Week 10

This journal entry is due on Tuesday, November 7, at 12:01 AM PDT.

Objectives

The purpose of this assignment is:

• to perform "high level" analysis on microarray data in preparation for the final research project.

Individual Journal Assignment

• Store this journal entry as "username Week 10" (i.e., this is the text to place between the square brackets when you link to this page).
• Invoke your template on your journal entry page so that you:
  • Link from your journal entry page to this Assignment page.
  • Link from your journal entry to your user page.
  • Add the "Journal Entry" category to the end of your wiki page.
• Because you have invoked your template on your user page, you should also have a:
  • Link from your user page to this Assignment page.
  • Link to your journal entry from your user page.
• Include both the Acknowledgments and References section as specified by the Week 1 assignment.
• For your assignment this week, the electronic laboratory notebook you will keep on your individual wiki page is crucial. An electronic laboratory notebook records all the manipulations you perform on the data and the answers to the questions throughout the protocol. Like a paper lab notebook found in a wet lab, it should contain enough information so that you or someone else could reproduce what you did using only the information from the notebook.
  • We will be building upon the Week 8 microarray data analsyis, performing additional "high level" analyses. In the interests of reproducible research, it is appropriate to copy and paste the methods from this assignment into your individual journal entry.
  • You must then modify the general instructions (which are generic to the whole class) to your own data analysis, recording the specific modifications and equations that you used on your dataset.
  • Record the answers to the questions posed in the protocol at the place in which they appear in the method. You do not need to separate them out in a different results section.
  • All files generated in the protocol must be uploaded to the wiki and linked to from your journal entry page.
  • You will write a summary paragraph that gives the conclusions from this week's analysis.

Homework Partners

Homework partners for this week are listed below. The particular dataset that you and your partner will work on is also indicated below. You are expected to consult with your partner, sharing your domain expertise, in order to complete the assignment. However, each partner must submit his or her own work as the individual journal entry (direct copies of each other's work is not allowed). You must give the details of the interaction with your partner in the Acknowledgments section of your journal assignment. Since this week's work is a continuation of the microarray data analysis you performed for the Week 8 assignment, you will return to those homework partners.

• Eddie Azinge, Emma Tyrnauer: wild type data
• Eddie Bachoura, Quinn Lanners: dASH1 data
• Mary Balducci, Simon Wroblewski: dCIN5 data
• Dina Bashoura, Zach Van Ysseldyk: dGLN3 data
• Blair Hamilton, Nicole Kalcic: dHAP4 data
• Hayden Hinsch, Arash Lari: dHMO1 data
• John Lopez, Corinne Wong: dSWI4 data
• Antonio Porras, Katie Wright: dZAP1 data

Microarray Data Analysis Part 2: "High-level Analysis"

We will be working on the protocols in class on Tuesday, October 31 and Thursday, November 2. Whatever you do not finish in class will be homework to be completed by the Week 10 journal deadline.

Background

This is a list of steps required to analyze DNA microarray data.

1. Quantitate the fluorescence signal in each spot
2. Calculate the ratio of red/green fluorescence
3. Log₂ transform the ratios
   • Steps 1-3 have been performed for you by the GenePix Pro software (which runs the microarray scanner).
4. Normalize the ratios on each microarray slide
5. Normalize the ratios for a set of slides in an experiment
   • Steps 4-5 was performed for you using a script in R, a statistics package (see: Microarray Data Analysis Workflow)
   • You will perform the following steps:
6. Perform statistical analysis on the ratios
7. Compare individual genes with known data
   • Steps 6-7 are performed in Microsoft Excel
8. Pattern finding algorithms (clustering)
9. Map onto biological pathways
   • We will use software called STEM for the clustering and mapping
10. Identifying regulatory transcription factors responsible for observed changes in gene expression
11. Dynamical systems modeling of the gene regulatory network (GRNmap)
12. Viewing modeling results in GRNsight

Clustering and GO Term Enrichment with stem

1. 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.
     • 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.
2. 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.
3. Running STEM
   1. 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.
   2. 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.
   3. 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.
   4. In section 4 (Execute) click on the yellow Execute button to run STEM.
4. Viewing and Saving STEM Results
   1. 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.
   2. 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).
5. Analyzing and Interpreting STEM Results
   1. 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.
        • 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 that was deleted from your strain?
        • 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.

This is the stopping point for the Week 10 Assignment. We will pick up the next steps in the analysis in subsequent weeks.

Using YEASTRACT to Infer which Transcription Factors Regulate a Cluster of Genes

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.

1. 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.
2. Launch a web browser and go to the YEASTRACT database.
   • On the left panel of the window, click on the link to 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.
3. 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"?
   • 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.
     • Is your transcription factor on the list? If so, what is their "% in user set", "% in YEASTRACT", and "p value". (Note that this doesn't apply to the wt strain).
4. For the mathematical model and GRNsight, 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 Generate Regulation Matrix.
   • Copy and paste the list of transcription factors you identified (plus the transcription factor deleted in your strain) 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.

1. 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.
2. Now we will visualize what these gene regulatory networks look like with the GRNsight software.
   • Go to the 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.

Summary of what you need to turn in for the individual Week 10 assignment

1. Your individual journal page should have an electronic lab notebook recording your work. This includes the detailed methods specific to your analysis, your result files, the answers to any questions posed in the protocol above, a scientific conclusion, and the acknowledgments and references sections. Don't forget your paragraph which is a biological interpretation of your stem results.
2. Upload your updated Excel spreadsheet to the wiki that has today's manipulations in it. Use the same filename as before so that the download link that you already (previous versions will still be available in the history).
3. Append the screenshots of the stem results to the PowerPoint presentation that contains the p value table that you created for the Week 8 assignments. Each slide in the presentation should have a meaningful title that describes the main message of the slide.
4. Zip together all of the tab-delimited text files that you created for and from stem and upload them to the wiki.
   • the file that was saved from your original spreadsheet that you used to run stem
   • each of the genelist and GOlist files for each of your significant profiles.
5. Write a paragraph-length conclusion for this week's exercise.

Shared Journal Assignment

• Store your journal entry in the shared Class Journal Week 10 page. If this page does not exist yet, go ahead and create it (congratulations on getting in first 👏🏼)
• Link to your journal entry from your user page.
• Link back from the journal entry to your user page.
  • NOTE: You can easily fulfill the links part of these instructions by adding them to your template and using the template on your user page.
• Sign your portion of the journal with the standard wiki signature shortcut (~~~~).
• Add the "Journal Entry" and "Shared" categories to the end of the wiki page (if someone has not already done so).

Reflect

This week marks the end of Part 2 "Going Deeper" unit of the course. At this point in the semester, you have worked with 8 other people in the class as partners on journal assignments. Next week, you will be placed on teams of four that you will stay with throughout the rest of the semester. Ultimately the teams will be assigned by Drs. Dahlquist and Dionsio based on balancing areas of expertise amongst the team members. However, we will also be gathering your input into the formation of the teams. In class on Thursday, November 2, you will confidentially fill out a card giving us the names of three other people in the class whom you want to be on your team (you can also choose people that haven't been your journal partner yet) and one person whom you prefer not to be on your team.

As we get ready for the team projects, we are asking you to reflect upon working in teams either in this class or in previous classes.

1. What kinds of characteristics do you want in your teammates, and why?
2. What kinds of things make teamwork go smoothly?
3. What kinds of things make teamwork not go so smoothly?