William A. C. Gendron Week 11

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Contents

Methods

Biological Methods

For this experiment, Jeff and I used data from microarray experiments that measured wild type yeast or in my case mutant yeast reaction to cold shock. The data I worked with was a dCIN5 mutant strain. All of the yeast started at 30°C and then were exposed to 13°C from time point 0 to 60 minutes. Microarray data was taken from 3 time points under cold shock: t15, t30 and t60. The yeast were then returned to 30°C at 60 minutes for a recovery period in which they were monitored at time points t90 and t120. The data was recorded 3 times for each time point.


Assigned Methods: Statistical Analysis Part 1: ANOVA

Below are the instructions we used to analyze the data. All of it is the exact information we were given to do the experiment. This was used after we were given the data set in an excel file.

  1. Create a new worksheet, naming it stats
  2. Copy the first two columns of the data worksheet (containing ID and Standard Name) into the stats sheet.
  3. In the first row, columns c through g, create column labels of the form (STRAIN)_xbar_(TIME) where (STRAIN) is wt, dGLN3, etc., and (TIME) is 15, 30, etc.
  4. In the first row, columns h and i, create the column labels (STRAIN)_xbar_grand and (STRAIN)_ss_HO.
  5. In the first row, columns j through n, create the column labels (STRAIN)_ss_(TIME) as in (3).
  6. In the first row, columns o, p, and q, create the column labels (STRAIN)_SS_full, Fstat and p-value.
  7. Now we're ready to compute. In cell c2, type =AVERAGE(
  8. Then click on the tab containing the data, and highlight all the data in row 2 associated with (STRAIN) and t15, press the closing paren key (shift 0),and press the "enter" key.
  9. Click on the tab for the stats sheet. Cell c2 now contains the average of the log fold change data from the first gene at t=15 minutes.
  10. Click on cell c2 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.
  11. Move to cell d2, and repeat (7) through (10) with the t30 data, to e2 with the t60 data, f2 with the t90, g2 with the 120.
  12. Move to cell h2, and repeat (7) through (10) highlighting all the data for (STRAIN) in row 2 instead of the individual time points.
  13. Now, we move to cell i2. Type =SUMSQ(
  14. Click on the data sheet's tab again, and highlight all the data in row 2 for your (STRAIN), press the closing paren key (shift 0),and press the "enter" key.
    • The data highlighted here will be same as in (12).
  15. Make a note of how many data points you have at each time point. In most cases this number will be 4, but for some strains and times it may be 5. Count carefully. Also, make a note of the total number of data points. For most strains this number will be 20, but for wt it may be 23.
  16. In cell j2, type =SUMSQ(data!C2:F2)-4*stats!C2^2 and hit enter.
    • The phrase "data!C2:F2" should be the data associated with t15. The number "4" is the number of data points (note that cells c2, d2, e2, f2 contain 4 data points). The phrase "stats!c2" gets the average you computed in Step (8) for t15, and the "^2" squares that value. Upon completion of this single computation, use the Step (10) trick to copy the formula throughout the column.
  17. In cells k2 through n2, repeat (16) for the t30 through t120 data points. Again, be sure to get the data for each time point, type the right number of data points, and get the average from the appropriate cell (d2,e2,f2,g2) for each time point, and copy the formula to the whole column for each computation.
  18. Once you've populated cells j2 through n2, click on o2 and type =sum(j2:n2) and hit enter. Copy to the whole column.
  19. recall the number of data points from (15): call that total n.
  20. In cell p2, type =((n-5)/5)*(i2-o2)/o2 and hit enter. Don't actually type the n but instead use the number from (20). copy to the whole column.
  21. In cell q2, type =FDIST(P2,5,n-5) replacing n as in (20) with the number of data points total. Copy to the whole column.
  22. Now we will perform adjustments to the p value to correct for the multiple testing problem. Label column r "STRAIN_Bonferroni_p-value".
  23. Type the equation =q2*6189, Upon completion of this single computation, use the Step (10) trick to copy the formula throughout the column.
  24. Replace any corrected p value that is greater than 1 by the number 1 by typing the following formula into cell s2: =IF(r2>1,1,r2)
Calculate the Benjamini & Hochberg p value Correction
  1. Insert a new worksheet named "B&H".
  2. First, create an index column by first typing "Index" into cell A1. Then type "1" into cell A2 and "2" into cell A3. Select both cells A2 and A3. 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. We will use this to put the genes back in order at the end of these calculations.
  3. Copy and paste the column of ID's from one of the previous worksheets into column B.
  4. For the following, use Paste special > Paste values. Copy Column Q (the unadjusted p values) from the stats worksheet and paste it into Column C.
  5. Select all of columns A, B, and C. Sort by ascending values on Column C. Click the sort button from A to Z on the toolbar, in the window that appears, sort by column C, smallest to largest.
  6. Type the header "Rank" in cell D1. Repeat what you did in step 2 to create a series of numbers in ascending order from 1 to 6189. This is the p value rank, smallest to largest.
  7. Now you can calculate the Benjamini and Hochberg p value correction. Type "STRAIN_B-H_p-value" in cell E1. Type the following formula in cell E2: =(C2*6189)/D2 and press enter. Copy that equation to the entire column using the trick you learned last week.
  8. Type "STRAIN_B-H_p-value" into cell F1.
  9. Type the following formula into cell F2: =IF(E2>1,1,E2) and press enter. Copy that equation to the entire column using the trick you learned last week.
  10. Select columns A through F. Now sort them by your Index in Column A in ascending order.
  11. Copy column F and use Paste special < Paste values to paste it into column T of your stats sheet.


Further Statistical Methods: Picking the best statistical system

This section is about evaluating the best statistical analysis for this situation. On either extreme with tests like this, there are potential dangers. If the statistics are not stringent enough, then excess genes are selected and assumed to be part of the system. If we try to cut down on the noise too much, then we risk ignoring genes that are potentially active in this system. Ideally, we would go back and just repeat the experiment to increase the n, but in this case we try to find the Goldilocks equation of statistics to make sense of the the minimal data. The process below is how we went about this.

  • Go to the "stats" 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 on Column Q. Select "Custom". 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.
  • 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:
  • 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.
Clustering and Gene Ontology Analysis with STEM

After finding p-values for the individual genes this program is used to analyze groups of genes that match certain patterns and seeing if they form significant patterns.

  1. Begin by downloading and extracting 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.cmd to launch the STEM program.
      • In Seaver 120, we encountered an issue where the program would not launch on the Windows XP machines due to a lack of memory. (Even though the computers have been upgraded to Windows 7, do this to launch the program.) To get around this problem, launch STEM from the command line.
        • Go to the start menu and click on Programs > Accessories > Command Prompt.
        • You will need to navigate to the directory (folder) in which the STEM program resides. If you followed the instructions above and extracted the stem folder to the Desktop, type the following: cd Desktop\stem and press "Enter".
        • To launch the program then type: java -mx512M -jar stem.jar -d defaults.txt and press "Enter". This will launch the program with less memory allocated to it.
  2. Prepare your microarray data file for loading into STEM.
    • Insert a new worksheet into your Excel workbook, and name it "stem".
    • Copy the "Index" column from your "B&H" worksheet and paste it into column A of your "stem" worksheet. Select all of the data from your "stats" worksheet and Paste special > paste values into your "stem" worksheet, starting with column B.
      • Your leftmost column should have the column header "Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol".
      • Filter the data on the B-H corrected p value to be > 0.05 (that's greater than in this case).
        • Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise.
      • Delete all of the data columns EXCEPT for the Average Log Fold change columns for each timepoint (for example, wt_xbar_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 it would be a good idea to turn on the file extensions by following the procedure on the class Help page.
  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 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.
      • 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!

Results

Excel Results
    • How many genes have p < 0.05? and what is the percentage (out of 6189)?
      • Wildtype: 2378/6198 (38.4%)
      • dCIN5 STRAIN: 2034/6198 (32.86%)
    • How many genes have p < 0.01? and what is the percentage (out of 6189)?
      • Wildtype: 1527/6198 (24.7%)
      • dCIN5 STRAIN: 1198/6198 (19.36%)
    • How many genes have p < 0.001? and what is the percentage (out of 6189)?
      • Wildtype:860/6198 (13.9%)
      • dCIN5 STRAIN: 576/6198 (9.31%)
    • How many genes have p < 0.0001? and what is the percentage (out of 6189)?
      • Wildtype: 460/6198 (7.4%)
      • dCIN5 STRAIN: 288/6198 (4.65%)
    • How many genes are p < 0.05 for the Bonferroni-corrected p value? and what is the percentage (out of 6189)?
      • Wildtype: 228/6198 (3.68%)
      • dCIN5 STRAIN: 119/6198 (1.92%)
    • How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?
      • Wildtype: 1656/6198 (26.8%)
      • dCIN5 STRAIN: 1177/6198 (19.02%)


The expression of the gene NSR1

    • What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values?
      • dCIN5 Strain
        • Unadjusted p-value: 8.80002*10^-8
        • Bonferroni-corrected: .000544634
        • Bonferroni-Hochberg-corrected: 3.63089*10^-5
    • What is its average Log fold change at each of the timepoints in the experiment?
      • t15: 4.088
      • t30: 3.376
      • t60: 4.217
      • t90: -2.759
      • t120: 1.600

STEM Results

Powerpoint Showing STEM Data: Media: GendronBiomath_dCIN5_STEM.pptx

    • Why did you select this profile? In other words, why was it interesting to you?
      • I selected Profile 30 because the gene activity reaction is complex. These genes are up-regulated for the first 30 minutes, but then experience a decrease around 60. The yeast are then returned to normal temperatures at 60 and again there is an up-regulation. My assumption is that these genes have something to do with acclimation to both environments.
    • How many genes were assigned to this profile?
      • 87 genes were assigned to this profile.
    • How many genes were expected to belong to this profile?
      • 39.5 genes were expected to belong to this profile.
    • What is the p value for the enrichment of genes in this profile?
      • The p-value for this file was 1.6*10^-11.
    • 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?
      • 9 terms are significant.
    • 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?
      • None
    • Select 10 Gene Ontology terms from your filtered list p<.05 . Look up the definitions for each of the terms at http://geneontology.org.
      1. GO:0031123 RNA 3'-end processing: Any process involved in forming the mature 3' end of an RNA molecule. Source: GOC:mah
      2. GO:0006402 mRNA catabolic process: The chemical reactions and pathways resulting in the breakdown of mRNA, messenger RNA, which is responsible for carrying the coded genetic 'message', transcribed from DNA, to sites of protein assembly at the ribosomes. Source: ISBN:0198506732
      3. GO:0000956 nuclear-transcribed mRNA catabolic process: The chemical reactions and pathways resulting in the breakdown of nuclear-transcribed mRNAs in eukaryotic cells. Source: GOC:krc
      4. GO:0031981 nuclear lumen: The volume enclosed by the nuclear inner membrane. Source: GOC:mah, GOC:pz.
      5. GO:0019439 aromatic compound catabolic process:The chemical reactions and pathways resulting in the breakdown of aromatic compounds, any substance containing an aromatic carbon ring. Source: GOC:ai
      6. GO:0031505 fungal-type cell wall organization: A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of the fungal-type cell wall. Source: GOC:dph, GOC:jl, GOC:mah, GOC:mtg_sensu
      7. GO:0005654 nucleoplasm: That part of the nuclear content other than the chromosomes or the nucleolus. Source: GOC:ma, ISBN:0124325653
      8. GO:0016071 mRNA metabolic process: The chemical reactions and pathways involving mRNA, messenger RNA, which is responsible for carrying the coded genetic 'message', transcribed from DNA, to sites of protein assembly at the ribosomes. Source: ISBN:0198506732
      9. GO:1901361 organic cyclic compound catabolic process: The chemical reactions and pathways resulting in the breakdown of organic cyclic compound. Source: GOC:TermGenie
      10. GO:0006401 RNA catabolic process: The chemical reactions and pathways resulting in the breakdown of RNA, ribonucleic acid, one of the two main type of nucleic acid, consisting of a long, unbranched macromolecule formed from ribonucleotides joined in 3',5'-phosphodiester linkage. Source: ISBN:0198506732
    • Biology Interpretation
      • Most of these are involved in gene regulation. This makes sense because they match the pattern of reacting to different environments. As the yeast enter a cold environment or are brought back into a warmer environment, this machinery is activated to help regulate the changes. It includes nuclear changes, chromosome changes and RNA changes. I am confused as to why there is something that would alter the fungal-type cell wall.
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