Zvanysse Week 8

Homework Assignment

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 "dGLN3_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 dGLN3_AvgLogFC_(TIME) where (TIME) is 15, 30, etc.
In the cell below the dGLN3_AvgLogFC_t15 header, type =AVERAGE(
Then highlight all the data in row 2 associated with dGLN3 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 dGLN3_AvgLogFC_t120 calculation, create the column header dGLN3_ss_HO.
In the first cell below this header, type =SUMSQ(
Highlight all the LogFC data in row 2 for dGLN3 (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 dGLN3_ss_HO, create the column headers dGLN3_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 dGLN3_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 dGLN3_ss_t120, create the column header dGLN3_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 dGLN3_Fstat and dGLN3_p-value.
Recall the number of data points from (13): call that total n.
In the first cell of the dGLN3_Fstat column, type =((n-5)/5)*(<dGLN3_ss_HO>-<(STRAIN)_SS_full>)/<dGLN3_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 dGLN3_ss_HO with the cell designation.
- Replace the phrase <dGLN3_SS_full> with the cell designation.
- Copy to the whole column.
In the first cell below the dGLN3_p-value header, type =FDIST(<dGLN3_Fstat>,5,n-5) replacing the phrase <dGLN3_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 dGLN3_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, dGLN3_Bonferroni_p-value.
Type the equation =<dGLN3_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 (STRAIN)_Bonferroni_p-value header: =IF(dGLN3_Bonferroni_p-value>1,1,dGLN3_Bonferroni_p-value), where "dGLN3_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 "dGLN3_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 dGLN3_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 "STRAIN_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.

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 dGLN3_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.

Uploaded Zip file

dGLN3 zip file
DGLN3 Data Powerpoint Slide

P-Value Computations

Genes with p < 0.05 = 2135 out of 6189 records. This is 34.50% of the data.
Genes with a p < 0.01 = 1204 out of 6189 records. This is 19.45% of the data.
Genes with a p < 0.001 = 514 out of 6189 records. This is 8.31% of the data.
Genes with a p < 0.0001 = 180 out of 6189 records. This is 2.91% of the data.

Bonferroni Corrected P-Value Computations

Genes with a p < 0.05 for the Bonferroni-corrected p-value = 45 out of 6189 records. This is 0.73% of the data.
Genes with a p < 0.05 for the Benjamini and Hochber-corrected p-value = 1185 out of 6189 records. This is 19.15% of the data.

NSR1 Computations

Unadjusted P - Value = 0.000506
Bonferroni - Corrected P - Value = 3.13616
B-H corrected P - value = 0.008167
Average Log Fold:
1. 3.50622
2. 4.53189
3. 2.75921
4. -1.85027
5. -1.86741
From the Average Log Fold, we see that the gene expression increased in the beginning, then decreases and bottomed out at the last two periods.

BRO1 Gene

Unadjusted P-Value = 0.40148
Bonferroni Corrected = 0
B-H corrected P-Value = 0.567043
Average Log Fold:
1. -0.31699
2. -0.03240
3. -0.34396
4. 0.98111
5. 0.34635
From the Average Log Fold, we see a slight increase in gene expression as the Yeast cell experiences cold shock over a long time period.

Summary Paragraph

As we can see from the data, the lower P value significance level, the lower the amount of genes that satisfy the parameters. From the data collected, a series of basic statistical analyses were performed to see what would happen to cold related Osmotic Shock on Yeast cells. From the analysis, we are able to tell how each gene expression responded.

Electronic Workbook

From this assignment, we learned many basic Excel functions and manipulation tools to make the analyses easier. First, we were able to select all the data clicking the button in the top left corner. We also learned how to copy and paste a function for the whole column quickly by double clicking the bottom corner of our selected cell. We also learned how to select certain data. This was very useful considering that the excel spreadsheet consisted of over 6100 rows of pure raw data. We learned how efficient this process is and understand how excel and computers has accelerated the scientific community regarding data analysis. As for the actual computations, we found the average time for each time period to find the most accurate data. We found the P-Values for the gene expression and limited the significance to certain levels to determine the best data to perform the analyses on. We used computations stemming from both Bonferroni and Benjamini/ Hochberg. from these computations we could find trends in the genomic data which will hep us to understand what exactly is going on much better. We see this as we took the information from NSR1 and our favorite gene (for me, BRO1) and made assumptions based on the data that we found such as expressions increasing or decreasing.

Acknowledgements

I worked with Dina on figuring out how to utilize Excel
I consulted with Dr. Dionisio to figure out some of the specifications of the assignment

While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
Zvanysse (talk) 13:29, 23 October 2017 (PDT)

References

LMU BioDB 2017. (2017). Week 8. Retrieved October 19, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_8

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Zvanysse

BIOL/CMSI 367-01: Biological Databases Fall 2017

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Zvanysse Week 8

Contents

Homework Assignment

Part 1: Statistical Analysis Part 1

Calculate the Bonferroni and p value Correction

Calculate the Benjamini & Hochberg p value Correction

Sanity Check: Number of genes significantly changed

Summary Paragraph

Uploaded Zip file

P-Value Computations

Bonferroni Corrected P-Value Computations

NSR1 Computations

BRO1 Gene

Summary Paragraph

Electronic Workbook

Acknowledgements

References

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Individual Assignments

Shared Journals

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