Week 10

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Objectives

The purpose of this assignment is:

• to conduct the "analyze" step of the data life cycle for a DNA microarray dataset.
• to conduct "high level" analysis, including clustering and analysis of gene regulatory networks (GRNs)
• to keep a detailed electronic laboratory notebook to facilitate reproducible research.

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 personal template that you created for the Week 1 Assignment on your individual journal entry page. Your template should provide the following set of navigation links:
  • Link to your user page.
  • Links to the weekly Assignment pages.
  • Links to your weekly Individual Journal entry pages.
  • Links to the weekly Class Journal pages.
  • The category "Journal Entry".
• 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, using the Microsoft Excel, stem, GRNmap, and GRNsight software. 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

The homework partners for this week are the same as for the Week 9 assignment. Please sit next to your partner in class. You will be expected to consult with your partner, 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:

• Katie & Dean ( wild type data)
• Hailey & Natalija ( Δcin5 data)
• Charlotte & Andrew ( Δgln3 data)

Preliminary Tasks

• If necessary, follow the instructions on the Week 8 page to turn on file extensions, control where the browser downloads files, and decompress files.

Microarray Data Analysis

Overview

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 were 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
   • Completed in Excel as part of the Week 9 assignment
8. Pattern finding algorithms (clustering)
9. Map onto biological pathways
   • We will use software called STEM for the clustering and mapping
   • Ernst, J., & Bar-Joseph, Z. (2006). STEM: a tool for the analysis of short time series gene expression data. BMC bioinformatics, 7(1), 191. DOI: 10.1093/bioinformatics/bti1022
10. Identifying regulatory transcription factors responsible for observed changes in gene expression (YEASTRACT)
    • Teixeira, M. C., Viana, R., Palma, M., Oliveira, J., Galocha, M., Mota, M. N., ... & Monteiro, P. T. (2023). YEASTRACT+: a portal for the exploitation of global transcription regulation and metabolic model data in yeast biotechnology and pathogenesis. Nucleic Acids Research, 51(D1), D785-D791. DOI: 10.1093/nar/gkac1041
11. Dynamical systems modeling of the gene regulatory network (GRNmap)
    • Dahlquist, K. D., Fitzpatrick, B. G., Camacho, E. T., Entzminger, S. D., & Wanner, N. C. (2015). Parameter estimation for gene regulatory networks from microarray data: cold shock response in Saccharomyces cerevisiae. Bulletin of mathematical biology, 77(8), 1457-1492. DOI: 10.1007/s11538-015-0092-6
12. Viewing modeling results in GRNsight
    • Dahlquist, K. D., Dionisio, J. D. N., Fitzpatrick, B. G., Anguiano, N. A., Varshneya, A., Southwick, B. J., & Samdarshi, M. (2016). GRNsight: a web application and service for visualizing models of small-to medium-scale gene regulatory networks. PeerJ Computer Science, 2, e85. DOI: 10.7717/peerj-cs.85

Clustering and GO Term Enrichment with stem

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.
   • Extract the file by right-clicking on it and selecting "Extract all" from the menu.
   • This will create a folder called stem.
     • You now need to download the Gene Ontology and yeast GO annotations and place them in this folder.
     • Click here to download the file "gene_ontology.obo".
     • Click here to download the file "gene_association.sgd.gz".
   • 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.
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 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. In your journal entry, 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.

Data and Files

Your data and files section should include:

• Your Excel workbook with all of your calculations.
  • 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.
  • 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 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.