BIOL478/S19:Microarray Data Analysis

Laboratory 10: Microarray Data Analysis

April 24, April 29, and May 1

Before We Begin

Viewing File Extensions

• The Windows 10 operating systems defaults to hiding file extensions. To turn them back on, do the following:

  

  Folder Options window

  1. Go to Cortana (the search function), a circle icon to the right of the start menu and search for "File Explorer Options".
  2. Open File Explorer Options in the search results.
  3. When the File Explorer Options window appears, click on the View tab.
  4. Uncheck the box for "Hide extensions for known file types".
  5. Click the OK button.
• The computers in Seaver 120 are are set to erase all custom user settings and restore the defaults once they have been restarted, so you will probably have to do this many times throughout the semester when using these computers.

Thawspace T: Drive

• The computers in Seaver 120 are set to delete all new files and settings whenever the computer is rebooted. Thus, files saved to the Desktop, Downloads or My Documents folders will not be there in the event that the computer is restarted between work sessions.
• To avoid losing your files, a special drive called the Thawspace T: drive has been set up on these computers that is immune to the file deletions upon reboot. Please create a folder using your name in the T: drive and save all of your files there.

Set Your Browser to Prompt You for the Location to Save your Downloaded Files

• In Mozilla Firefox, open the Options window.
  • Select the radio button that says "Always ask me where to save files".
  • You could also change the default "Save files to" location to the T: drive folder you created, so that will be the first choice when it prompts you where to save the file. (You will have to temporarily deselect the radio button to do this and then reselect it when you are done.
  • Click OK to save your changes.
    • Note that you may need to redo these settings if the computer has been rebooted in between work sessions.
• In Google Chrome, open the Settings window.
  • Click on the link at the bottom of the page that says "Advanced Settings".
  • Check the box that says "Ask where to save each file before downloading".
  • You could also change the default Download location to the T: drive folder you created, so that will be the first choice when it prompts you where to save the file.
  • Your settings are automatically saved.
    • Note that you may need to redo these settings if the computer has been rebooted in between work sessions.

Compressing/Decompressing Files with 7-Zip

• The 7-Zip file compression software has been installed on the computers in Seaver 120. (If you want to install this software on your own computer, go to the 7-zip Download page.) To compress a single file or a group of files, do the following:
  1. Select all of the files you want to zip together by clicking and dragging or control-clicking on the filenames.
  2. Right-click on your selection. In the context menu that appears, select the menu option: 7-Zip > Add to archive
  3. Make sure there is a meaningful filename in the field under the word "Archive:". If not, change it to something that is. Typically you will want to include your name or initials, a short descriptor of what the file contains, and the date in year-month-day format (yyyy-mm-dd).
  4. Select "zip" as the Archive format.
  5. You do not need to change any of the other defaults. Click OK. The zip file will appear in the same folder as the files you compressed.
• You must decompress a zipped file before using it. Windows Explorer will appear to open the compressed archive to show you the contents, and you may even be able to open the files from this view. However, unless they have been properly decompressed, they will not function normally. To decompress them using 7-zip, do the following:
  1. Right-click on the file icon.
  2. Choose the menu item, 7-zip > Extract Here. The files will be extracted and can be used normally afterwards.

Laboratory Notebook

• This protocol has been designed to utilize best practices from DataONE.
• The protocol itself is one form of documentation of the data manipulations that you will perform on the data.
• You will also keep an electronic lab notebook, either as a Word document or Google Doc, which you will turn in as an appendix to your final lab report. In your notebook, you will need to record the following:
  • Any changes that were made to this protocol (changes are likely because the protocol has been copied from other classes/years and there may be mistakes that we correct in class).
  • Answers to any questions that are embedded in the protocol.
  • A list of files that you created in this procedure. Include the filename, the program that created the file, and a brief (one sentence) description of the contents.
  • Any other notes that you take.
• You will also collect and turn in the files that you create as part of this protocol. Zip together all of the files that you create and upload them to your folder within the "BIOL478 Spring 2019" shared folder on Box at the end of each work session. Make sure that your name or initials is in the filename to distinguish your work from your classmates' work.

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 are performed by the GenePix Pro software and have been done for you
4. Normalize the ratios on each microarray slide
5. Normalize the ratios for a set of slides in an experiment
   • Steps 4-5 are performed by using a script written in the R statistical programming language and have been done for you
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)
   • We will use software called STEM for clustering
9. Mapping onto biological pathways
   • We will use STEM for mapping the clusters onto Gene Ontology categories
10. Identifying regulatory transcription factors responsible for observed changes in gene expression
    • We will use the YEASTRACT database for this
    • We will visualize the resulting gene regulatory network with GRNsight

GenePix Pro Protocol (Steps 1-3)

These steps will be demonstrated for you by the instructor. The protocol is listed here so that you can see what the steps are and for documentation purposes.

• Follow this link to download the raw image files. Put them in your folder in the T: drive.
  • Right-click on the zipped file and select the menu item 7-zip > Extract here. This will extract the raw image files so that we can open them in GenePix Pro.
• Download the gene array list (GAL) file "SC4001_Yeast_MI_ReadyArray_GAL_file.gal", and put it in your folder in the T: drive.
  • To download this file, right-click on the link and select "Save link as..." from the context menu that appears.

Gridding and Generating Intensity Data

• Launch GenPix Pro 7 (select Analysis Only)
• On the right hand side of the screen, select the File icon
  • Select File > Open Images
  • Navigate to the folder containing .tif images for your chip.
  • Hold down the Control key while clicking to select both the 532 and 635 wavelength files
  • Click on the Open button
• Use the brightness and contrast sliders on the upper left hand side to adjust your image to see all of the spots
• You can use the magnifying glass icon to zoom in on an area of the image, and the magnifying glass return icon to go back to the whole chip image
  • Zoom out so that you can see the entire microarray slide on your screen
• On the right hand side of the screen, select the File icon
  • Select Load Array List
  • Select the file SC4001_Yeast_MI_ReadyArray_GAL_file.gal that you previously saved in your folder in the T: drive.
  • Click the Open button
• Using the icon that has an arrow and a square, drag the grids so that they are approximately aligned with the top block of spots
  • Click on the icon that looks like a compass, or a circle with cross-hairs, and select Find Array, Find All blocks, Align Features
    • Alternately, you can do this in three steps by:
      • Click on the icon that looks like a compass, or a circle with cross-hairs, and select Find Array
      • Click on the icon again, and select Find All Blocks
      • Click on the icon again, and select Align Features in All Blocks
• On the right hand side of the screen, select the icon that looks like a table of data (it says "DATA" at the top of it)
• At this point, you would click on the File icon on the right hand side and select, Save Results As, and save your results as type "GenePix Pro Files (*.gpr)", along with saving a JPEG image containing all analyzed features. However, since the Seaver 120 computers do not have the license dongle, the software won't save the results. Dr. Dahlquist has saved them for you and posted them on Brightspace instead.

Generating an Array Quality Report

• Launch GenePix Pro 7 (select Analysis Only)
• On the right hand side of the screen, select the File icon
• File > Open Results
• Select the desired file
• Select Report tab at the top of the screen
• On the left side of the screen, under Navigate, select the back arrow or home icon
• Select Array Quality Control
• Adjust Vital Statistic Thresholds values to:

Quality Control Limits
Median signal to background > 2.5
Mean of Median background < 500
Median Signal to Noise	> 4
Median % >B + 1 StdDev	> 90
Feature variation < 0.5
Background Variation < 1.2
Features with Saturated pixels	< 3.3%
Not Found < 18%
Bad Features < 7%

• Select Start
• Select Show Printable Version
• File > Print
• Under Select Printer > Select Adobe PDF
• Select Print
• Save in your folder in the T: drive
• Name the file: ChipBarcode#_yyyymmdd
• The PDF file will either open on its own or you need to open it from the file you saved it to

Looking at the raw data

• We will look at the raw data for two genes of interest, ASH1 (which should have been deleted in this strain), and NSR1, which is a gene known to be induced by cold shock.
• Open the GenePix results file (.gpr) for your chip.
• To find the data for NSR1, first go to the NSR1 page at the Saccharomyces Gene Database to learn what the systematic name (ID) for NSR1 is.
• Then search for this ID in your Excel spreadsheet
• This will tell you the block, row, and column where the NSR1 spot occurs and you can look for it in GenePix Pro.
• Repeat with ASH1 (ASH1 page at the Saccharomyces Gene Database)
• We are expecting that NSR1 should be expressed and that ASH1 should not because it has been deleted from the yeast genome. Is this the case?

Statistical Analysis (Step 6)

Downloading the Data

• Since the data we collected for the Δash1 strain did not pass chip quality control, we will instead analyze the wild type (wt) data previously gathered in the Dahlquist Lab.
• You have been sent an invitation to access a folder called "BIOL478 Spring 2019" on Box. From there, download the file "BIOL478_Spring2019_master_microarray_data_wt.xlsx" into your folder in the T: drive.
• Immediately make a copy of the file with a different name which contains your name or initials to distinguish it from the master file and others in the class.

Experimental Design

In the Excel spreadsheet, there is a worksheet labled "Master_Sheet_wt". In this worksheet, each row contains the data for one gene (one spot on the microarray). The first column contains the "Master Index", which numbers all of the rows sequentially in the worksheet so that we can always use it to sort the genes into the order they were in when we started. The second column (labeled "ID") contains the gene identifier from the Saccharomyces Genome Database. The third column contains the Standard Name for each of the genes. Each subsequent column contains the log₂ ratio of the red/green fluorescence from each microarray hybridized in the experiment (steps 1-5 above having been performed for you already).

Each of the column headings from the data begin with the strain name ("wt" for wild type S. cerevisiae data). "LogFC" stands for "Log₂ Fold Change" which is the Log₂ red/green ratio. The timepoints are designated as "t" followed by a number in minutes. Replicates are numbered as "-0", "-1", "-2", etc. after the timepoint.

The timepoints are t15, t30, t60 (cold shock at 13°C) and t90 and t120 (cold shock at 13°C followed by 30 or 60 minutes of recovery at 30°C).

• Begin by recording in your electronic notebook, the the filename, the number of replicates for each strain and each time point in your data.
  • NOTE: before beginning any analysis, immediately change the filename so that it contains your initials to distinguish it from other students' work.

Modified t test for each timepoint

We will perform a modified t test to determine if any genes had a gene expression change that was significantly different than zero at each timepoint.

• Insert a new worksheet into your Excel workbook and name it "wt_ttest".
• Go back to the "Master_Sheet_wt" worksheet. Copy the entire sheet and paste it into your new worksheet.
• Go to the empty columns to the right on your worksheet. Create new column headings in the top cells to label the average log fold changes that you will compute. Name them with the pattern wt_<AvgLogFC>_<tx> where you use the appropriate text within the <> and where x is the time. For example, "wt_AvgLogFC_t15".
• Compute the average log fold change for the replicates for each timepoint by typing the equation:

=AVERAGE(range of cells in the row for that timepoint)

into the second cell below the column heading. For example, your equation might read

=AVERAGE(D2:G2)

Copy this equation and paste it into the rest of the column. A quick way to do this is to click on this cell and position your cursor at the bottom right corner. You should see your cursor change to a thin black plus sign (not a chubby white one). When it does, double click, and the formula will magically be copied to the entire column of 6188 other genes. Note that you should check to make sure that this really worked.

• Create the equation for the rest of the timepoints and paste it into their respective columns. Note that you can save yourself some time by completing the first equation for all of the averages and then copy and paste all the columns at once.
• Go to the empty columns to the right on your worksheet. Create new column headings in the top cells to label the T statistic that you will compute. Name them with the pattern <wt>_<Tstat>_<tx> where you use the appropriate text within the <> and where x is the time. For example, "wt_Tstat_t15". You will now compute a T statistic that tells you whether the normalized average log fold change is significantly different than 0 (no change in expression). Enter the equation into the second cell below the column heading:

=AVERAGE(range of cells)/(STDEV(range of cells)/SQRT(number of replicates))

For example, your equation might read:

=AVERAGE(D2:G2)/(STDEV(D2:G2)/SQRT((COUNTA(D2:G2))))

NOTE: in this case the number of replicates is 4. However, we are using the COUNTA function because it counts the number of cells in the specified range that have data in them (i.e., does not count cells with missing values). Be careful that you are using the correct number of parentheses. Copy the equation and paste it into all rows in that column. Create the equation for the rest of the timepoints and paste it into their respective columns. Note that you can save yourself some time by completing the first equation for all of the T statistics and then copy and paste all the columns at once.

• Go to the empty columns to the right on your worksheet. Create new column headings in the top cells to label the P value that you will compute. Name them with the pattern <wt>_<Pval>_<tx> where you use the appropriate text within the <> and where x is the time. For example, "wt_Pval_t15". In the cell below the label, enter the equation:

=TDIST(ABS(cell containing T statistic),degrees of freedom,2)

For example, your equation might read:

=TDIST(ABS(AF2),(COUNTA(D2:G2)-1),2)

The number of degrees of freedom is the number of replicates minus one. Again, we are using the COUNTA function to count this for us. Copy the equation and paste it into all rows in that column.

• Now we will perform adjustments to the p value to correct for the multiple testing problem.

Bonferroni Correction

1. Now we will perform adjustments to the p value to correct for the multiple testing problem. Label the columns to the right with the label, wt_Bonferroni-Pval_tx (do this twice in a row).
2. Type the equation =<(STRAIN)_Pval_tx>*6189, Upon completion of this single computation, use the trick to copy the formula throughout the column.
3. Replace any corrected p value that is greater than 1 by the number 1 by typing the following formula into the first cell below the second wt_Bonferroni-Pval_tx header: =IF([cell with p value]>1,1,[cell with p value]). Use the trick to copy the formula throughout the column.

Benjamini & Hochberg Correction

1. Insert a new worksheet named "wt_ttest_B-H". You will need to perform the procedure below for the p values for each timepoint. Do them individually one at a time to avoid confusion.
2. Copy and paste the "MasterIndex", "ID", and "Standard Name" columns from your previous worksheet into the first two columns of the new worksheet.
3. For the following, use Paste special > Paste values. Copy your unadjusted p values from the first timepoint from your ttest worksheet and paste it into Column D.
4. Select all of columns A, B, C, and D. Sort by ascending values on Column D. Click the sort button from A to Z on the toolbar, in the window that appears, sort by column D, smallest to largest.
5. Type the header "Rank" in cell E1. We will create a series of numbers in ascending order from 1 to 6189 in this column. This is the p value rank, smallest to largest. Type "1" into cell E2 and "2" into cell E3. Select both cells E2 and E3. Double-click on the plus sign on the lower right-hand corner of your selection to fill the column with a series of numbers from 1 to 6189.
6. Now you can calculate the Benjamini and Hochberg p value correction. Type (STRAIN)_B-H_Pval_tx in cell F1. Type the following formula in cell F2: =(D2*6189)/E2 and press enter. Copy that equation to the entire column.
7. Type "STRAIN_B-H_Pval_tx" into cell G1.
8. Type the following formula into cell G2: =IF(F2>1,1,F2) and press enter. Copy that equation to the entire column.
9. Select columns A through G. Now sort them by your MasterIndex in Column A in ascending order.
10. Copy column G and use Paste special > Paste values to paste it into the next column on the right of your ttest sheet.

• Save your file to your flash drive and Box. Give Dr. Dahlquist access to the file.

Sanity Check: Number of genes significantly changed

We will now perform a "sanity check" on the analysis we have conducted so far. We are checking our work to see how many genes actually changed their expression in our experiment. Checking these numbers against each other will reveal if any mistakes were made in the statistical analysis. Checking this against what our expectations would be tells us whether there were any problems with the experiment itself.

• We will perform the "sanity check" as follows. Create a new worksheet called "sanity_check" to record your results (and compute percentages).
• Go to your wt_ttest worksheet.
• Select row 1 (the row with your column headers) and select the menu item Data > Filter > Autofilter (The funnel icon on the Data tab). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
• Click on the drop-down arrow for the unadjusted p value for t60. Set a criterion that will filter your data so that the p value has to be less than 0.05.
  • How many genes have p < 0.05? and what is the percentage (out of 6189)?
  • How many genes have p < 0.01? and what is the percentage (out of 6189)?
  • How many genes have p < 0.001? and what is the percentage (out of 6189)?
  • How many genes have p < 0.0001? and what is the percentage (out of 6189)?
    • What is the expected number and percentage of genes we should see at each of these p value cut-offs? What is the relationship of our results to the expectations?
• Determine how many genes have a p value < 0.05 at each of the other timepoints (t15, t30, t90, t120).
• When we use a p value cut-off of p < 0.05, what we are saying is that you would have seen a gene expression change that deviates this far from zero by chance less than 5% of the time.
• We have just performed 6189 hypothesis tests. Another way to state what we are seeing with p < 0.05 is that we would expect to see this a gene expression change for at least one of the timepoints by chance in about 5% of our tests, or 309 times. Since we have more than 309 genes that pass this cut off, we know that some genes are significantly changed. However, we don't know which ones. To apply a more stringent criterion to our p values, we perform the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values. The Bonferroni correction is very stringent. The Benjamini-Hochberg correction is less stringent. To see this relationship, filter your data to determine the following:
  • How many genes are p < 0.05 for the Bonferroni-corrected p value at each timepoint? and what is the percentage (out of 6189)?
    • Which unadjusted p value cut-off is the t60 Bonferroni-corrected p value most similar to?
  • How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value at each timepoint? and what is the percentage (out of 6189)?
    • Which unadjusted p value cut-off is the t60 Benjamini and Hochberg-corrected p value most similar to?
• In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant". Instead, it is a moveable confidence level. If we want to be very confident of our data, use a small p value cut-off. If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off. For a humorous take on this, see https://xkcd.com/1478/.
• There is one last thing to do: keeping the "Pval" filter at p < 0.05, How many have an average log fold change of > 0.25 and p < 0.05 at each timepoint? How many have an average log fold change of < -0.25 and p < 0.05 at each timepoint? (These log fold change cut-offs represent about a 20% fold change in expression.) This tells us how many genes have expression that is significantly increased or decreased at each timepoint.
• Use slide 2 of this sample PowerPoint slide to see how your table should be formatted.

Comparing results with known data (step 7)

• The expression of the gene NSR1 (ID: YGR159C)is known to be induced by cold shock. Find NSR1 in your dataset. What is its average Log fold change at each of the timepoints in the experiment? What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values?

Clustering and GO Term Enrichment with stem (steps 8-9)

1. Prepare your microarray data file for loading into STEM.
   • Insert a new worksheet into your Excel workbook, and name it "wt_stem".
   • Select all of the data from your "wt_ttest" worksheet and Paste special > paste values into your "wt_stem" worksheet.
     • Your leftmost column should have the column header "MasterIndex". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "StandardName".
     • You are now going to filter the data in such a way so that we can remove the "least significant" genes from the clustering analysis (if we keep them, the program will just be clustering noise.) Filter the data on the p value for t15 to be > 0.1 (that's greater than in this case). Then filter the p value for t30, t60, t90, and t120 to be > 0.1, without removing the filter from t15. This will select all genes that did not have a significant change in expression any any timepoint in the experiment.
       • 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.
     • Back in the "wt_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.).
     • 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 the wt_stem worksheet as Text (Tab-delimited) (*.txt). Include "wt" and "stem" in the filename to distinguish it from your other file. Click OK to the warnings.
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 folder in the T: drive.
   • 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 the program will not work properly if you do not do this step.
   • 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.
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 folder in the T: drive. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
        • Save these files to your flash drive and Box. Give Dr. Dahlquist access to the file. (It will be easier to zip all the files together and store 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 folder in the T: drive. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt" 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!
        • Save these files to your flash drive and Box. Give Dr. Dahlquist access to the file. (It will be easier to zip all the files together and store them as one file).
5. Analyzing and Interpreting STEM Results
   1. Each person in the class will select one profile for further analysis. 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 for each of the strains? 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. The p value reported by stem 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 10 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05) that you will present and analyze in your final report.
        • Create a table for your final report with just those 10 terms. Your table should include the following data from the GO list file:
          • Category ID
          • Category Name
          • #Genes Category
          • #Genes Assigned
          • #Genes Expected
          • #Genes Enriched
          • p-value
          • Corrected p-value
          • Fold
        • Look up the definitions for each of the terms at http://geneontology.org. For your final lab report, you will supply the definition and 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?
        • 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.

Identifying regulatory transcription factors responsible for observed changes in gene expression (step 10)

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 that you saved from stem in Excel for the cluster that you chose to analyze further.
   • 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. You will include the top 15 regulatory transcription factors in a table in your final report.
     • Is ASH1 on the list? If so, what is its "% in user set", "% in YEASTRACT", and "p value".
4. We will now define a gene regulatory network of transcription factors that regulate this cluster. We can use YEASTRACT to assist us with creating the network.
   • Select the "significant" transcription factors from your YEASTRACT results. You will use these transcription factors and add ASH1 if it is not in your list. Explain in your notebook how you decided on which transcription factors to include. Record the list and your justification in your 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 ASH1) 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 folder in the T: drive. Rename this file with a meaningful name so that you can distinguish it from the other files you will generate.

Visualizing Your Gene Regulatory Network with GRNsight

We will analyze the regulatory matrix files you generated above in Microsoft Excel and visualize them using GRNsight.

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).
   • 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 (or use the grid layout option) and take a screenshot of the results. Paste it into your PowerPoint presentation.

Summary list of files you need to turn in to your folder in Box

1. The .xlsx file that contains your statistical analysis (step 6)
2. The .txt file that you input into the stem software (steps 8 & 9)
3. Each of the gene list and GO list files generated by stem, zipped together (steps 8 & 9)
4. an .xlsx file containing the YEASTRACT results from "rank by TF" (step 10)
5. an .xlsx file formatted for input into GRNsight (step 10)
6. A .pptx file that contains the following slides:
   • a table of p values (step 6)
   • screenshot of the main stem results (steps 8 & 9)
   • individual screenshots of the individual stem profiles steps 8 & 9)
   • screenshot of your candidate gene regulatory network from GRNsight (step 10)