Kwrigh35 Week 8

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Assignment Deliverables

Excel Workbook: media:DZAP1_ANOVA_KWRIGH35.zip
P-Value PowerPoint Slide: media:Kwrigh35_dZAP1_p-value_slide.zip

Electronic Notebook

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. Create a new worksheet, naming it "dZAP1_ANOVA"
  2. Copy the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet and paste it into your new worksheet. Copy the columns containing the data for dZAP1 and paste it into your new worksheet.
  3. At the top of the first column to the right of your data, create five column headers of the form dZAP1_AvgLogFC_(TIME) where (TIME) is 15, 30, etc.
  4. In the cell below the dZAP_AvgLogFC_t15 header, type =AVERAGE(
  5. Then highlight all the data in row 2 associated with dZAP1 and t15, press the closing paren key (shift 0),and press 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. 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.
  8. Repeat steps (4) through (8) with the t30, t60, t90, and the t120 data.
  9. Now in the first empty column to the right of the dZAP1_AvgLogFC_t120 calculation, create the column header dZAP1_ss_HO.
  10. In the first cell below this header, type =SUMSQ(
  11. Highlight all the LogFC data in row 2 for dZAP1 (but not the AvgLogFC), press the closing paren key (shift 0),and press the "enter" key.
  12. In the next empty column to the right of dZAP1_ss_HO, create the column headers dZAP1_ss_(TIME) as in (3).
  13. Make note that at each time point for dZAP1, there will be 4 data points. Also, make a note that there are a total of 20 data points for each gene.
  14. In the first cell below the header dZAP1_ss_t15, type =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, use the Step (7) trick to copy the formula throughout the column.
  15. Repeat this computation for the t30 through t120 data points. Again, be sure to get the data for each time point, get the average from the appropriate cell for each time point, and copy the formula to the whole column for each computation.
  16. In the first column to the right of dZAP1_ss_t120, create the column header dZAP1_SS_full.
  17. In the first row below this header, type =sum(<range of cells containing "ss" for each timepoint>) and hit enter.
  18. In the next two columns to the right, create the headers dZAP1_Fstat and dZAP1_p-value.
  19. Recall that the total number of data points for each gene is 20; call that total n.
  20. In the first cell of the dZAP1_Fstat column, type =((n-5)/5)*(<dZAP1_ss_HO>-<dZAP1_SS_full>)/<dZAP1_SS_full> and hit enter.
    • Don't actually type the n but instead use the number 20. Also note that "5" is the number of timepoints.
    • Replace the phrase <dZAP1_ss_HO> with the cell designation.
    • Replace the phrase <dZAP1_SS_full> with the cell designation.
    • Copy to the whole column.
  21. In the first cell below the dZAP1_p-value header, type =FDIST(<dZAP1_Fstat>,5,n-5) replacing the phrase <dZAP1_Fstat> with the cell designation and the "n" with the number 20, as before. Copy to the whole column.
  22. Before we move on to the next step, we will perform a quick sanity check to see if we did all of these computations correctly.
    • Click on cell A1 and click on the Data tab. Select 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.
    • Click on the drop-down arrow on your dZAP1_p-value column. Select "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. Now we will perform adjustments to the p value to correct for the multiple testing problem. Label the next two columns to the right with the same label, dZAP1_Bonferroni_p-value.
  2. Type the equation =<dZAP1_p-value>*6189, Upon completion of this single computation, use the Step (10) 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 dZAP1_Bonferroni_p-value header: =IF(dZAP1_Bonferroni_p-value>1,1,dZAP1_Bonferroni_p-value), where "dZAP1_Bonferroni_p-value" refers to the cell in which the first Bonferroni p value computation was made. Use the Step (10) trick to copy the formula throughout the column.

Calculate the Benjamini & Hochberg p value Correction

  1. Insert a new worksheet named "dZAP1_ANOVA_B-H".
  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 your ANOVA 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 dZAP1_B-H_p-value 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 "dZAP1_B-H_p-value" 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 ANOVA sheet.
  • Zip and upload the .xlsx file that you have just created to the wiki.

Sanity Check: Number of genes significantly changed

Before we move on to further analysis of the data, we want to perform a more extensive sanity check to make sure that we performed our data analysis correctly. We are going to find out the number of genes that are significantly changed at various p value cut-offs.

  • Go to your dZAP1_ANOVA 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. 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)?
      • 2485, which is 40.15%
    • How many genes have p < 0.01? and what is the percentage (out of 6189)?
      • 1609 which is 26.00%
    • How many genes have p < 0.001? and what is the percentage (out of 6189)?
      • 885 which is 14.30%
    • How many genes have p < 0.0001? and what is the percentage (out of 6189)?
      • 457 which is 7.38%
  • 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 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. To see this relationship, filter your data to determine the following:
    • How many genes are p < 0.05 for the Bonferroni-corrected p value? and what is the percentage (out of 6189)?
      • 209, which is 3.38%
    • How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?
      • 1766 which is 28.53%
  • 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.
  • Comparing results with known data: the expression of the gene NSR1 (ID: YGR159C)is known to be induced by cold shock. Find NSR1 in your dataset. What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values? What is its average Log fold change at each of the timepoints in the experiment? Note that the average Log fold change is what we called "dZAP1_AvgLogFC_(TIME)" in step 3 of the ANOVA analysis.
    • Unadjusted p-value: 6.05652E-08
    • Bonferroni-corrected p-value: 0.000374838
    • B-H-corrected p-value: 1.04122E-05
    • Average Log fold change at
      • t=15: 3.899590737
      • t=30: 3.723795809
      • t=60: 3.962776134
      • t=90: -2.155992517
      • t=120: 0.054215477
  • We will compare the numbers we get between the wild type strain and the other strains studied, organized as a table. Use this sample PowerPoint slide to see how your table should be formatted. Upload your slide to the wiki.
  • For fun, find "your favorite gene" (from your web page) in the dataset. What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values? What is its average Log fold change at each of the timepoints in the experiment? Does your favorite gene change expression due to cold shock in this experiment?
    • ASP1 unadjusted p-value: 0.003071934
    • ASP1 bonferroni p-value: 19.01219831
    • ASP1 B-H p-value: 0.016112032
    • ASP1 average Log fold change at t=
      • 15: -0.599364594
      • 30: -0.518466645
      • 60: -0.317314932
      • 90: -0.506140896
      • 120: -1.222038905

Summary Paragraph

While completing this assignment, I learned a number of things. I learned a few new tricks in Excel, as well as why it is so important to manage data well. If data is mis-managed, it can create a whole host of frustrating (and potentially life threatening, in the case of Dr. Potti's fraud) problems down the road. While analyzing the data set from this week, I determined the p-values for the log-fold changes of genes in the dZAP1 deleted strain of yeast. I found that just over 40% of the genes have p < 0.05. Two more stringent p-value corrections, "Bonferroni" and "Benjamini and Hochberg," put the number of genes having p < 0.05 at 3.38% and 28.53% respectively. These two corrections are good for times when we want more certainty in our results, but are probably unnecessary for this set of data since it is not being used to justify clinical trials.

Acknowledgements

  • I'd like to thank my homework partner, Antonio Porras. We worked together/checked answers during class on Thursday October 19th, and nearly finished the assignment.
  • Thanks also to Dr. Dionisio, who helped me figure out how to upload my PowerPoint slide.
  • While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.


Kwrigh35 (talk) 19:46, 23 October 2017 (PDT)

Refereneces

LMU BioDB 2017. (2017). Week 8. Retrieved October 17, 2017, from Week 8


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