Difference between revisions of "Kwrigh35 Week 10"

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==Electronic Notebook==
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==Deliverables==
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===Clustering and GO Term Enrichment with stem===
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[[media:DZAP1_STEM_Results_Kwrigh35.zip | STEM Results]] including tab-delimited files and ppt with results.
  
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# '''Prepare your microarray data file for loading into STEM.'''
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[[media:dZAP1_ANOVA_KWRIGH35.zip | Updated Excel Workbook]] including STEM page.
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#* Download your Excel workbook that you used for your [[Week 8]] assignment.
+
 
−
#* Insert a new worksheet into your Excel workbook, and name it "(STRAIN)_stem".
+
==Electronic Notebook: Clustering and GO Term Enrichment with stem==
−
#* Select all of the data from your "(STRAIN)_ANOVA" worksheet and Paste special > paste values into your "(STRAIN)_stem" worksheet.
+
===Prepare your microarray data file for loading into STEM===
−
#** Your leftmost column should have the column header "Master_Index".  Rename this column to "SPOT".  Column B should be named "ID".  Rename this column to "Gene Symbol".  Delete the column named "Standard_Name".
+
* Download your Excel workbook that you used for your [[Week 8]] assignment.
−
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
+
* Insert a new worksheet into your Excel workbook, and name it "(STRAIN)_stem".
−
#*** Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu.  Undo the filter.  This ensures that we will cluster only the genes with a "significant" change in expression and not the noise.  '''''Record the number of genes left in your electronic notebook.''''' (1785 genes left)
+
* Select all of the data from your "(STRAIN)_ANOVA" worksheet and Paste special > paste values into your "(STRAIN)_stem" worksheet.
−
#** Delete all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
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** Your leftmost column should have the column header "Master_Index".  Rename this column to "SPOT".  Column B should be named "ID".  Rename this column to "Gene Symbol".  Delete the column named "Standard_Name".
−
#** Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
+
** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
−
#** Save your work.  Then use ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt).  Click OK to the warnings and close your file.
+
*** Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu.  Undo the filter.  This ensures that we will cluster only the genes with a "significant" change in expression and not the noise.  '''''Record the number of genes left in your electronic notebook.''''' <span style="color:red">1785 genes left</span>
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#*** Note that you should turn on the file extensions if you have not already done so.
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** Delete all of the data columns '''''EXCEPT''''' for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
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# '''Now download and extract the STEM software.'''  [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
+
** Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
−
#* Click on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
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** Save your work.  Then use ''Save As'' to save this spreadsheet as Text (Tab-delimited) (*.txt).  Click OK to the warnings and close your file.
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#* Unzip the file.  In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
+
*** Note that you should turn on the file extensions if you have not already done so.
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#* This will create a folder called <code>stem</code>.  Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.
+
===Now download and extract the STEM software===
 +
[http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
 +
* Click on the [http://www.andrew.cmu.edu/user/zivbj/stemreg.html download link], register, and download the <code>stem.zip</code> file to your Desktop.
 +
* Unzip the file.  In Seaver 120, you can right click on the file icon and select the menu item ''7-zip > Extract Here''.
 +
* This will create a folder called <code>stem</code>.  Inside the folder, double-click on the <code>stem.jar</code> to launch the STEM program.
 
<!--#** In Seaver 120, we encountered an issue where the program would not launch on the Windows XP machines due to a lack of memory. (Even though the computers have been upgraded to Windows 7, do this to launch the program.)  To get around this problem, launch STEM from the command line.
 
<!--#** In Seaver 120, we encountered an issue where the program would not launch on the Windows XP machines due to a lack of memory. (Even though the computers have been upgraded to Windows 7, do this to launch the program.)  To get around this problem, launch STEM from the command line.
−
#*** Go to the start menu and click on ''Programs > Accessories > Command Prompt''.
+
*** Go to the start menu and click on ''Programs > Accessories > Command Prompt''.
−
#*** You will need to navigate to the directory (folder) in which the STEM program resides.  If you followed the instructions above and extracted the stem folder to the Desktop, type the following:  <code>cd Desktop\stem</code>  and press "Enter".
+
*** You will need to navigate to the directory (folder) in which the STEM program resides.  If you followed the instructions above and extracted the stem folder to the Desktop, type the following:  <code>cd Desktop\stem</code>  and press "Enter".
−
#*** To launch the program then type:  <code>java -mx512M -jar stem.jar -d defaults.txt</code>  and press "Enter".  This will launch the program with less memory allocated to it.-->
+
*** To launch the program then type:  <code>java -mx512M -jar stem.jar -d defaults.txt</code>  and press "Enter".  This will launch the program with less memory allocated to it.-->
−
# '''Running STEM'''
 
−
## In section 1 (Expression Data Info) of the the main STEM interface window, click on the ''Browse...'' button to navigate to and select your file.
 
−
##* Click on the radio button ''No normalization/add 0''.
 
−
##* Check the box next to ''Spot IDs included in the data file''.
 
−
## In section 2 (Gene Info) of the main STEM interface window, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source.  Select ''No cross references'', from the Cross Reference Source drop-down menu.  Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
 
−
## In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
 
−
## In section 4 (Execute) click on the yellow Execute button to run STEM.
 
−
# '''Viewing and Saving STEM Results'''
 
−
## A new window will open called "All STEM Profiles (1)".  Each box corresponds to a model expression profile.  Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value.  Profiles with the same color belong to the same cluster of profiles.  The number in each box is simply an ID number for the profile.
 
−
##* Click on the button that says "Interface Options...".  At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time".  Then close the Interface Options window.
 
−
##*Take a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
 
−
## Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
 
−
##* Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
 
−
##* At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table".  For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile.  In the window that appears, click on the "Save Table" button and save the file to your desktop.  Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
 
−
##** Upload these files to the wiki and link to them on your individual journal page.  (Note that it will be easier to zip all the files together and upload them as one file).
 
−
##* For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile.  In the window that appears, click on the "Save Table" button and save the file to your desktop.  Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number.  At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
 
−
##** Upload these files to the wiki and link to them on your individual journal page.  (Note that it will be easier to zip all the files together and upload them as one file).
 
−
# '''Analyzing and Interpreting STEM Results'''
 
−
## Select '''''one''''' of the profiles you saved in the previous step for further intepretation of the data.  I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints.  '''''Each member of your group should choose a different profile.'''''  Answer the following:
 
−
##* '''''Why did you select this profile?  In other words, why was it interesting to you?'''''
 
−
##* '''''How many genes belong to this profile?'''''
 
−
##* '''''How many genes were expected to belong to this profile?'''''
 
−
##* '''''What is the p value for the enrichment of genes in this profile?'''''  Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point.  This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
 
−
##* Open the GO list file you saved for this profile in Excel.  This list shows all of the Gene Ontology terms that are associated with genes that fit this profile.  Select the third row and then choose from the menu Data > Filter > Autofilter.  Filter on the "p-value" column to show only GO terms that have a p value of < 0.05.  '''''How many GO terms are associated with this profile at p < 0.05?'''''  The GO list also has a column called "Corrected p-value".  This correction is needed because the software has performed thousands of significance tests.  Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05.  '''''How many GO terms are associated with this profile with a corrected p value < 0.05?'''''
 
−
##* Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05). 
 
−
##** Each member of the group will be reporting on his or her own cluster in your presentation next week.  You should take care to choose terms that are the most significant, but that are also not too redundant.  For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
 
−
##*** Note whether the same GO terms are showing up in multiple clusters.
 
−
##**'''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org].  In your final presentation, you will discuss the biological interpretation of these GO terms.  In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms?  Also, what does this have to do with the transcription factor being deleted (for the Δgln3 and Δswi4 groups)?'''''
 
−
##** To easily look up the definitions, go to [http://geneontology.org http://geneontology.org].
 
−
##** Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
 
−
##** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
 
−
##** The definition will be on the next results page, e.g. [http://amigo.geneontology.org/amigo/term/GO:0044848 here].
 
  
−
===Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes===
+
===Running STEM===
 +
* In section 1 (Expression Data Info) of the the main STEM interface window, click on the ''Browse...'' button to navigate to and select your file.
 +
** Click on the radio button ''No normalization/add 0''.
 +
** Check the box next to ''Spot IDs included in the data file''.
 +
* In section 2 (Gene Info) of the main STEM interface window, select ''Saccharomyces cerevisiae (SGD)'', from the drop-down menu for Gene Annotation Source.  Select ''No cross references'', from the Cross Reference Source drop-down menu.  Select ''No Gene Locations'' from the Gene Location Source drop-down menu.
 +
* In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
 +
* In section 4 (Execute) click on the yellow Execute button to run STEM.
 +
===Viewing and Saving STEM Results===
 +
* A new window will open called "All STEM Profiles (1)".  Each box corresponds to a model expression profile.  Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value.  Profiles with the same color belong to the same cluster of profiles.  The number in each box is simply an ID number for the profile.
 +
** Click on the button that says "Interface Options...".  At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time".  Then close the Interface Options window.
 +
**Take a screenshot of this window (on a PC, simultaneously press the <code>Alt</code> and <code>PrintScreen</code> buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
 +
* Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
 +
** Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
 +
** At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table".  For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile.  In the window that appears, click on the "Save Table" button and save the file to your desktop.  Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
 +
*** Upload these files to the wiki and link to them on your individual journal page.  (Note that it will be easier to zip all the files together and upload them as one file).
 +
** For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile.  In the window that appears, click on the "Save Table" button and save the file to your desktop.  Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number.  At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
 +
*** Upload these files to the wiki and link to them on your individual journal page.  (Note that it will be easier to zip all the files together and upload them as one file).
 +
===Analyzing and Interpreting STEM Results===
 +
* Select '''''one''''' of the profiles you saved in the previous step for further interpretation of the data.  I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints.  '''''Each member of your group should choose a different profile.'''''  Answer the following:
 +
** '''''Why did you select this profile?  In other words, why was it interesting to you?'''''
 +
*** I selected this profile because it had the lowest p-value, the most genes, and a very clear shape. 
 +
** '''''How many genes belong to this profile?'''''
 +
*** 498
 +
** '''''How many genes were expected to belong to this profile?'''''
 +
*** 46.5
 +
** '''''What is the p value for the enrichment of genes in this profile?'''''  Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point.  This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
 +
*** The p-value is 0.  The p-value is most likely not actually 0, but rounded to 0 since the number was so small.
 +
** Open the GO list file you saved for this profile in Excel.  This list shows all of the Gene Ontology terms that are associated with genes that fit this profile.  Select the third row and then choose from the menu Data > Filter > Autofilter.  Filter on the "p-value" column to show only GO terms that have a p value of < 0.05.  '''''How many GO terms are associated with this profile at p < 0.05?''''' <span style="color:red">263</span> The GO list also has a column called "Corrected p-value".  This correction is needed because the software has performed thousands of significance tests.  Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05.  '''''How many GO terms are associated with this profile with a corrected p value < 0.05?'''''<span style="color:red">16</span>
 +
** Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05). 
 +
*** Each member of the group will be reporting on his or her own cluster in your presentation next week.  You should take care to choose terms that are the most significant, but that are also not too redundant.  For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
 +
**** Note whether the same GO terms are showing up in multiple clusters.
 +
***'''''Look up the definitions for each of the terms at [http://geneontology.org http://geneontology.org].  In your final presentation, you will discuss the biological interpretation of these GO terms.  In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms?  Also, what does this have to do with the transcription factor being deleted (for the Δgln3 and Δswi4 groups)?'''''
 +
*** To easily look up the definitions, go to [http://geneontology.org http://geneontology.org].
 +
*** Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
 +
*** In the [http://amigo.geneontology.org/amigo/medial_search?q=GO%3A0044848 results] page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
 +
*** The definition will be on the next results page, e.g. [http://amigo.geneontology.org/amigo/term/GO:0044848 here].
  
−
In the previous analysis using STEM, we found a number of gene expression profiles (aka clusters) which grouped genes based on similarity of gene expression changes over time.  The implication is that these genes share the same expression pattern because they are regulated by the same (or the same set) of transcription factors.  We will explore this using the YEASTRACT database.
+
==Acknowledgements==
−
 
+
I'd like to thank my homework partener [[user:Aporras1|Antonio Porras]].  We worked together during class and nearly finished the assignment, with plans to text each other if we have questions.   
−
# Open the gene list in Excel for the one of the significant profiles from your stem analysis.  Choose a cluster with a clear cold shock/recovery up/down or down/up pattern.  You should also choose one of the largest clusters.
 
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#* Copy the list of gene IDs onto your clipboard.
 
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# Launch a web browser and go to the [http://www.yeastract.com/ YEASTRACT database].
 
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#* On the left panel of the window, click on the link to [http://www.yeastract.com/formrankbytf.php ''Rank by TF''].
 
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#* Paste your list of genes from your cluster into the box labeled ''ORFs/Genes''.
 
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#* Check the box for ''Check for all TFs''.
 
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#* Accept the defaults for the Regulations Filter (Documented, DNA binding plus expression evidence)
 
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#* Do '''''not''''' apply a filter for "Filter Documented Regulations by environmental condition".
 
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#* Rank genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
 
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#* Click the ''Search'' button.
 
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# Answer the following questions:
 
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#* In the results window that appears, the p values colored green are considered "significant", the ones colored yellow are considered "borderline significant" and the ones colored pink are considered "not significant".  '''''How many transcription factors are green or "significant"?'''''
 
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#* '''''Copy the table of results from the web page and paste it into a new Excel workbook to preserve the results.'''''
 
−
#** '''''Upload the Excel file to OWW or Box and link to it in your electronic lab notebook.'''''
 
−
#** '''''Are GLN3 or HAP4 on the list?  If so, what is their "% in user set", "% in YEASTRACT", and "p value".'''''
 
−
# For the mathematical model that we will build, we need to define a ''gene regulatory network'' of transcription factors that regulate other transcription factors.  We can use YEASTRACT to assist us with creating the network.  We want to generate a network with approximately 15-30 transcription factors in it. 
 
−
#* You need to select from this list of "significant" transcription factors, which ones you will use to run the model.  You will use these transcription factors and add GLN3 and HAP4 if they are not in your list.  Explain in your electronic notebook how you decided on which transcription factors to include.  Record the list and your justification in your electronic lab notebook.
 
−
#* Go back to the YEASTRACT database and follow the link to ''[http://www.yeastract.com/formgenerateregulationmatrix.php Generate Regulation Matrix]''.
 
−
#* Copy and paste the list of transcription factors you identified (plus HAP4 and GLN3) into both the "Transcription factors" field and the "Target ORF/Genes" field.
 
−
#* We are going to use the "Regulations Filter" options of "Documented", "'''Only''' DNA binding evidence"
 
−
#** Click the "Generate" button.
 
−
#** In the results window that appears, click on the link to the "Regulation matrix (Semicolon Separated Values (CSV) file)" that appears and save it to your Desktop.  Rename this file with a meaningful name so that you can distinguish it from the other files you will generate.
 
−
 
 
−
<!--In the future, look at their networks to make sure that their TF of interest is being regulated by at least one other factor and regulates at least one factor.  They may need to fiddle around with this to find a network that does this.  Also, have them upload their Excel spreadsheets to the wiki, not just figures in PowerPoint.-->
 
  
 +
''While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.''
  
−
===Visualizing Your Gene Regulatory Networks with GRNsight===
+
[[User:Kwrigh35|Kwrigh35]] ([[User talk:Kwrigh35|talk]]) 16:54, 2 November 2017 (PDT)
  
−
We will analyze the regulatory matrix files you generated above in Microsoft Excel and visualize them using GRNsight to determine which one will be appropriate to pursue further in the modeling.
 
−
# First we need to properly format the output files from YEASTRACT.  You will repeat these steps for each of the three files you generated above.
 
−
#*  Open the file in Excel.  It will not open properly in Excel because a semicolon was used as the column delimiter instead of a comma.  To fix this, Select the entire Column A.  Then go to the "Data" tab and select "Text to columns".  In the Wizard that appears, select "Delimited" and click "Next".  In the next window, select "Semicolon", and click "Next".  In the next window, leave the data format at "General", and click "Finish".  This should now look like a table with the names of the transcription factors across the top and down the first column and all of the zeros and ones distributed throughout the rows and columns.  This is called an "adjacency matrix."  If there is a "1" in the cell, that means there is a connection between the trancription factor in that row with that column.
 
−
#* Save this file in Microsoft Excel workbook format (.xlsx).
 
−
#* Check to see that all of the transcription factors in the matrix are connected to at least one of the other transcription factors by making sure that there is at least one "1" in a row or column for that transcription factor.  If a factor is not connected to any other factor, delete its row and column from the matrix.  Make sure that you still have somewhere between 15 and 30 transcription factors in your network after this pruning.
 
−
#** Only delete the transcription factor if there are all zeros in its column '''AND''' all zeros in its row.  You may find visualizing the matrix in GRNsight (below) can help you find these easily.
 
−
#* For this adjacency matrix to be usable in GRNmap (the modeling software) and GRNsight (the visualization software), we need to transpose the matrix.  Insert a new worksheet into your Excel file and name it "network".  Go back to the previous sheet and select the entire matrix and copy it.  Go to you new worksheet and click on the A1 cell in the upper left.  Select "Paste special" from the "Home" tab.  In the window that appears, check the box for "Transpose".  This will paste your data with the columns transposed to rows and vice versa.  This is necessary because we want the transcription factors that are the "regulatORS" across the top and the "regulatEES" along the side.
 
−
#* The labels for the genes in the columns and rows need to match. Thus, delete the "p" from each of the gene names in the columns.  Adjust the case of the labels to make them all upper case.
 
−
#* In cell A1, copy and paste the text "rows genes affected/cols genes controlling".
 
−
#* Finally, for ease of working with the adjacency matrix in Excel, we want to alphabatize the gene labels both across the top and side.
 
−
#** Select the area of the entire adjacency matrix.
 
−
#** Click the Data tab and click the custom sort button.
 
−
#** Sort Column A alphabetically, being sure to exclude the header row.
 
−
#** Now sort row 1 from left to right, excluding cell A1.  In the Custom Sort window, click on the options button and select sort left to right, excluding column 1.
 
−
#* Name the worksheet containing your organized adjacency matrix "network" and Save.
 
−
# Now we will visualize what these gene regulatory networks look like with the GRNsight software.
 
−
#* Go to the [http://dondi.github.io/GRNsight/ GRNsight] home page.
 
−
#* Select the menu item File > Open and select the regulation matrix .xlsx file that has the "network" worksheet in it that you formatted above.  If the file has been formatted properly, GRNsight should automatically create a graph of your network.  Move the nodes (genes) around until you get a layout that you like and take a screenshot of the results.  Paste it into your PowerPoint presentation.
 
−
 
−
==Acknowledgements==
 
 
==References==
 
==References==
 
LMU BioDB 2017. (2017). Week 10. Retrieved October 17, 2017, from [[Week 10]]
 
LMU BioDB 2017. (2017). Week 10. Retrieved October 17, 2017, from [[Week 10]]

Latest revision as of 00:12, 3 November 2017

Deliverables

STEM Results including tab-delimited files and ppt with results.

Updated Excel Workbook including STEM page.

Electronic Notebook: Clustering and GO Term Enrichment with stem

Prepare your microarray data file for loading into STEM

  • Download your Excel workbook that you used for your Week 8 assignment.
  • Insert a new worksheet into your Excel workbook, and name it "(STRAIN)_stem".
  • Select all of the data from your "(STRAIN)_ANOVA" worksheet and Paste special > paste values into your "(STRAIN)_stem" worksheet.
    • Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
    • Filter the data on the B-H corrected p value to be > 0.05 (that's greater than in this case).
      • Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise. Record the number of genes left in your electronic notebook. 1785 genes left
    • Delete all of the data columns EXCEPT for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
    • Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
    • Save your work. Then use Save As to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
      • Note that you should turn on the file extensions if you have not already done so.

Now download and extract the STEM software

Click here to go to the STEM web site.

  • Click on the download link, register, and download the stem.zip file to your Desktop.
  • Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item 7-zip > Extract Here.
  • This will create a folder called stem. Inside the folder, double-click on the stem.jar to launch the STEM program.

Running STEM

  • In section 1 (Expression Data Info) of the the main STEM interface window, click on the Browse... button to navigate to and select your file.
    • Click on the radio button No normalization/add 0.
    • Check the box next to Spot IDs included in the data file.
  • In section 2 (Gene Info) of the main STEM interface window, select Saccharomyces cerevisiae (SGD), from the drop-down menu for Gene Annotation Source. Select No cross references, from the Cross Reference Source drop-down menu. Select No Gene Locations from the Gene Location Source drop-down menu.
  • In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
  • In section 4 (Execute) click on the yellow Execute button to run STEM.

Viewing and Saving STEM Results

  • A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
    • Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
    • Take a screenshot of this window (on a PC, simultaneously press the Alt and PrintScreen buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
  • Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
    • Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
    • At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
      • Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
    • For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
      • Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).

Analyzing and Interpreting STEM Results

  • Select one of the profiles you saved in the previous step for further interpretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. Each member of your group should choose a different profile. Answer the following:
    • Why did you select this profile? In other words, why was it interesting to you?
      • I selected this profile because it had the lowest p-value, the most genes, and a very clear shape.
    • How many genes belong to this profile?
      • 498
    • How many genes were expected to belong to this profile?
      • 46.5
    • What is the p value for the enrichment of genes in this profile? Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
      • The p-value is 0. The p-value is most likely not actually 0, but rounded to 0 since the number was so small.
    • Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. How many GO terms are associated with this profile at p < 0.05? 263 The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. How many GO terms are associated with this profile with a corrected p value < 0.05?16
    • Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
      • Each member of the group will be reporting on his or her own cluster in your presentation next week. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
        • Note whether the same GO terms are showing up in multiple clusters.
      • Look up the definitions for each of the terms at http://geneontology.org. In your final presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor being deleted (for the Δgln3 and Δswi4 groups)?
      • To easily look up the definitions, go to http://geneontology.org.
      • Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
      • In the results page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
      • The definition will be on the next results page, e.g. here.

Acknowledgements

I'd like to thank my homework partener Antonio Porras. We worked together during class and nearly finished the assignment, with plans to text each other if we have questions.

While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.

Kwrigh35 (talk) 16:54, 2 November 2017 (PDT)

References

LMU BioDB 2017. (2017). Week 10. Retrieved October 17, 2017, from Week 10


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