and reload the graph, or you can simply add it using the V() function.		Hint: one way to do this in R is to add two new node attributes (one for TP53 and one for TP53 interaction). You can either add these node attribute vectors to the node attribute table and reload the graph, or you can simply add it using the ''V()'' function.

	Hint 2: ~~for~~ the interaction attribute, start by making a vector repeating "no" n times, where n is the number of nodes. Then, change a subset of that vector to "yes" based on whether a given node interacts with TP53.		Hint 2: For the interaction attribute, start by making a vector repeating "no" n times, where n is the number of nodes. Then, change a subset of that vector to "yes" based on whether a given node interacts with TP53.

	[[Image:Office-notes-line_drawing.png\|30px\|left]]		[[Image:Office-notes-line_drawing.png\|30px\|left]]
	'''Report question 2''': Paste a screen shot of your TP53 annotated network into your report.		'''Report question 2''': Paste a screen shot of your TP53 annotated network into your report.

	Now create a subnetwork consisting only of TP53 and its interaction partners, and label with gene names.		Now, create a subnetwork consisting only of TP53 and its interaction partners, and label it with the corresponding gene names.

	Hint: one way to do this is to use the delete_vertices() function to make a new graph, keeping only the nodes interacting with TP53.		Hint: one way to do this is to use the ''delete_vertices()'' function to make a new graph, keeping only the nodes interacting with TP53. Another way to do it is by using ''induced_subgraph()'' and only keeping the nodes that are neighbours to TP53.

	[[Image:Office-notes-line_drawing.png\|30px\|left]]		[[Image:Office-notes-line_drawing.png\|30px\|left]]

Krivi: /* Part I. Getting started */

2025-08-28T08:50:52Z

Part I. Getting started

← Older revision		Revision as of 10:50, 28 August 2025
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	<pre style="overflow:auto;">		<pre style="overflow:auto;">
	load("/home/projects/22140/~~exercise3~~.Rdata")		load("/home/projects/22140/exercise2.Rdata")
	</pre>		</pre>

Krivi: /* Part IV. Network clustering */

2025-08-21T13:07:45Z

Part IV. Network clustering

← Older revision		Revision as of 15:07, 21 August 2025
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	'''TASK: Find subclusters in the full network'''		'''TASK: Find subclusters in the full network'''

	~~You can use the <pre style="overflow~~:~~auto;">cluster_louvain()</pre> function.~~		Louvain clustering is implemented in:

			<pre style="overflow:auto;">
			cluster_louvain()
			</pre>

			Where the ''resolution'' is the important parameter.

			[[Image:Office-notes-line_drawing.png\|30px\|left]]
			'''Report question 9''': Make a plot of the entire network with the resolution you think fits the network best (try a few different).

	[[Image:Office-notes-line_drawing.png\|30px\|left]]		[[Image:Office-notes-line_drawing.png\|30px\|left]]
	'''Report question 9''': ~~What is~~ the ~~highest number of edges that you need to connect any two nodes in~~ the network?		'''Report question 10''': Extract the sub-network from cluster #2 and calculate the network summary statistics we taugth you.

Krivi: /* Part IV. Network clustering */

2025-08-21T12:58:24Z

Part IV. Network clustering

← Older revision		Revision as of 14:58, 21 August 2025
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	'''TASK: Find subclusters in the full network'''		'''TASK: Find subclusters in the full network'''

	You can use the		You can use the <pre style="overflow:auto;">cluster_louvain()</pre> function.


	[[Image:Office-notes-line_drawing.png\|30px\|left]]		[[Image:Office-notes-line_drawing.png\|30px\|left]]
	'''Report question 9''': What is the highest number of edges that you need to connect any two nodes in the network?		'''Report question 9''': What is the highest number of edges that you need to connect any two nodes in the network?

Krivi: /* Part IV. Network clustering */

2025-08-21T12:57:28Z

Part IV. Network clustering

← Older revision		Revision as of 14:57, 21 August 2025
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	'''TASK: Find subclusters in the full network'''		'''TASK: Find subclusters in the full network'''

			You can use the


	[[Image:Office-notes-line_drawing.png\|30px\|left]]		[[Image:Office-notes-line_drawing.png\|30px\|left]]
	'''Report question 9''': What is the highest number of edges that you need to connect any two nodes in the network?		'''Report question 9''': What is the highest number of edges that you need to connect any two nodes in the network?

Krivi: /* Part IV. Network clustering */

2025-08-21T12:51:19Z

Part IV. Network clustering

← Older revision		Revision as of 14:51, 21 August 2025
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	Now we will turn towards finding sub-clusters in the network.		Now we will turn towards finding sub-clusters in the network.

			'''TASK: Find subclusters in the full network'''

	[[Image:Office-notes-line_drawing.png\|30px\|left]]		[[Image:Office-notes-line_drawing.png\|30px\|left]]
	'''Report question 9''': What is the highest number of edges that you need to connect any two nodes in the network?		'''Report question 9''': What is the highest number of edges that you need to connect any two nodes in the network?

Krivi: /* Part IV. Network clustering */

2025-08-21T12:49:33Z

Part IV. Network clustering

← Older revision		Revision as of 14:49, 21 August 2025
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	== Part IV. Network clustering ==		== Part IV. Network clustering ==

	~~Calculate~~ the network ~~diameter (longest shortest path)~~.		Now we will turn towards finding sub-clusters in the network.

	[[Image:Office-notes-line_drawing.png\|30px\|left]]		[[Image:Office-notes-line_drawing.png\|30px\|left]]
	'''Report question 9''': What is the highest number of edges that you need to connect any two nodes in the network?		'''Report question 9''': What is the highest number of edges that you need to connect any two nodes in the network?

	This phenomenon is known as ‘small-world-network’ and can be found in many real life networks, e.g. the network that connects actors who have appeared in the same movie. You can connect any two actors on [http://oracleofbacon.org/ http://oracleofbacon.org/]. Try, just for fun, with a few actors and see how many edges (movies) that are required to connect them.

	~~[[Image:Office-notes-line_drawing.png\|30px\|left]]~~
	~~'''Report question 10 (optional and just for fun)''': What actor gave you the highest bacon number and who was it?~~