Week 8

From LMU BioDB 2017
Jump to: navigation, search

This journal entry is due on Tuesday, October 24, at 12:01 AM PDT.

Objectives

The purpose of this assignment is:

  • to conduct the "analyze" step of the data life cycle for a DNA microarray dataset.
  • to develop an intuition about what different p-value cut-offs mean.
  • to keep a detailed electronic laboratory notebook to facilitate reproducible research.
  • to revisit the "Deception at Duke" case with new insights because you have analyzed your own dataset.

Individual Journal Assignment

  • Store this journal entry as "username Week 8" (i.e., this is the text to place between the square brackets when you link to this page).
  • Invoke your template on your journal entry page so that you:
    • Link from your journal entry page to this Assignment page.
    • Link from your journal entry to your user page.
    • Add the "Journal Entry" category to the end of your wiki page.
  • Because you have invoked your template on your user page, you should also have a:
    • Link from your user page to this Assignment page.
    • Link to your journal entry from your user page.
  • Include both the Acknowledgments and References section as specified by the Week 1 assignment.
  • For your assignment this week, the electronic laboratory notebook you will keep on your individual wiki page is crucial. An electronic laboratory notebook records all the manipulations you perform on the data and the answers to the questions throughout the protocol. Like a paper lab notebook found in a wet lab, it should contain enough information so that you or someone else could reproduce what you did using only the information from the notebook.
    • We will be performing a series of computations on a microarray dataset, primarily using Microsoft Excel. In the interests of reproducible research, it is appropriate to copy and paste the methods from this assignment into your individual journal entry.
    • You must then modify the general instructions (which are generic to the whole class) to your own data analysis, recording the specific modifications and equations that you used on your dataset.
    • Record the answers to the questions posed in the protocol at the place in which they appear in the method. You do not need to separate them out in a different results section.
    • All files generated in the protocol must be uploaded to the wiki and linked to from your journal entry page.
    • You will write a summary paragraph that gives the conclusions from this week's analysis.

Homework Partners

Homework partners for this week are listed below. The particular dataset that you and your partner will work on is also indicated below. You are expected to consult with your partner, sharing your domain expertise, in order to complete the assignment. However, each partner must submit his or her own work as the individual journal entry (direct copies of each other's work is not allowed). You must give the details of the interaction with your partner in the Acknowledgments section of your journal assignment.

  • Eddie Azinge, Emma Tyrnauer: wild type data
  • Eddie Bachoura, Quinn Lanners: dASH1 data
  • Mary Balducci, Simon Wroblewski: dCIN5 data
  • Dina Bashoura, Zach Van Ysseldyk: dGLN3 data
  • Blair Hamilton, Nicole Kalcic: dHAP4 data
  • Hayden Hinsch, Arash Lari: dHMO1 data
  • John Lopez, Corinne Wong: dSWI4 data
  • Antonio Porras, Katie Wright: dZAP1 data

Microarray Data Analysis

We will be working on the protocols in class on Tuesday, October 17 and Thursday, October 19. Whatever you do not finish in class will be homework to be completed by the Week 8 journal deadline.

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 have been performed for you by the GenePix Pro software (which runs the microarray scanner).
  4. Normalize the ratios on each microarray slide
  5. Normalize the ratios for a set of slides in an experiment
  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)
  9. Map onto biological pathways
    • We will use software called STEM for the clustering and mapping
  10. Identifying regulatory transcription factors responsible for observed changes in gene expression
  11. Dynamical systems modeling of the gene regulatory network (GRNmap)
  12. Viewing modeling results in GRNsight

You will download the starting Excel spreadsheet from Box. You were e-mailed a link to do this before class.

Experimental Design and Getting Ready

  • In the Excel spreadsheet, there is a worksheet labeled "Master_Sheet".
    • In this worksheet, each row contains the data for one gene (one spot on the microarray).
    • The first column contains the "MasterIndex", 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), for each strain starting with wild type and proceeding in alphabetical order by strain deletion.
    • Each of the column headings from the data begin with the experiment name ("wt" for wild type S. cerevisiae data, "dASH1" for the Δash1 data, etc.). "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 wiki, the strain that you will analyze, 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.
  • The first thing you will do is to delete the data columns of the strains that you are not analyzing so that your file contains only the strain's data that you will be working with (to make a smaller and less confusing file).
  • Next you will replace cells that have "NA" in them (which indicates missing data) with an empty cell.
    • Use the keyboard shortcut Control+F to open the "Find" dialog box and select the "Replace" tab.
    • Type "NA" in the Search field and don't type anything in the "Replace" field.
    • Click the button "Replace all" and record the number of replacements made in your electronic lab notebook.
    • Save early and often throughout this protocol. We are working with a large spreadsheet and glitches do occur.

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 either "(STRAIN)_ANOVA" as appropriate. For example, you might call yours "wt_ANOVA" or "dHAP4_ANOVA"
  2. Copy the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for your strain and paste it into your new worksheet. Copy the columns containing the data for your strain 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 (STRAIN)_AvgLogFC_(TIME) where dHMO1 is your strain designation and (TIME) is 15, 30, etc.
  4. In the cell below the (STRAIN)_AvgLogFC_t15 header, type =AVERAGE(
  5. Then highlight all the data in row 2 associated with dHMO1 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 (STRAIN)_AvgLogFC_t120 calculation, create the column header (STRAIN)_ss_HO.
  10. In the first cell below this header, type =SUMSQ(
  11. Highlight all the LogFC data in row 2 for your dHMO1 (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 (STRAIN)_ss_HO, create the column headers (STRAIN)_ss_(TIME) as in (3).
  13. Make a note of how many data points you have at each time point for your strain. For most of the strains, it will be 4, but for dHAP4 t90 or t120, it will be "3", and for the wild type it will be "4" or "5". Count carefully. Also, make a note of the total number of data points. Again, for most strains, this will be 20, but for example, dHAP4, this number will be 18, and for wt it should be 23 (double-check).
  14. In the first cell below the header (STRAIN)_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, type the right number of data points, and 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 (STRAIN)_ss_t120, create the column header dHMO1_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 (STRAIN)_Fstat and dHMO1_p-value.
  19. Recall the number of data points from (13): call that total n.
  20. In the first cell of the (STRAIN)_Fstat column, type =((n-5)/5)*(<(STRAIN)_ss_HO>-<dHMO1_SS_full>)/<dHMO1_SS_full> and hit enter.
    • Don't actually type the n but instead use the number from (13). Also note that "5" is the number of timepoints and the dSWI4 strain has 4 timepoints (it is missing t15).
    • Replace the phrase (STRAIN)_ss_HO with the cell designation.
    • Replace the phrase <dHMO1_SS_full> with the cell designation.
    • Copy to the whole column.
  21. In the first cell below the dHMO1_p-value header, type =FDIST(<(STRAIN)_Fstat>,5,n-5) replacing the phrase <(STRAIN)_Fstat> with the cell designation and the "n" as in (13) with the number of data points total. (Again, note that the number of timepoints is actually "4" for the dSWI4 strain). 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 dHMO1_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, (STRAIN)_Bonferroni_p-value.
  2. Type the equation =<dHMO1_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 (STRAIN)_Bonferroni_p-value header: =IF((STRAIN)_Bonferroni_p-value>1,1,(STRAIN)_Bonferroni_p-value), where "(STRAIN)_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 "(STRAIN)_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 (STRAIN)_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 "STRAIN_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 (STRAIN)_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)?
    • 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)?
  • 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)?
    • How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?
  • 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.
  • 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.
    • Note that since the wild type data is being analyzed by one of the groups in the class, it will be sufficient for this week to supply just the data for your strain. We will do the comparison with wild type at a later date.
  • 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 "STRAIN)_AvgLogFC_(TIME)" in step 3 of the ANOVA analysis. Does NSR1 change expression due to cold shock in this experiment?
  • 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?

Summary Paragraph

  • Write a summary paragraph that gives the conclusions from this week's analysis.

Shared Journal Assignment

  • Store your journal entry in the shared Class Journal Week 8 page. If this page does not exist yet, go ahead and create it (congratulations on getting in first 👏🏼)
  • Link to your journal entry from your user page.
  • Link back from the journal entry to your user page.
    • NOTE: You can easily fulfill the links part of these instructions by adding them to your template and using the template on your user page.
  • Sign your portion of the journal with the standard wiki signature shortcut (~~~~).
  • Add the "Journal Entry" and "Shared" categories to the end of the wiki page (if someone has not already done so).

View

Now that you've done your own microarray data analysis, we will revisit the case "Deception at Duke".

Reflect

  1. What were the main issues with the data and analysis identified by Baggerly and Coombs? What best practices enumerated by DataONE were violated? Which of these did Dr. Baggerly claim were common issues?
  2. What recommendations does Dr. Baggerly recommend for reproducible research? How do these correspond to what DataONE recommends?
  3. What best practices did you perform for this week's assignment?
  4. Do you have any further reaction to this case after viewing Dr. Baggerly's talk?
Retrieved from "https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php?title=Week_8&oldid=3364"