Week 9

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This journal entry is due on Thursday, October 31, at 12:01am Pacific time.

Note that you have an interim deadline of Tuesday, October 29, at 12:01am Pacific time, the content of which will be determined in class on Thursday; see stopping point below.

Objectives

The purpose of this assignment is:

  • to conduct the "analyze" step of the data life cycle for a DNA microarray dataset.
  • to "think like a cell" to interpret the clusters and associated Gene Ontology terms.
  • to develop an intuition about gene regulatory networks.
  • to keep a detailed electronic laboratory notebook to facilitate reproducible research.

Individual Journal Assignment

  • Store this journal entry as "username Week 9" (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.
  • The sections you need for this week are Purpose, Methods/Results (one combined section), Data & Files, Conclusion, Acknowledgments, and References (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 performing a series of computations on a microarray dataset, primarily using Microsoft Excel. 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 in a "Data and Files" section.
    • You will write a summary paragraph that gives the conclusions from this week's analysis.

Homework Partners

You will work in groups of three or four for the next two weeks of assignments. You will keep the same group for this week as you did last week. Please sit next to your group members in class. You will be expected to consult with your group members, in order to complete the assignment. However, unless otherwise stated, each partner must submit his or her own work as the individual journal entry (direct copies of each other's work is not allowed). Homework partners for this week are:

Microarray Data Analysis

We will be working on the protocols in class on Thursday, October 24 and Tuesday, October 29. Based on the progress that is made during class, the milestone of what needs to be completed by the Week 9 journal deadline will be announced in class. We will conclude this protocol in Week 10.

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
  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
  10. Identifying regulatory transcription factors responsible for observed changes in gene expression (YEASTRACT)
  11. Dynamical systems modeling of the gene regulatory network (GRNmap)
  12. Viewing modeling results in GRNsight

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.

  1. 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.
  2. Now download and extract the STEM software. Click here to go to the STEM web site.
    • Click on the download link 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, 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".
    3. 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.
    4. 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.
    5. 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
  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 the top 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
          • 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 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.
        • Copy and paste the GO ID (e.g. GO:0044848) into the search field on the left of the page.
        • 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 interim deadline of 12:01am, Tuesday October 29.

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.

  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 the wiki and link to it in your electronic lab notebook.
      • Are CIN5, GLN3, and/or HAP4 on the list? If so, what is their "% in user set", "% in YEASTRACT", and "p value".
  4. 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 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.

  1. 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).
    • 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. 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.

Creating the GRNmap Input Workbook

TBD

Data and Files

Your data and files section should include:

  • Your Excel workbook with all of your calculations (you may have had to make corrections since the last version).
    • Note that you will be working with this workbook for the next week or two, adding computations to it. Save the new versions to the wiki with the same filename. The wiki will store each version of the file so you can always go back to a previous version, if need be.
  • Your PowerPoint slide with a summary table of p values, updated with the screenshots from the stem software and the GRNsight network.
    • You will also be adding to the PowerPoint presentation during subsequent steps in the analysis.
  • The input .txt file that you used to run stem.
  • The file that you uploaded to GRNsight.
  • The zipped together genelist and GOlist files for each of your significant profiles.

Conclusion (Summary Paragraph)

  • Write a summary paragraph that gives the conclusions from this week's analysis.

Shared Journal Assignment

  • Store your journal entry in the shared Class Journal Week 9 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 7-8 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 Dr. Dahlquist based on balancing areas of expertise amongst the team members. However, I will also be gathering your input into the formation of the teams. In class on Thursday, October 31, you will confidentially fill out a card giving me the names of three other people in the class whom you want to be on your team (you are allowed 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, please reflect upon working in teams either in this class or in previous classes. Reading each others's reflections may also give you some insight into whom you want on your team.

  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?
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