ExGeneOntology R - Revision history

Krivi: /* Molecular Function examples */

2026-09-17T13:23:03Z

Molecular Function examples

← Older revision		Revision as of 15:23, 17 September 2026
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	answer the following questions:		answer the following questions:

	* Can the activities described be ~~directed towards~~ both DNA and RNA? Remember add your arguments		* Can the activities described be done on both DNA and RNA? Remember add your arguments
	* At which node does the "tree" branch out of the "Molecular Function" ontology and into a different ontology? With what type of relationship? Does this make biological sense?		* At which node does the "tree" branch out of the "Molecular Function" ontology and into a different ontology? With what type of relationship? Does this make biological sense?

Krivi: /* Cellular Component examples */

2026-09-17T13:19:35Z

Cellular Component examples

← Older revision		Revision as of 15:19, 17 September 2026
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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:'''
	* Is "nucleus" a membrane-~~bound~~, or non membrane-~~bound~~ organelle? On which linked GO terms do you base this conclusion?		* Is "nucleus" a membrane-bounded, or non membrane-bounded organelle? On which linked GO terms do you base this conclusion?
	* What types of relationships are found among ancestor terms?		* What types of relationships are found among ancestor terms?

Krivi: /* Part 1a: using the Gene Ontology terms and tools */

2026-09-17T13:19:05Z

Part 1a: using the Gene Ontology terms and tools

← Older revision		Revision as of 15:19, 17 September 2026
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	** http://www.ebi.ac.uk/QuickGO - An alternative search system for Gene Ontology, created and maintained by the EBI. They provide a nice graphical representation of the GO trees.		** http://www.ebi.ac.uk/QuickGO - An alternative search system for Gene Ontology, created and maintained by the EBI. They provide a nice graphical representation of the GO trees.
	* '''Overrepresentation analysis:'''		* '''Overrepresentation analysis:'''
	** The R package [https://bioconductor.org/packages/release/bioc/html/fgsea.html,"fgsea"] for over representation analysis, and the R package [https://cran.r-project.org/web/packages/msigdbr/index.html, "msigdbr"] for retrieving genesets. '''Note''': These packages are already installed in the RStudio server - no need to do that yourself.		** The R package [https://bioconductor.org/packages/release/bioc/html/fgsea.html "fgsea"] for over representation analysis, and the R package [https://cran.r-project.org/web/packages/msigdbr/index.html "msigdbr"] for retrieving genesets. '''Note''': These packages are already installed in the RStudio server - no need to do that yourself.

	== Example: "Cell division" ==		== Example: "Cell division" ==

Krivi: /* Cellular Component examples */

2026-09-17T13:17:32Z

Cellular Component examples

← Older revision		Revision as of 15:17, 17 September 2026
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	'''REPORT QUESTION #2:'''		'''REPORT QUESTION #2:'''
	* Is "nucleus" a membrane-bound, or non membrane-bound organelle? On which linked GO terms do you base this conclusion?		* Is "nucleus" a membrane-bound, or non membrane-bound organelle? On which linked GO terms do you base this conclusion?
	* What types of relationships are found?		* What types of relationships are found among ancestor terms?

	'''IS A''' vs. '''PART OF''': So far we have been focusing on the "'''IS A'''" relationship (nucleus IS A organelle which in turn IS A cellular component). However, things get a bit more complicated, when we bring the "PART OF" relationship into the picture, as the next example will show.		'''IS A''' vs. '''PART OF''': So far we have been focusing on the "'''IS A'''" relationship (nucleus IS A organelle which in turn IS A cellular component). However, things get a bit more complicated, when we bring the "PART OF" relationship into the picture, as the next example will show.

Krivi: /* Enrichment analysis - reexamining cluster #1 */

2025-09-17T10:16:10Z

Enrichment analysis - reexamining cluster #1

← Older revision		Revision as of 12:16, 17 September 2025
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	# The '''enrichment''' of observed GO terms compared to expected		# The '''enrichment''' of observed GO terms compared to expected
	# The p-value for each GO term (use <code>fisher.test()</code> in RStudio)		# The p-value for each GO term (use <code>fisher.test()</code> in RStudio)
	# What will happen to the P-value and odds ratio (calculated by <code>fisher.test()</code>) if the background was 500 genes,100,000 genes? What does this mean for the choice of background?		# What will happen to the P-value and odds ratio (calculated by <code>fisher.test()</code>) if the background was 500 genes,100,000 genes? Calculate the result and comment on it. What does this mean for the choice of background?

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Krivi: /* Enrichment analysis - reexamining cluster #1 */

2025-09-11T12:13:27Z

Enrichment analysis - reexamining cluster #1

← Older revision		Revision as of 14:13, 11 September 2025
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	# The '''enrichment''' of observed GO terms compared to expected		# The '''enrichment''' of observed GO terms compared to expected
	# The p-value for each GO term (use <code>fisher.test()</code> in RStudio)		# The p-value for each GO term (use <code>fisher.test()</code> in RStudio)
	# What will happen to the P-value and odds ratio (calculated by <code>fisher.test()</code>) if the background was ~~1000~~ genes,100,000 genes?		# What will happen to the P-value and odds ratio (calculated by <code>fisher.test()</code>) if the background was 500 genes,100,000 genes? What does this mean for the choice of background?

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Krivi: /* Enrichment analysis - reexamining cluster #1 */

2025-09-11T11:52:07Z

Enrichment analysis - reexamining cluster #1

← Older revision		Revision as of 13:52, 11 September 2025
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	# The '''enrichment''' of observed GO terms compared to expected		# The '''enrichment''' of observed GO terms compared to expected
	# The p-value for each GO term (use <code>fisher.test()</code> in RStudio)		# The p-value for each GO term (use <code>fisher.test()</code> in RStudio)
			# What will happen to the P-value and odds ratio (calculated by <code>fisher.test()</code>) if the background was 1000 genes,100,000 genes?

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Krivi: /* Enrichment analysis - reexamining cluster #1 */

2025-09-11T11:50:16Z

Enrichment analysis - reexamining cluster #1

← Older revision		Revision as of 13:50, 11 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 #7:''' Assuming a total of '''5500''' annotated genes calculate/report the following values:		'''TASK/REPORT QUESTION #7:''' Assuming a total of '''5500''' annotated genes (background for this exercise) calculate/report the following values:
	# Population group size		# Population group size
	# Study group size		# Study group size

Krivi: /* Part 2: Gene Ontology overrepresentation analysis (ORA) */

2025-09-11T11:49:13Z

Part 2: Gene Ontology overrepresentation analysis (ORA)

← Older revision		Revision as of 13:49, 11 September 2025
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	* The subset (study group) could be a set of overexpressed genes from a RNAseq experiment, or simply a list of genes which you for other reasons expect to be involved in the same biological process.		* The subset (study group) could be a set of overexpressed genes from a RNAseq experiment, or simply a list of genes which you for other reasons expect to be involved in the same biological process.
	'''Population group:'''		'''Population group:'''
	* The population group would then be defined as the '''background''' to compare to - e.g. the entire list of genes, or in the case of gene expression all genes ~~represented on~~ the ~~microarray~~. We can then ask if the frequency of proteins annotation to a GO term is different for the study group compared to the overall population.		* The population group would then be defined as the '''background''' to compare to - e.g. the entire list of genes, or in the case of gene expression all genes that had the possiblity of ending in the final list (e.g. all genes tested for statistical significance). We can then ask if the frequency of proteins annotation to a GO term is different for the study group compared to the overall population.

	== Enrichment analysis - reexamining cluster #1 ==		== Enrichment analysis - reexamining cluster #1 ==

Krivi: /* Repeat analysis on selected clusters */

2025-09-10T08:36:36Z

Repeat analysis on selected clusters

← Older revision		Revision as of 10:36, 10 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 #12:''' perform the following over-representation analysis and create a short report documenting you finding:		'''TASK/REPORT QUESTION #10:''' perform the following over-representation analysis and create a short report documenting you finding:
	* Biological Process		* Biological Process
	* Molecular function		* Molecular function