# Natalie Williams Week 11

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## Week 11 Assignment

Partner: Karina
Time Stamps

• T15 = 4
• T30 = 4
• T60 = 4
• T90 = 4
• T120 = 4

Genes: 6189

Anova = is the gene changing at all; narrow down to do T test and find out which time point it was

1. Followed the statistical analysis part 1
1. Copy and paste instructions of the ANOVA test
2. Downloaded the file/information from the Dahlquist Lab

Followed instructions from the Microarry Data in Week 11's Assignment BIOL398-04/S15:Week 11

### Part 1: Statistical Analysis: ANOVA

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)

I created my own columns that held information regarding p-values in the Bonferroni column. I created columns next to the s2 that would count the p-values below 0.05. I did this by typing in the formula:

=COUNTIF(S3,"<0.05")


From there, I created a single cell that would count the values that were not equal to zero. I took the sum of all the values in the column:

=SUM(T2:T6190)

##### 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.

### Sanity Check: P-Values

P-Values

• How many genes have p < 0.05? and what is the percentage (out of 6189)?
• 2262 genes had a value less than 0.05. It is 36.55%.
• How many genes have p < 0.01? and what is the percentage (out of 6189)?
• 1400 genes had a p-value less than 0.01, which is 22.62%.
• How many genes have p < 0.001? and what is the percentage (out of 6189)?
• 684 genes were less than 0.001 with a percentage of 11.05%.
• How many genes have p < 0.0001? and what is the percentage (out of 6189)?
• 314 genes had p less than 0.0001. Its percentage was 5.073%.

P-Values of Bonferroni and Benjamini and Hochberg

• How many genes are p < 0.05 for the Bonferroni-corrected p value? and what is the percentage (out of 6189)?
• There were 145 genes with a p-value below 0.05. Its percentage was 2.343%.
• How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?
• 1501 genes with values less than 0.05 were seen with the B&H corrected values, which is 24.25%.

#### NSR1 Gene

• The unadjusted p-value
• 1.211E-05
• The Bonferroni-corrected p-value
• 0.07498
• The Benjamini & Hochberg-corrected p-value
• 0.0004628

The average log fold change for each time point is as follows:

• T15: 2.061
• T30: 2.136
• T60: 2.316
• T90: -0.9585
• T120: -0.2175

Karina and I put our p-values on a powerpoint for comparison of the WT of S. cerevisiae and S. paradoxus. The document can be found here

### STEM

#### Instructions

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 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 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!
5. Analyzing and Interpreting STEM Results
1. Select one of the profiles you saved in the previous step for further intepretation of the data. We suggest that you choose one that has a pattern of up- or down-regulated genes at the early (first three) timepoints. Answer the following:
• Why did you select this profile? In other words, why was it interesting to you?
• How many genes belong to this profile?
• How many genes were expected to belong to this profile?
• What is the p value for the enrichment of genes in this profile? Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
• 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? 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?
• Select 10 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05). Look up the definitions for each of the terms at http://geneontology.org. Write a paragraph that describes the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms?
• To easily look up the definitions, go to http://geneontology.org.
• Copy and paste the GO ID (e.g. GO:0044848) into the search field at the upper left of the page called "Search GO Data".
• In the results page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
• The definition will be on the next results page, e.g. here.

#### Analyzing Results

1. Why did you select this profile? In other words, why was it interesting to you?
• I chose Profile#45 because of its great initial activation and then during recovery time, it dipped below its average expression and became down regulated
2. How many genes belong to this profile?
• 386.0 genes were assigned to this profile
3. How many genes were expected to belong to this profile?
• 37.1 genes were expected to be in this profile
4. What is the p value for the enrichment of genes in this profile?
• The p-value for the enrichment of genes is 1.6E-264 (which is very significant)

P-Value Analysis by looking at the GOlist

1. How many GO terms are associated with this profile at p < 0.05?
• 204 genes have p <0.05
2. How many GO terms are associated with this profile with a corrected p value < 0.05?
• 25 genes have corrected p-values less than 0.05
3. 10 Gene Ontology Terms:
1. RNA binding (GO:0003723): Interacting selectively and non-covalently with an RNA molecule or a portion of it
2. Hexose transport (GO:0008645): The directed movement of hexose into, out of or within a cell, or between cells, by means of some agent such as a transporter or pore. Hexoses are any aldoses with a chain of six carbon atoms in the molecule
3. Macromolecule metabolic process (GO:0043170): The chemical reactions and pathways involving macromolecules, any molecule of high relative molecular mass, the structure of which essentially comprises the multiple repetition of units derived, actually or conceptually, from molecules of low relative molecular mass
4. Preribosome, large subunit precursor (GO:0030687): A preribosomal complex consisting of 27SA, 27SB, and/or 7S pre-rRNA, 5S rRNA, ribosomal proteins including late-associating large subunit proteins, and associated proteins; a precursor of the eukaryotic cytoplasmic large ribosomal subunit
• Preribosome: Any complex of pre-rRNAs, ribosomal proteins, and associated proteins formed during ribosome biogenesis
5. maturation of 5.8S rRNA (GO:0000460): Any process involved in the maturation of a precursor 5.8S ribosomal RNA (rRNA) molecule into a mature 5.8S rRNA molecule
6. nucleolar part (GO:0044452): Any constituent part of a nucleolus, a small, dense body one or more of which are present in the nucleus of eukaryotic cells. It is rich in RNA and protein, is not bounded by a limiting membrane, and is not seen during mitosis
7. nucleoplasm (GO:0005654): That part of the nuclear content other than the chromosomes or the nucleolus
8. RNA modification (GO:0009451): The covalent alteration of one or more nucleotides within an RNA molecule to produce an RNA molecule with a sequence that differs from that coded genetically
9. non-membrane-bounded organelle (GO:0043228): Organized structure of distinctive morphology and function, not bounded by a lipid bilayer membrane. Includes ribosomes, the cytoskeleton and chromosomes
10. nucleic acid binding (GO:0003676): Interacting selectively and non-covalently with any nucleic acid

#### Conclusions

The cell must change all of its functions when it endures environmental stresses, in this case cold shock. Certain functions in the cells must be altered so that the cell can survive. These processes, the genes that code for them, and the proteins that carry out these functions must change as a response to cold shock. The cell must alter its processes at the most fundamental level - transcription of RNA and using the resulting RNA to code for proteins that will enable its survival under cold shock. Any GO term that deals with RNA, transcription, translation (large ribosomal subunit), or reading nucleic acid is part of the first few steps needed for the cell to cope with cold shock. Nucleic acids as well as nucleoplasm and nucleolar part deal with RNA and DNA interactions. Certain parts of the DNA will be transcribed to RNA and the nucleolus compose parts in the cell. The macromolecular metabolic process involves the creation as well as the breakdown of macromolecules which are made up of monomers. These monomers are nucleotides, monosaccharides, and amino acids that make up RNA, DNA, complex sugars, and proteins. The cell must therefore change individual units and items, which ultimately alters the composition of molecules in the cell as well as the functions and approaches the cell has for its survival.

To see the files used for this Week's Assignment:
Media:Williams Spar Results.zip

To see my Class Journal Response

Back to User Page: User:Natalie Williams
To view the Course and Assignments:BIOL398-04/S15