Heart Disease and Virtual Pulldown - Revision history

Krivi: /* Visualizing networks */

2025-09-26T10:19:58Z

Visualizing networks

← Older revision		Revision as of 12:19, 26 September 2025
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	[[Image:Office-notes-line_drawing.png\|30px\|left]]		[[Image:Office-notes-line_drawing.png\|30px\|left]]
	'''REPORT QUESTION #2":		'''REPORT QUESTION #2":
	* ~~Plot~~ the graph created by filtering with confidence score 0.2. Indicate which nodes are seed notes and which are neighbours.		* Use ggraph to plot the graph created by filtering with confidence score 0.2. Indicate which nodes are seed notes and which are neighbours.

	=== Functional classification ===		=== Functional classification ===

Krivi: /* Exercise on "Virtual Pulldowns" */

2025-09-26T10:19:39Z

Exercise on "Virtual Pulldowns"

← Older revision		Revision as of 12:19, 26 September 2025
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	</pre>		</pre>

	2: Extract all connections with a confidence score >= 0.2 and use igraph to create a graph with the data		2: Extract all connections with a confidence score >= 0.2 and use igraph to create a graph with the (data via the graph_from_dataframe() function). No plotting needed now.

Krivi: /* Exercise on "Virtual Pulldowns" */

2025-09-20T16:26:42Z

Exercise on "Virtual Pulldowns"

← Older revision		Revision as of 18:26, 20 September 2025
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	# The PPI database we will use is InWeb:		# The PPI database we will use is InWeb:
	load(file='/home/projects/22140/~~inweb_reduced~~.Rdata')		load(file='/home/projects/22140/exercise4.Rdata')
	</pre>		</pre>

Krivi: /* Functional classification */

2025-09-19T07:12:08Z

Functional classification

← Older revision		Revision as of 09:12, 19 September 2025
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	=== Functional classification ===		=== Functional classification ===
	Use loivant clustering with resolution = 0.4 to find sub-clusters in the network. For the 2-3 largest clusters do gene-set overrepressentation analysis with fora(). What background do you need to use? (you already have all information needed to ~~descided~~ this ~~- no information is missing~~)		Use loivant clustering with resolution = 0.4 to find sub-clusters in the network. For the 2-3 largest clusters do gene-set overrepressentation analysis with fora(). What background do you need to use? (you already have all the information needed to decide and use this background)

	<pre>		<pre>

Krivi: /* Functional classification */

2025-09-19T07:11:33Z

Functional classification

← Older revision		Revision as of 09:11, 19 September 2025
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	=== Functional classification ===		=== Functional classification ===
	Use loivant clustering with resolution = 0.4 to find sub-clusters in the network. For the 2-3 largest clusters do gene-set overrepressentation analysis with fora().		Use loivant clustering with resolution = 0.4 to find sub-clusters in the network. For the 2-3 largest clusters do gene-set overrepressentation analysis with fora(). What background do you need to use? (you already have all information needed to descided this - no information is missing)

	<pre>		<pre>

Krivi: /* Human diseases / virtual pulldown exercise */

2025-09-19T07:10:11Z

Human diseases / virtual pulldown exercise

← Older revision		Revision as of 09:10, 19 September 2025
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	*# Virtual pulldowns: sampling 1st order networks and filtering "sticky proteins"		*# Virtual pulldowns: sampling 1st order networks and filtering "sticky proteins"
	*# Finding tightly connected clusters in larger networks		*# Finding tightly connected clusters in larger networks
	*# Using R to extract, analyze and visualize (sub)networks		*# Using R to extract, analyze, interpret and visualize (sub)networks

	== Introduction ==		== Introduction ==

Krivi: /* Functional classification */

2025-09-19T07:09:30Z

Functional classification

← Older revision		Revision as of 09:09, 19 September 2025
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	[[Image:Office-notes-line_drawing.png\|30px\|left]]		[[Image:Office-notes-line_drawing.png\|30px\|left]]
	'''TASK/REPORT QUESTION #4:'''		'''TASK/REPORT QUESTION #5:'''
	* Which gene is the most central (as defined by betweenness centrality) in the largest cluster? Does that gene make sense in the disease context?		* Which gene is the most central (as defined by betweenness centrality) in the largest cluster? Does that gene make sense in the disease context?

Krivi: /* Functional classification */

2025-09-19T07:09:18Z

Functional classification

← Older revision		Revision as of 09:09, 19 September 2025
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	'''TASK/REPORT QUESTION #4:'''		'''TASK/REPORT QUESTION #4:'''
	* Suggest the main biological function for each cluster. Do they make biological sense in the context of heart disease?		* Suggest the main biological function for each cluster. Do they make biological sense in the context of heart disease?

			[[Image:Office-notes-line_drawing.png\|30px\|left]]
			'''TASK/REPORT QUESTION #4:'''
			* Which gene is the most central (as defined by betweenness centrality) in the largest cluster? Does that gene make sense in the disease context?

Krivi: /* Exercise on "Virtual Pulldowns" */

2025-09-17T11:39:53Z

Exercise on "Virtual Pulldowns"

← Older revision		Revision as of 13:39, 17 September 2025
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	== Exercise on "Virtual Pulldowns" ==		== Exercise on "Virtual Pulldowns" ==
	For this part of the exercise, we'll be using a resource created here at DTU: the "InWeb" inferred human interactome. ~~As we have been going through in much more details in the lecture, InWeb was build by:~~		For this part of the exercise, we'll be using a resource created here at DTU: the "InWeb" inferred human interactome. This is one of many interaction networks. Note that it has a confidence score (cs-score) telling us how certain we about each interaction.
	~~# Transferring interactions from model organisms to human by orthology (if a pair~~ of ~~interacting proteins have strong orthologs in human, the~~ interaction ~~is transferred)~~
	~~# '''Scoring''' the reliability of the interactions~~. ~~This allows for filtering out interactions with little experimental support, thus building~~ a ~~high-~~confidence ~~network~~

	~~For querying the InWeb we'll use the "DiscoNet" R package, which works as follows:~~

	~~# Input: a list of protein believed to be involved in a particular biology~~ (~~here: different heart diseases/developmental stages~~)
	~~# For each protein, 1st order interaction partners are found~~
	~~# For all input proteins and all 1st order interaction partners, a combined network is built~~
	~~# For the combined network a series of '''scored subnetworks''' are build in order to filter away "sticky proteins" (as~~ we ~~talked~~ about ~~in the lecture)~~
	~~# An overrepresentation analysis is performed for~~ each ~~complex using the fgsea package~~
	~~# Finally a visual representation of the network is presented~~

	=== Heart disease proteins ===		=== Heart disease proteins ===

Krivi: /* Human diseases / virtual pulldown exercise */

2025-09-17T11:38:19Z

Human diseases / virtual pulldown exercise

← Older revision		Revision as of 13:38, 17 September 2025
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	*# Virtual pulldowns: sampling 1st order networks and filtering "sticky proteins"		*# Virtual pulldowns: sampling 1st order networks and filtering "sticky proteins"
	*# Finding tightly connected clusters in larger networks		*# Finding tightly connected clusters in larger networks
	*# Using ~~the DiscoNet package in~~ R		*# Using R to extract, analyze and visualize (sub)networks

	== Introduction ==		== Introduction ==