Revision as of 05:29, 8 December 2017

Electronic Notebook

Week 8 Corrections

• strain: dGLN3
• filename: DGLN3_ANOVA_DB
  
• timepoints: 15, 30, 60, 90, 120
  
• number of replicates: there are 4 replicates for each time point
  
• number of NA cells replaced: 6652

Part 1: Statistical Analysis Part 1

The purpose of the witin-stain ANOVA test is to determine if any genes had a gene expression change that was significantly different than zero at any timepoint.

1. I created a new worksheet, named it "dGLN3_ANOVA".
2. I copied the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for my strain and pasted it into dGLN3_ANOVA. I copied the columns containing the data for dGLN3 and pasted it into dGLN3_ANOVA.
3. At the top of the first column to the right of your data, I created five column headers of the form dGLN3_AvgLogFC_(TIME) where (TIME) is 15, 30, etc.
4. In the cell below the dGLN3_AvgLogFC_t15 header, I typed =AVERAGE(
5. Then I highlighted all the data in row 2 associated with dGLN3 and t15 and pressed the closing paren key (shift 0),and pressed the "enter" key.
6. This cell now contains the average of the log fold change data from the first gene at t=15 minutes.
7. I Clicked on this cell and positioned my 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.
8. I then repeated steps (4) through (8) with the t30, t60, t90, and the t120 data. Except with each new time column I used the formula corresponding to its time point i.e. dGLN3_AvgLogFC_t30 for time column 30, dGLN3_AvgLogFC_t60 for time column 60. dGLN3_AvgLogFC_t90 for time column 90, and dGLN3_AvgLogFC_t120 for time column 120.
9. Now in the first empty column to the right of the dGLN3_AvgLogFC_t120 calculation, I created the column header dGLN3_ss_HO.
10. In the first cell below this header, I typed =SUMSQ(
11. I highlighted all the LogFC data in row 2 of dGLN3 (but not the AvgLogFC), then pressed the closing paren key (shift 0),and pressed the "enter" key.
12. In the next empty column (AC) to the right of dGLN3_ss_HO, I created the column headers dGLN3_ss_(TIME) where (TIME) is 15, 30, 60, 90, and 120.
13. There are 4 data points for each time point. The total number of data points is 20 .
14. In the cell AC2, I typed =SUMSQ(<range of cells for logFC_t15>)-COUNTA(<range of cells for logFC_t15>)*<AvgLogFC_t15>^2 and hit enter.
    • The COUNTA function counts the number of cells in the specified range that have data in them (i.e., does not count cells with missing values).
    • The phrase <range of cells for logFC_t15> should be replaced by the data range associated with t15.
    • The phrase <AvgLogFC_t15> should be replaced by the cell number in which you computed the AvgLogFC for t15, and the "^2" squares that value.
    • Upon completion of this single computation, I used the Step (7) trick to copy the formula throughout the column.
15. I repeated this computation for the t30 through t120 data points. Again, I made sure to get the data for each time point, typed the right number of data points, got the average from the appropriate cell for each time point, and copied the formula to the whole column for each computation.
16. In cell AI1, I created the column header dGLN3_SS_full.
17. In cell AI2, I typed =sum(<range of cells containing "ss" for each timepoint>) and hit enter.
18. In cells AJ1 and AK1, I created the headers dGLN3_Fstat and dGLN3_p-value.
19. In cell AJ2, I typed =((20-5)/5)*(AC2-AI2)/AI2 and hit enter.
    • I copied this to the whole column.
20. In cell AK2, I typed =FDIST(AJ2,5,20-5).
    • I copied this to the whole column.
21. Before I moved on to the next step, I performed a quick sanity check to see if I did all of these computations correctly.
    • I clicked on cell A1 and click on the Data tab. I selected the Filter icon (looks like a funnel). 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.
    • I click on the drop-down arrow on cell AK2 and selected "Number Filters". In the window that appears, set a criterion that will filter your data so that the p value has to be less than 0.05.
    • Excel will now only display the rows that correspond to data meeting that filtering criterion. A number will appear in the lower left hand corner of the window giving you the number of rows that meet that criterion. We will check our results with each other to make sure that the computations were performed correctly.

Calculate the Bonferroni and p value Correction

1. Then I performed adjustments to the p value to correct for the multiple testing problem. I labeled the next two columns to the right with the same label, dGLN3_Bonferroni_p-value.
2. I type the equation =<dGLN3_p-value>*6189, Upon completion of this single computation, I used the Step (10) trick to copy the formula throughout the column.
3. I replace any corrected p value that is greater than 1 by the number 1 by typing the following formula into cell AL2: =IFAK2>1,1,AK2). I used the Step (10) trick to copy the formula throughout the column.

Calculate the Benjamini & Hochberg p value Correction

1. I Inserted a new worksheet named "dGLN3_ANOVA_B-H".
2. I copied and pasted 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. I copied the unadjusted p values from my ANOVA worksheet and pasted it into Column D.
4. I select all of columns A, B, C, and D and sorted by ascending values on Column D. I clicked the sort button from A to Z on the toolbar, in the window that appears, sort by column D, smallest to largest.
5. I typed 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 dGLN3_B-H_p-value in cell F1. Type the following formula in cell F2: =(D2*6189)/E2 and press enter. I copied that equation to the entire column.
7. I typed "dGLN3_B-H_p-value" into cell G1.
8. I typed the following formula into cell G2: =IF(F2>1,1,F2) and pressed enter then copied that equation to the entire column.
9. I selected columns A through G. Then sorted them by my MasterIndex in Column A in ascending order.
10. I copied column G and used Paste special > Paste values to paste it into the next column on the right of my ANOVA sheet.

• I uploaded this file to the deliverables section of this wiki page, naming it DGLN3_ANOVA_DB.

Sanity Check: Number of genes significantly changed

Before I moved on to further analysis of the data, I wanted to perform a more extensive sanity check to make sure that I performed my data analysis correctly. I am going to find out the number of genes that are significantly changed at various p value cut-offs.

• I went to my dGLN3_ANOVA worksheet.
• I Selected row 1 (the row with my column headers) and selected 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.
• I clicked on the drop-down arrow for the unadjusted p value. I set a criterion that will filter my data so that the p value has to be less than 0.05.

**Genes with a p < 0.05 = 2135/6189 records or 34.50% of the data.

• • Genes with a p < 0.01 = 1204/6189 records or 19.45% of the data.
  • Genes with a p < 0.001 = 514/6189 records or 8.31% of the data.
  • Genes with a p < 0.0001 = 180/6189 records or 2.91% of the data.

• When I use a p value cut-off of p < 0.05, what I am saying is that I would have seen a gene expression change that deviates this far from zero by chance less than 5% of the time.
• I have just performed 6189 hypothesis tests. Another way to state what I am seeing with p < 0.05 is that I 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 I have more than 309 genes that pass this cut off, I know that some genes are significantly changed. However, I don't know which ones. To apply a more stringent criterion to our p values, I performed 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. I filtered the data to determine this relationship and found:

**Genes with a p < 0.05 for the Bonferroni-corrected p-value = 45/6189 records or 0.73% of the data.

• • Genes with a p < 0.05 for the Benjamini and Hochber-corrected p-value = 1185/6189 records or 19.15% of the data.
• 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.
• I compared the results to the wild type strain and uploaded the information to a powerpoint that is linked to in the deliverables section of this wiki.
• I also compared NSR1 and ADH1 and found the following:

NSR1

1. Unaltered p-value = 0.000506
2. Bonferroni-Corrected p-value = 1
3. B-H corrected p-value = 0.008167
4. Average Log Fold:
   • @ 15 = 3.50622
   • @ 30 = 4.53189
   • @ 60 = 2.75921
   • @ 90 = -1.85027
   • @ 120 = -1.86741
5. As shown above, we can infer that gene expression started off high but eventually decreased to a consistent level due to cold shock.

ADH1

1. Unaltered p-value = 0.772
2. Bonferonni-corrected P-Value: 1
3. B-H-corrected P-Value: 0.8617
4. Average Log Fold
   • @ 15 = -0.902324179
   • @ 30 = -0.692990646
   • @ 60 = 0.138781308
   • @ 90 = -0.045097454
   • @ 120 = 0.514075012
5. As shown above, we can infer that gene expression started low but rose around the 60 interval then decreased a significant amount at the 90 interval, only to rise again at the last interval. This shows that cold shock affects the gene expression around the 30-60 interval and the 90-120 interval.

Summary

In summary, we hoped to find genes that showed significantly different rates of gene expression from the start of the experiment (time 0) to the end, 120 minutes later. We determined this by performing an ANOVA and determining the significance of the results. We were able to determine what the effects of cold related osmotic shock was on gene expression.

Week 10 Corrections

Clustering and GO Term Enrichment with stem

1. Prepare your microarray data file for loading into STEM.
   • Download your Excel workbook that you used for your Week 8 assignment.
   • Insert a new worksheet into your Excel workbook, and name it "(STRAIN)_stem".
   • Select all of the data from your "(STRAIN)_ANOVA" worksheet and Paste special > paste values into your "(STRAIN)_stem" worksheet.
     • Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
     • Filter the data on the B-H corrected p value to be > 0.05 (that's greater than in this case).
       • Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise. Record the number of genes left in your electronic notebook.
     • Delete all of the data columns EXCEPT for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
     • Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
     • Save your work. Then use Save As to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
       • Note that you should turn on the file extensions if you have not already done so.
2. Now download and extract the STEM software. Click here to go to the STEM web site.
   • Click on the download link, register, and download the stem.zip file to your Desktop.
   • Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item 7-zip > Extract Here.
   • This will create a folder called stem. Inside the folder, double-click on the stem.jar to launch the STEM program.
3. Running STEM
   1. In section 1 (Expression Data Info) of the the main STEM interface window, click on the Browse... button to navigate to and select your file.
      • Click on the radio button No normalization/add 0.
      • Check the box next to Spot IDs included in the data file.
   2. In section 2 (Gene Info) of the main STEM interface window, select Saccharomyces cerevisiae (SGD), from the drop-down menu for Gene Annotation Source. Select No cross references, from the Cross Reference Source drop-down menu. Select No Gene Locations from the Gene Location Source drop-down menu.
   3. In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
   4. In section 4 (Execute) click on the yellow Execute button to run STEM.
4. Viewing and Saving STEM Results
   1. A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
      • Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
      • Take a screenshot of this window (on a PC, simultaneously press the Alt and PrintScreen buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
   2. Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
      • Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
      • At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
        • Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
      • For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
        • Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
5. Analyzing and Interpreting STEM Results
   1. Select one of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. Each member of your group should choose a different profile. Answer the following:
      • Why did you select this profile? In other words, why was it interesting to you?
      • How many genes belong to this profile?
      • How many genes were expected to belong to this profile?
      • What is the p value for the enrichment of genes in this profile? Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
      • Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. How many GO terms are associated with this profile at p < 0.05? The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. How many GO terms are associated with this profile with a corrected p value < 0.05?
      • Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05).
        • Each member of the group will be reporting on his or her own cluster in your presentation next week. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
        • Look up the definitions for each of the terms at http://geneontology.org. In your final presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor that was deleted from your strain?
        • To easily look up the definitions, go to http://geneontology.org.
        • Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
        • In the results page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
        • The definition will be on the next results page, e.g. here.

Week 10 Continued

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

In the previous analysis using STEM, we found a number of gene expression profiles (aka clusters) which grouped genes based on similarity of gene expression changes over time. The implication is that these genes share the same expression pattern because they are regulated by the same (or the same set) of transcription factors. We will explore this using the YEASTRACT database.

1. I opened the gene list in Excel for profile 45 of my stem analysis. I chose this cluster because it had a cold shock/recovery up/down or down/up pattern and it was one of the largest clusters.
   • I then copied the list of gene IDs onto my clipboard.
2. I launched a web browser and went to the YEASTRACT database.
   • On the left panel of the window, I clicked on the link to Rank by TF.
   • I pasted my list of genes from my chosen 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"?
   • There are 30 green or significant transcription factors.
     • I copied the table of results from the web page and pasted it into a new Excel workbook to preserve the results.
       • I upload the Excel file to Box and link to it below in the deliverables section of this wiki, naming it "Yeastract_Results_DB_Gene_hAPI.
       • My transcription factor is on the list. It's % in user set is 0.3085%, its % in yeastract is 0.1044%, and its p value is 1E-13.
4. For the mathematical model and GRNsight, we need to define a gene regulatory network of transcription factors that regulate other transcription factors. We can use YEASTRACT to assist us with creating the network. We want to generate a network with approximately 15-30 transcription factors in it.
   • I chose all 30 of the significant transcription factors on my list, adding HAP4 since it was not already on the list. I chose these transcription factors among the rest because they are all significant so this way I can analyze all the significant TFs keeping in mind their % in user set, % in yeastract, and p value. All 30 TFs are listed below.
     • Sfp1p
     •  Msn2p
     • Yhp1p
     • Yox1p
     • Ace2p
     • Gln3p
     • Yap1p
     • Pdr3p
     • Ume6p
     •  Pdr1p
     •  Stb5p
     •  Swi5p
     •  YLR278C
     •  Mig2p
     •  Asg1p
     •  Tup1p
     •  Gcr2p
     •  Msn4p
     •  Rim101p
     •  Gcn4p
     • Sut1p
     • Mcm1p
     •  Met4p
     •  Rlm1p
     •  Ino4p
     •  Ndt80p
     •  Zap1p
     •  Abf1p
     •  Cyc8p
     •  Gat3p
   • I then went to the link Generate Regulation Matrix on the yeastract database and copied and pasted the list of transcription factors above into both the "Transcription factors" field and the "Target ORF/Genes" field.
   • We are going to use the "Regulations Filter" options of "Documented", "Only DNA binding evidence"
     • Click the "Generate" button.
     • In the results window that appears, click on the link to the "Regulation matrix (Semicolon Separated Values (CSV) file)" that appears and save it to your Desktop. Rename this file with a meaningful name so that you can distinguish it from the other files you will generate.

Visualizing Your Gene Regulatory Networks with GRNsight

We will analyze the regulatory matrix files you generated above in Microsoft Excel and visualize them using GRNsight to determine which one will be appropriate to pursue further in the modeling.

1. First we need to properly format the output files from YEASTRACT. You will repeat these steps for each of the three files you generated above.
   • Open the file in Excel. It will not open properly in Excel because a semicolon was used as the column delimiter instead of a comma. To fix this, Select the entire Column A. Then go to the "Data" tab and select "Text to columns". In the Wizard that appears, select "Delimited" and click "Next". In the next window, select "Semicolon", and click "Next". In the next window, leave the data format at "General", and click "Finish". This should now look like a table with the names of the transcription factors across the top and down the first column and all of the zeros and ones distributed throughout the rows and columns. This is called an "adjacency matrix." If there is a "1" in the cell, that means there is a connection between the trancription factor in that row with that column.
   • Save this file in Microsoft Excel workbook format (.xlsx).
   • I checked 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, it was deleted, making sure that I still had somewhere between 15 and 30 transcription factors in my network after this pruning.
     • Only delete the transcription factor if there are all zeros in its column AND all zeros in its row. You may find visualizing the matrix in GRNsight (below) can help you find these easily.
   • For this adjacency matrix to be usable in GRNmap (the modeling software) and GRNsight (the visualization software), we need to transpose the matrix. Insert a new worksheet into your Excel file and name it "network". Go back to the previous sheet and select the entire matrix and copy it. Go to you new worksheet and click on the A1 cell in the upper left. Select "Paste special" from the "Home" tab. In the window that appears, check the box for "Transpose". This will paste your data with the columns transposed to rows and vice versa. This is necessary because we want the transcription factors that are the "regulatORS" across the top and the "regulatEES" along the side.
   • The labels for the genes in the columns and rows need to match. Thus, delete the "p" from each of the gene names in the columns. Adjust the case of the labels to make them all upper case.
   • In cell A1, copy and paste the text "rows genes affected/cols genes controlling".
   • Finally, for ease of working with the adjacency matrix in Excel, we want to alphabatize the gene labels both across the top and side.
     • Select the area of the entire adjacency matrix.
     • Click the Data tab and click the custom sort button.
     • Sort Column A alphabetically, being sure to exclude the header row.
     • Now sort row 1 from left to right, excluding cell A1. In the Custom Sort window, click on the options button and select sort left to right, excluding column 1.
   • Name the worksheet containing your organized adjacency matrix "network" and Save.
2. Now we will visualize what these gene regulatory networks look like with the GRNsight software.
   • Go to the GRNsight home page.
   • Select the menu item File > Open and select the regulation matrix .xlsx file that has the "network" worksheet in it that you formatted above. If the file has been formatted properly, GRNsight should automatically create a graph of your network. Move the nodes (genes) around until you get a layout that you like and take a screenshot of the results. Paste it into your PowerPoint presentation.

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

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

Deliverables

DGLN3 ANOVA/Stem
DGLN3 ppt Dina
DGLN3 Gene List and GO list
Yeastract TF List
GRNmap dGLN3 input
GRNmap dGLN3 output