Cazinge Week 10

Quantitate the fluorescence signal in each spot
Calculate the ratio of red/green fluorescence
Log₂ transform the ratios
- Steps 1-3 have been performed for you by the GenePix Pro software (which runs the microarray scanner).
Normalize the ratios on each microarray slide
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:
Perform statistical analysis on the ratios
Compare individual genes with known data
- Steps 6-7 are performed in Microsoft Excel
Pattern finding algorithms (clustering)
Map onto biological pathways
- We will use software called STEM for the clustering and mapping
Identifying regulatory transcription factors responsible for observed changes in gene expression
Dynamical systems modeling of the gene regulatory network (GRNmap)
Viewing modeling results in GRNsight

Clustering and GO Term Enrichment with stem

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 "wt_stem".
- Select all of the data from your "wt_ANOVA" worksheet and Paste special > paste values into your "wt_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.
      - ANSWER: 2006 genes.
  - Delete all of the data columns EXCEPT for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
  - Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
  - Save your work. Then use Save As to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
    - Note that you should turn on the file extensions if you have not already done so.
Now download and extract the STEM software. 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.
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.
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).
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?
    - ANSWER: I chose profile 22 because the graph looked interesting, as for the majority of its included genes went from a neutral state to experiencing a positive expression change from the cold shock.
  - How many genes belong to this profile?
    - ANSWER: 284.0
  - How many genes were expected to belong to this profile?
    - ANSWER: 29.0
  - 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.
    - ANSWER: 5.9E-181 (significant)
  - Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. 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?
    - ANSWER: 263 (35.25%) GO terms are associated with profile 22 at p < 0.05. 14 (1.88%) GO terms are associated with profile 22 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.
      - 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 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 being deleted (for the Δgln3 and Δswi4 groups)?
      - ANSWER:
        cytoplasm: Genes associated with cytoplasm are expressed when cold shock occurs as the cell undergoes a period of stress, due to the cytoplasm's need for more energy.
        
        carbohydrate derivative metabolic process: During cold shock, the cell attempts to produce carbohydrates for oxidation, thus expressing genes of this category.
        
        nucleotide metabolic process: When the cell experiences a period of stress due to cold shock, it attempts to generate energy via nucleotide metabolic processes, causing it to express genes associated with this category.
        
        perioxidase activity: During cold shock, the cell attempts oxidation, causing genes associated with perioxidase activity to become expressed.
        
        cellular response to toxic substance: A cell in cold shock exhibits similar characteristics as cells responding to toxic substances, causing genes of the latter category to be expressed.
        
        organophosphate metabolic process: During cold shock, the cell attempts to produce phosphate for oxidation, thus expressing genes of this category.
    - 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.

Open the gene list in Excel for the one of the significant profiles from your stem analysis. Choose a cluster with a clear cold shock/recovery up/down or down/up pattern. You should also choose one of the largest clusters.
- Copy the list of gene IDs onto your clipboard.
Launch a web browser and go to the 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.
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).
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.

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.
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.

Conclusion

In this experiment, we (almost) finished analyzing the DNA microarray dataset from Dahlquist's wetlab via a STEM analysis of the data. After pruning our data set solely to the p values and AvgLogFC's per timepoint for each gene, we ran our data through the STEM software provided to us. This produced a number of profiles comprised of genes expression curves and associated genes which had similar shapes to one another. After formatting and uploading this data to the wiki, I filtered the data in profile 22, taking only those that had corrected p values only .05, and found explanations as to why the genes in 6 of the profile's categories became expressed during cold shock. Due to time constraints, this is where the Week 10 assignment ends.

Acknowledgements

Met outside of class with Emma Tyrnauer to discuss any questions we had prior to meeting and throughout the process of completing the Week 10 assignment.

While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.

Cazinge (talk) 15:43, 6 November 2017 (PST)

References

LMU BioDB 2017. (2017). Week 10. Retrieved November 3, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10
Gene Ontology Consortium. (2017). Retrieved November 3, 2017, from http://geneontology.org