Functional Analysis Exercise - Revision history

Rf at 09:08, 11 May 2017

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Rf at 23:41, 10 May 2017

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Rf: Rf moved page I2rda fax to Functional Analysis Exercise over redirect

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 An example of the text file you would download is the file <code>biomart_GO.txt</code>.
-This <code>.txt</code> file can be converted into a <code>.gmt</code> file suitable for use in <code>GSEA</code> using the Perl script <code>makeGMT.pl</code>:
+This <code>.txt</code> file can be converted into a <code>.gmt</code> file suitable for use in <code>GSEA</code> using the Perl script <code>makeGMT.pl</code> which is found in the <code>08_Functional_analysis</code> folder:
   cat biomart_GO.txt | perl makeGMT.pl > your_go_sets.gmt

@@ Line 9: / Line 9: @@
   module load gsea
-The dataset you will investigate is from the study described in '''RNA-Seq Analyses Generate Comprehensive Transcriptomic Landscape and Reveal Complex Transcript Patterns in Hepatocellular Carcinoma Data''' by Huang et al. (<code>http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0026168</code>).
+The dataset you will investigate is from the study described in '''RNA-Seq Analyses Generate Comprehensive Transcriptomic Landscape and Reveal Complex Transcript Patterns in Hepatocellular Carcinoma Data''' by Huang et al. (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0026168).
 You will use the following files:
@@ Line 16: / Line 16: @@
 = Data for analysis =
 Go to the directory <code>08_Functional_analysis</code>
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 This file contains two tab-separated columns, that contain the name of the gene (<code>Ensembl ID</code>) and a numerical value of its differential expression (<code>log FDR</code>). The order of the genes doesn't matter, they will be ranked by GSEA based on their differential expression.
-Have a look at the file go_sets.gmt:
+Have a look at the file <code>go_sets.gmt</code>:
   less go_sets.gmt
@@ Line 44: / Line 45: @@
 # under <code>Steps in GSEA analysis</code>.
 # Click on <code>Method 1: Browse for files</code>
-# Select the files <code>go_sets.gmt</code> and <code>pv_glm.rnk</code> (which are in the directory <code>~/i2rda_data/08_Functional_analysis</code>) and click <code>Open</code>.  (This should give a pop-up message saying 'Files loaded successfully: 2/2 There were NO errors').
+# Select the files <code>go_sets.gmt</code> and <code>pv_glm.rnk</code> (which are in the directory <code>~/i2rda_data/08_Functional_analysis</code>) and click <code>Open</code>.  (This should give a pop-up message saying "Files loaded successfully: 2/2 There were NO errors").
 # Select <code>Tools > GseaPreranked</code> from the top menu bar.
-# Select for the <code>Gene sets database</code> the file <code>go_sets.gmt</code> (which is under the <code>Gene matrix (local gmx/gmt)</code> tab) and click <code>OK</code>.
+# Select for the <code>Gene sets database</code>, the <code>...</code> button and the file <code>go_sets.gmt</code> (which is under the <code>Gene matrix (local gmx/gmt)</code> tab) and click <code>OK</code>.
 # Change the <code>Number of permutations</code> to 100 (for demonstration purposes only).
 # Select for the <code>Ranked list</code> the file pv_glm (this file should already be selected by default).

@@ Line 35: / Line 35: @@
 = Gene Set Enrichment Analysis =
 Launch the GSEA GUI:
@@ Line 43: / Line 45: @@
 # Click on <code>Method 1: Browse for files</code>
 # Select the files <code>go_sets.gmt</code> and <code>pv_glm.rnk</code> (which are in the directory <code>~/i2rda_data/08_Functional_analysis</code>) and click <code>Open</code>.  (This should give a pop-up message saying 'Files loaded successfully: 2/2 There were NO errors').
-# Select 'Tools > GseaPreranked' from the top menu bar.
+# Select <code>Tools > GseaPreranked</code> from the top menu bar.
-# Select for the 'Gene sets database' the file go_sets.gmt (which is under the 'Gene matrix (local gmx/gmt)' tab) and click [OK].
+# Select for the <code>Gene sets database</code> the file <code>go_sets.gmt</code> (which is under the <code>Gene matrix (local gmx/gmt)</code> tab) and click <code>OK</code>.
-# Change the 'Number of permutations' to 100 (for demonstration purposes only).
+# Change the <code>Number of permutations</code> to 100 (for demonstration purposes only).
-# Select for the 'Ranked list' the file pv_glm (this file should already be selected by default).
+# Select for the <code>Ranked list</code> the file pv_glm (this file should already be selected by default).
-# Change 'Collapse dataset to gene symbols' to false.
+# Change <code>Collapse dataset to gene symbols</code> to false.
-# Click on '>Run' at the bottom of the page.
+# Click on <code>>Run</code> at the bottom of the page.
-# Under 'GSEA reports' a 'process' will appear with a status of “Running”.
+# Under <code>GSEA reports</code> a <code>process</code> will appear with a status of "Running".
-# When the status of the process has changed to “Success” click on “Success”. This will open the GSEA Report for our dataset.
+# You need to wait now as it runs its course. When the status of the process has changed to "Success" click on <code>Success</code>. This will open the GSEA Report for our dataset.
 The first section of the report shows the gene sets that are enriched among genes that are up-regulated in cancer compared to non-cancer (remember that we set non-cancer as the reference).

@@ Line 16: / Line 16: @@
 = Data for analysis =
-Go to the directory <code>10_Functional_analysis</code>
+Go to the directory <code>08_Functional_analysis</code>
   cd ~/i2rda_data/08_Functional_analysis/

@@ Line 40: / Line 40: @@
   launchGSEA.sh
-# Click on 'Load data' under 'Steps in GSEA analysis'.
+# under <code>Steps in GSEA analysis</code>.
-# Click on 'Method 1: Browse for files ...'.
+# Click on <code>Method 1: Browse for files</code>
-# Select the files go_sets.gmt and pv_glm.rnk (which are in the directory /home/training/Data/10_Functional_analysis) and click [Open].  (This should give a pop-up message saying 'Files loaded successfully: 2/2 There were NO errors').
+# Select the files <code>go_sets.gmt</code> and <code>pv_glm.rnk</code> (which are in the directory <code>~/i2rda_data/08_Functional_analysis</code>) and click <code>Open</code>.  (This should give a pop-up message saying 'Files loaded successfully: 2/2 There were NO errors').
 # Select 'Tools > GseaPreranked' from the top menu bar.
 # Select for the 'Gene sets database' the file go_sets.gmt (which is under the 'Gene matrix (local gmx/gmt)' tab) and click [OK].

@@ Line 5: / Line 5: @@
 You will use the following tools, which have been pre-installed on <code>marvin</code> our bioinformatics training server at the University of St Andrews:
-*Gene Set Enrichment Analysis (<code>GSEA</code>): <code>http://www.broadinstitute.org/gsea/index.jsp</code>.
+* Gene Set Enrichment Analysis (<code>GSEA</code>): <code>http://www.broadinstitute.org/gsea/index.jsp</code>. This loaded up with
-The data set you will investigate is from the study described in '''RNA-Seq Analyses Generate Comprehensive Transcriptomic Landscape and Reveal Complex Transcript Patterns in Hepatocellular Carcinoma Data''' by Huang et al. (<code>http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0026168</code>).
 You will use the following files:
 * <code>pv_glm.rnk</code>: gene list
 * <code>go_sets.gmt</code>: Gene Ontology gene sets
 = Data for analysis =
@@ Line 32: / Line 32: @@
 This file contains three tab-separated columns, that contain the gene ontology (GO) term name, description, and all the genes that have been annotated with each term. How to create this file is described at the end of this exercise.
-We used Gene Ontology (GO) annotation to create our ''gene sets'', but you can categorise the genes any way you think appropriate.
+We used Gene Ontology (GO) annotation to create our "gene sets", but you can categorise the genes any way you think appropriate.
 = Gene Set Enrichment Analysis =
 Launch the GSEA GUI:
-    java –jar gsea.jar
+ launchGSEA.sh
 # Click on 'Load data' under 'Steps in GSEA analysis'.

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 Go to the directory <code>10_Functional_analysis</code>
-  cd ~/Data/10_Functional_analysis/
+  cd ~/i2rda_data/08_Functional_analysis/
 Have a look at the file pv_glm.rnk:

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 = Data for analysis =
-Go to the directory 09_Differential_expression:
+Go to the directory <code>10_Functional_analysis</code>
   cd ~/Data/10_Functional_analysis/
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   less pv_glm.rnk
-This file contains two tab-separated columns, that contain the name of the gene (Ensembl ID) and a numerical value of its differential expression (log FDR). The order of the genes doesn't matter, they will be ranked by GSEA based on their differential expression.
+This file contains two tab-separated columns, that contain the name of the gene (<code>Ensembl ID</code>) and a numerical value of its differential expression (<code>log FDR</code>). The order of the genes doesn't matter, they will be ranked by GSEA based on their differential expression.
 Have a look at the file go_sets.gmt:
@@ Line 30: / Line 30: @@
   less go_sets.gmt
-This file contains three tab-separated columns, that contain the gene ontology (GO) term name, description, and all the genes that have been annotated with each term. How to create this file is described at the end of this tutorial.
+This file contains three tab-separated columns, that contain the gene ontology (GO) term name, description, and all the genes that have been annotated with each term. How to create this file is described at the end of this exercise.
 We used Gene Ontology (GO) annotation to create our ''gene sets'', but you can categorise the genes any way you think appropriate.
@@ Line 57: / Line 57: @@
 To view the detailed results, click on <code>enrichment results in html format</code>.
-Detailed documentation on how to interpret GSEA results can be found in the GSEA User Guide: http://www.broadinstitute.org/gsea/doc/GSEAUserGuideFrame.html and the paper by ''Subramanian et al.'' '''Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles''', Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50.
+Detailed documentation on how to interpret GSEA results can be found in the GSEA User Guide: http://www.broadinstitute.org/gsea/doc/GSEAUserGuideFrame.html and the paper by Subramanian et al. '''Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles''', ''Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50''.
 Are there any genes up- or down-regulated that look like they could be involved in cancer?
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 = Creating a <code>go_sets.gmt</code> file =
-A go_sets.gmt file can be created by first downloading the GO information from Ensembl (http://www.ensembl.org):
+A <code>go_sets.gmt</code> file can be created by first downloading the GO information from Ensembl (<code>http://www.ensembl.org</code>):
 # Go to Ensembl Biomart: <code>http://www.ensembl.org/biomart/martview/</code>