Cazinge Week 8

Electronic Notebook

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.

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

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.
- ANSWER: wild type data (wt); BIOL367_Fall2017_Dahlquist-microarray-data-master_20171017_EA.xlsx; (Timepoint=#replicates) wt: t15=4, t30=5, t60=4, t90=5, t120=5
NOTE: before beginning any analysis, immediately change the filename so that it contains your initials to distinguish it from other students' work.
- ANSWER: Changed to include my initials.
The first thing you will do is to delete the data columns of all strains other than those prefixed with wt.
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.
  - ANSWER: 4071 instances replaced
- 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.

Create a new worksheet, naming it either "wt_ANOVA" as appropriate. For example, you might call yours "wt_ANOVA" or "dHAP4_ANOVA"
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.
At the top of the first column to the right of your data, create five column headers of the form wt_AvgLogFC_(TIME) where wt is your strain designation and (TIME) is 15, 30, etc.
In the cell below the wt_AvgLogFC_t15 header, type =AVERAGE(
Then highlight all the data in row 2 associated with wt and t15, press the closing paren key (shift 0),and press the "enter" key.
This cell now contains the average of the log fold change data from the first gene at t=15 minutes.
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.
Repeat steps (4) through (8) with the t30, t60, t90, and the t120 data.
Now in the first empty column to the right of the wt_AvgLogFC_t120 calculation, create the column header wt_ss_HO.
In the first cell below this header, type =SUMSQ(
Highlight all the LogFC data in row 2 for your wt (but not the AvgLogFC), press the closing paren key (shift 0),and press the "enter" key.
In the next empty column to the right of wt_ss_HO, create the column headers wt_ss_(TIME) as in (3).
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).
In the first cell below the header wt_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.
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.
In the first column to the right of wt_ss_t120, create the column header wt_SS_full.
In the first row below this header, type =sum(<range of cells containing "ss" for each timepoint>) and hit enter.
In the next two columns to the right, create the headers wt_Fstat and wt_p-value.
Recall the number of data points from (13): call that total n.
In the first cell of the wt_Fstat column, type =((n-5)/5)*(<wt_ss_HO>-<wt_SS_full>)/<wt_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 wt_ss_HO with the cell designation.
- Replace the phrase <wt_SS_full> with the cell designation.
- Copy to the whole column.
In the first cell below the wt_p-value header, type =FDIST(<wt_Fstat>,5,n-5) replacing the phrase <wt_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.
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 wt_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

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, wt_Bonferroni_p-value.
Type the equation =<wt_p-value>*6189, Upon completion of this single computation, use the Step (10) trick to copy the formula throughout the column.
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 wt_Bonferroni_p-value header: =IF(wt_Bonferroni_p-value>1,1,wt_Bonferroni_p-value), where "wt_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

Insert a new worksheet named "wt_ANOVA_B-H".
Copy and paste the "MasterIndex", "ID", and "Standard Name" columns from your previous worksheet into the first two columns of the new worksheet.
For the following, use Paste special > Paste values. Copy your unadjusted p values from your ANOVA worksheet and paste it into Column D.
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.
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.
Now you can calculate the Benjamini and Hochberg p value correction. Type wt_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.
Type "wt_B-H_p-value" into cell G1.
Type the following formula into cell G2: =IF(F2>1,1,F2) and press enter. Copy that equation to the entire column.
Select columns A through G. Now sort them by your MasterIndex in Column A in ascending order.
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.
- ANSWER: Media:BIOL367_Fall2017_Dahlquist-microarray-data-master_20171017_EA.zip

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 wt_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)?
  - ANSWER: 2528, 40.8466%
- How many genes have p < 0.01? and what is the percentage (out of 6189)?
  - ANSWER: 1652, 26.6925%
- How many genes have p < 0.001? and what is the percentage (out of 6189)?
  - ANSWER: 919, 14.8489%
- How many genes have p < 0.0001? and what is the percentage (out of 6189)?
  - ANSWER: 496, 8.0142%
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)?
  - ANSWER: 248, 4.0071%
- How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?
  - ANSWER: 2006, 32.4123%
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.
- ANSWER: Media:Wt_p-value_slide_EA.pptx
- 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?
- ANSWER: Unadjusted: 2.8690e-10; Bonferroni-corrected: 1.77563e-6; B-H-corrected: 7.52066e-10; t15=3.27923, t30=3.621, t60=3.5265, t90=-2.0498, t120=-0.6062; Yes, NSR1 changes expression due to cold shock between times t60 and t90. It becomes expressed as it's Log2FC ratio changes from positive to negative.
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?
- ANSWER: Unadjusted: .3616; Bonferroni-corrected: 1; B-H-corrected: 0.4868; t15=0.4699, t30=-0.4585, t60=-0.3564, t90=0.9098, t120=-0.1191; Yes, NSR1 changes expression due to cold shock between times t60 and t90. It becomes slightly repressed as it's Log2FC ratio changes from negative to positive.

Summary Paragraph

In this experiment, we analyzed a DNA microarray dataset from Dahlquist's wetlab by preforming a number of transformations to the data. After averaging the data and finding its sum of squares, we were able to extrabolate an F-stat and derive a p-value for each of the genes of the wild type strain's data. After this process, we recieved p-values for which 40% of the dataset had p < .05. In order to correct this, we preformed a Bonferrani p-value correction, and a Benjamin & Hochberg p-value correction, the first of which returned 4% of our genes as having p-values under .05, and the latter, which returned 32% of our dataset as having p-values underneath .05. Finally, we analyzed NSR1 and CLN1, which were expressed and repressed by cold shock respectively.

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 8 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)