View source for Week 9 Individual Journal

==Purpose==
==Methods/Results==
=== Clustering and GO Term Enrichment with stem ===

''Note that this section is continued from [[Week 8]]; the entire section is included even though you have completed some of it already.''

# '''Prepare your microarray data file for loading into STEM.'''
#* 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.
#** 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.'''  [http://www.cs.cmu.edu/~jernst/stem/ Click here to go to the STEM web site].
#* Click on the [http://www.sb.cs.cmu.edu/stem/stem.zip download link] 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>.
#** You now need to download the Gene Ontology and yeast GO annotations and place them in this folder.
#** Click here to download the file [https://lmu.box.com/s/t8i5s1z1munrcfxzzs7nv7q2edsktxgl "gene_ontology.obo"].
#** Click here to download the file [https://lmu.box.com/s/zlr1s8fjogfssa1wl59d5shyybtm1d49 "gene_association.sgd.gz"].
#*Inside the folder, double-click on the <code>stem.jar</code> 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, leave the default selection for the three drop-down menu selections for Gene Annotation Source, Cross Reference Source, and Gene Location Source as "User provided".
## Click the "Browse..." button to the right of the "Gene Annotation File" item.  Browse to your "stem" folder and select the file "gene_association.sgd.gz" and click Open.
## 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.
##* If you get an error, there are some known reasons why stem might not work.  If you had #DIV/0! errors in your input file, it will cause problems. Re-open your file and open the Find/Replace dialog.  Search for #DIV/0!, but don't put anything in the replace field.  Click "Replace all" to remove the #DIV/0! errors.  Then save your file and try again with stem.
##* '''''This is the stopping point for the [[Week 8]] assignment and the beginning point for Week 9'''''
# '''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 [[Week_4#Compressing_and_Decompressing_Files_with_7-Zip | 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 [[Week_4#Compressing_and_Decompressing_Files_with_7-Zip | 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:
====1. 'Why did you select this profile?  In other words, why was it interesting to you?====
*This profile [Profile 9] was interesting to me because of it's sporadic pattern of up-down regulation as well as it having a high number of assigned genes.

====2. How many genes belong to this profile?====
*118 genes belong to this specific profile.

====3. How many genes were expected to belong to this profile?====
*34.1 genes were expected to be seen to have this specific profile

====4. What is the p value for the enrichment of genes in this profile?====
*The p-value for the enrichment of genes in this profile is 1.3E-30, which is considered significant.


*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.

====5. 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 research presentation.  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 research 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 groups working with deletion strain data)?'''''
##** 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 on the left of the page.
##** 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 (Tuesday, October 29)====

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.

# 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.
#* Copy the list of gene IDs onto your clipboard.
# Launch a web browser and go to the [http://www.yeastract.com/ YEASTRACT database].
#* On the left panel of the window, click on the link to [http://www.yeastract.com/formrankbytf.php ''Rank by TF''].
#* Paste your list of genes from your cluster into the box labeled ''ORFs/Genes''.
#* Check the box for ''Check for all TFs''.
#* Accept the defaults for the Regulations Filter (Documented, DNA binding plus expression evidence)
#* Do '''''not''''' apply a filter for "Filter Documented Regulations by environmental condition".
#* Rank genes by TF using: The % of genes in the list and in YEASTRACT regulated by each TF.
#* Click the ''Search'' button.
# Answer the following questions:
#* 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"?''''' : 3, 6,174.
#* '''''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 the wiki and link to it in your electronic lab notebook.'''''
#** '''''Are CIN5:17.95%, 0.96%, 0,999, GLN3 36.75%, 1.78%, 0.215224764, and/or HAP4 23.08%, 2.47%, 0.008910675 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 15-20 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, HAP4, and CIN5 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.  Each group member will select a different network (they can have some overlapping transcription factors, but some should also be different).
#* 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, GLN3, and CIN5) 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.

==== Visualizing Your Gene Regulatory Networks with GRNsight ====

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.
#*  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.  You can click the "Grid Layout" button to arrange the nodes in a grid, or you can click and drag 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.
#** If you have nodes (genes) floating around in the display that are not connected to any other nodes, we need to delete them from the network for the modeling to work properly.  Go back to the Excel workbook and network sheet and delete both the row and column with the floating gene's name.  Then re-upload the edited file to GRNsight to visualize it.  Use this final version in your PowerPoint and subsequent modeling.



==Data and Files==
*[[Media: DGLN3_STEM_Profiles_JNM.pptx | Powerpoint Slideshow of the STEM profile Sceenshots]]
*[[Media: DGLN3_genelist_JNM.zip | dGLN3 Genelist Files from STEM]]
*[[Media: DGLN3_GOlist_JNM.zip ‎ | dGLN3_GOlist Files from STEM]]
*[[Media: DGLN3_profile9_GOlist_JNM.xlsx| Excel sheet of Profile 9]]
*[[Media: BIOL367_F19_Jnimmers_p-value_slide.pptx | Powerpoint of all relavant figures (P-Value Table and GRNsight)]]

==Conclusion==
==Acknowledgements==
==References==

{{jnimmers}}