Vpachec3 Week8

ALL FILES

File:Bdvp20151025-Criterion0-GO.txt There is a new version of this file with the correct Criterion! SEE OCTOBER 26 SECTION

File:GO table.xlsx

File:Merrell Compiled Raw Data Vibrio VP 20151020.gmf

Statistical Analysis of Vibrio cholerae Microarray Data (Part 1)

Downloaded the [Merrell_Compiled_Raw_Data_Vibrio.xls] file.
- Renamed the file to Merrell_Compiled_Raw_Data_Vibrio_BK_20151015.xls

Normalizing the log ratios for the set of slides in the experiment

Copied data from the compiled_raw_data tab to a new tab, entitled scaled_centered
- Inserted two rows below the ID row, labelled Average and StDev.
  - Average: Calculated the averages of the data in each column using the function =AVERAGE()
    - Sample- for row B the function is =AVERAGE(B4:B5224)
  - StDev: Calculated the standard deviations of the data in each column using the function =STDEV()
    - Sample- for row B the function is =STDEV(B4:B5224)
- Created a new series of columns for data sets A1-C4, adding the labels "_scaled_centered" (e.g. A1_scaled_centered)
  - Wrote a function to normalize each value in the data set
    - For the first value in column B (data set A1), the function was =(B4-B$2)/B$3
    - Extended this to all data values in the new scaled_centered columns

Created a new tab entitled statistics
- Copied over:
  - The first column of gene ID values from the compiled_raw_data tab
  - The _scaled_centered columns from the scaled_centered tab
- Deleted the blank Average and StDev rows
- Created a new series of columns

Before we begin...

The data from the Merrell et al. (2002) paper was accessed from this page at the Stanford Microarray Database (now hosted by Princeton — Kam D. Dahlquist 18:26, 7 October 2013 (EDT).
The Log₂ of R/G Normalized Ratio (Median) has been copied from the raw data files downloaded from the Stanford Microarray Database.
- Patient A
  - Sample 1: 24047.xls (A1)
  - Sample 2: 24048.xls (A2)
  - Sample 3: 24213.xls (A3)
  - Sample 4: 24202.xls (A4)
- Patient B
  - Sample 5: 24049.xls (B1)
  - Sample 6: 24050.xls (B2)
  - Sample 7: 24203.xls (B3)
  - Sample 8: 24204.xls (B4)
- Patient C
  - Sample 9: 24053.xls (C1)
  - Sample 10: 24054.xls (C2)
  - Sample 11: 24205.xls (C3)
  - Sample 12: 24206.xls (C4)
- Stationary Samples (We will not be using these, they are listed here for completeness, but do not appear in your compiled raw data file.)
  - Sample 13: 24059.xls (Stationary-1)
  - Sample 14: 24060.xls (Stationary-2)
  - Sample 15: 24211.xls (Stationary-3)
  - Sample 16: 24212.xls (Stationary-4)
Downloaded the Merrell_Compiled_Raw_Data_Vibrio.xls file to your Desktop.
- Saved a copy of the file with a different filename that includes your initials and the date. For example, I would call mine "Merrell_Compiled_Raw_Data_Vibrio_KD_20091020.xls".

Normalize the log ratios for the set of slides in the experiment

To scale and center the data (between chip normalization) perform the following operations:

Inserted a new Worksheet into your Excel file, and name it "scaled_centered".
Went to the "compiled_raw_data" worksheet, Select All and Copy. Went to new "scaled_centered" worksheet, click on the upper, left-hand cell (cell A1) and Paste.
Inserted two rows in between the top row of headers and the first data row.
In cell A2, typed "Average" and in cell A3, typed "StdDev".
Computed the Average log ratio for each chip (each column of data). In cell B2, type the following equation:

=AVERAGE(B4:B5224)

and pressed "Enter". Excel is computing the average value of the cells specified in the range given inside the parentheses. Instead of typing the cell designations, you can click on the beginning cell, scroll down to the bottom of the worksheet, and shift-click on the ending cell.

Computed the Standard Deviation of the log ratios on each chip (each column of data). In cell B3, type the following equation:

=STDEV(B4:B5224)

and press "Enter".

Copied these two equations (cells B2 and B3) and pasted them into the empty cells in the rest of the columns. Excel will automatically change the equation to match the cell designations for those columns.
Copied the column headings for all of your data columns and then pasted them to the right of the last data column so that you have a second set of headers above blank columns of cells. Edited the names of the columns so that they read: A1_scaled_centered, A2_scaled_centered, etc.
In cell N4, typed the following equation:

=(B4-B$2)/B$3

In this case, we wanted the data in cell B4 to have the average subtracted from it (cell B2) and be divided by the standard deviation (cell B3). We used the dollar sign symbols in front of the "2" and "3" to tell Excel to always reference that row in the equation, even though we pasted it for the entire column of 5221 genes. Why is this important?

Copied and pasted this equation into the entire column. Clicked on the original cell with your equation and position the cursor at the bottom right corner. Cursor changed to a thin black plus sign (not a chubby white one).

Double clicked, and the formula was copied to the entire column of genes.

Copied and pasted the scaling and centering equation for each of the columns of data with the "_scaled_centered" column header. Be sure that your equation is correct for the column you are calculating.

Perform statistical analysis on the ratios

We are going to perform this step on the scaled and centered data you produced in the previous step.

Insert a new worksheet and name it "statistics".
Go back to the "scaling_centering" worksheet and copy the first column ("ID").
Paste the data into the first column of your new "statistics" worksheet.
Go back to the "scaling_centering" worksheet and copy the columns that are designated "_scaled_centered".
Go to your new worksheet and click on the B1 cell. Select "Paste Special" from the Edit menu. A window will open: click on the radio button for "Values" and click OK. This will paste the numerical result into your new worksheet instead of the equation which must make calculations on the fly.
Delete Rows 2 and 3 where it says "Average" and "StDev" so that your data rows with gene IDs are immediately below the header row 1.
Go to a new column on the right of your worksheet. Type the header "Avg_LogFC_A", "Avg_LogFC_B", and "Avg_LogFC_C" into the top cell of the next three columns.
Compute the average log fold change for the replicates for each patient by typing the equation:

=AVERAGE(B2:E2)

into cell N2. Copy this equation and paste it into the rest of the column.

Create the equation for patients B and C and paste it into their respective columns.
Now you will compute the average of the averages. Type the header "Avg_LogFC_all" into the first cell in the next empty column. Create the equation that will compute the average of the three previous averages you calculated and paste it into this entire column.
Insert a new column next to the "Avg_LogFC_all" column that you computed in the previous step. Label the column "Tstat". This will compute a T statistic that tells us whether the scaled and centered average log ratio is significantly different than 0 (no change). Enter the equation:

=AVERAGE(N2:P2)/(STDEV(N2:P2)/SQRT(number of replicates))

(NOTE: in this case the number of replicates is 3. Be careful that you are using the correct number of parentheses.) Copy the equation and paste it into all rows in that column.

Label the top cell in the next column "Pvalue". In the cell below the label, enter the equation:

=TDIST(ABS(R2),degrees of freedom,2)

The number of degrees of freedom is the number of replicates minus one, so in our case there are 2 degrees of freedom. Copy the equation and paste it into all rows in that column.

Calculate the Bonferroni 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, Bonferroni_Pvalue.
Type the equation =S2*5221, Upon completion of this single computation, use the 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 Bonferroni_Pvalue header: =IF(T2>1,1,T2). Use the trick to copy the formula throughout the column.

Calculate the Benjamini & Hochberg p value Correction

Insert a new worksheet named "B-H_Pvalue".
Copy and paste the "ID" column from your previous worksheet into the first column of the new worksheet.
Insert a new column on the very left and name it "MasterIndex". We will create a numerical index of genes so that we can always sort them back into the same order.
- Type a "1" in cell A2 and a "2" in cell A3.
- Select both cells. Hover your mouse over the bottom-right corner of the selection until it makes a thin black + sign. Double-click on the + sign to fill the entire column with a series of numbers from 1 to 5221 (the number of genes on the microarray).
For the following, use Paste special > Paste values. Copy your unadjusted p values from your previous worksheet and paste it into Column C.
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.
Type the header "Rank" in cell D1. We will create a series of numbers in ascending order from 1 to 5221 in this column. This is the p value rank, smallest to largest. Type "1" into cell D2 and "2" into cell D3. Select both cells D2 and D3. 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 5221.
Now you can calculate the Benjamini and Hochberg p value correction. Type B-H_Pvalue in cell E1. Type the following formula in cell E2: =(C2*5221)/D2 and press enter. Copy that equation to the entire column.
Type "B-H_Pvalue" into cell F1.
Type the following formula into cell F2: =IF(E2>1,1,E2) and press enter. Copy that equation to the entire column.
Select columns A through F. Now sort them by your MasterIndex in Column A in ascending order.
Copy column F and use Paste special > Paste values to paste it into the next column on the right of your "statistics" sheet.

Prepare file for GenMAPP

Insert a new worksheet and name it "forGenMAPP".
Go back to the "statistics" worksheet and Select All and Copy.
Go to your new sheet and click on cell A1 and select Paste Special, click on the Values radio button, and click OK. We will now format this worksheet for import into GenMAPP.
Select Columns B through Q (all the fold changes). Select the menu item Format > Cells. Under the number tab, select 2 decimal places. Click OK.
Select all the columns containing p values. Select the menu item Format > Cells. Under the number tab, select 4 decimal places. Click OK.
Delete the left-most Bonferroni p value column, preserving the one that shows the result of your "if" statement.
Insert a column to the right of the "ID" column. Type the header "SystemCode" into the top cell of this column. Fill the entire column (each cell) with the letter "N".
Select the menu item File > Save As, and choose "Text (Tab-delimited) (*.txt)" from the file type drop-down menu. Excel will make you click through a couple of warnings because it doesn't like you going all independent and choosing a different file type than the native .xls. This is OK. Your new *.txt file is now ready for import into GenMAPP. But before we do that, we want to know a few things about our data as shown in the next section.
- Upload both the .xls and .txt files that you have just created to your journal page in the class wiki. Make sure that your file name is distinct from your other classmates so that nobody overwrites anyone else's file.

Sanity Check: Number of genes significantly changed

Before we move on to the GenMAPP/MAPPFinder analysis, we want to perform a 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 and also compare our data analysis with the published results of Merrell et al. (2002).

Open your spreadsheet and go to the "forGenMAPP" tab.
Click on cell A1 and select the menu item Data > Filter > Autofilter. 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 "Pvalue" column. Select "Custom". In the window that appears, set a criterion that will filter your data so that the Pvalue has to be less than 0.05.
- How many genes have p value < 0.05? and what is the percentage (out of 5221)?
- What about p < 0.01? and what is the percentage (out of 5221)?
- What about p < 0.001? and what is the percentage (out of 5221)?
- What about p < 0.0001? and what is the percentage (out of 5221)?

948 (18.2%) genes have a p value < 0.05

235 (4.5%) genes have a p value < 0.01

24 (0.5%) genes have a p value < 0.001

0 (0%) genes have a p value < 0.0001

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 less than 5% of the time.
We have just performed 5221 T tests for significance. Another way to state what we are seeing with p < 0.05 is that we would expect to see this magnitude of a gene expression change in about 5% of our T tests, or 261 times. (Test your understanding: http://xkcd.com/882/.) Since we have more than 261 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 5221)?
- How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 5221)?

0 (0%) genes have a Bonferroni-corrected p value < 0.05

0 (0%) genes have a Benjamini and Hochberg-corrected p value < 0.05

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.
The "Avg_LogFC_all" tells us the size of the gene expression change and in which direction. Positive values are increases relative to the control; negative values are decreases relative to the control.
- Keeping the (unadjusted) "Pvalue" filter at p < 0.05, filter the "Avg_LogFC_all" column to show all genes with an average log fold change greater than zero. How many are there? (and %)
- Keeping the (unadjusted) "Pvalue" filter at p < 0.05, filter the "Avg_LogFC_all" column to show all genes with an average log fold change less than zero. How many are there? (and %)
- What about an average log fold change of > 0.25 and p < 0.05? (and %)
- Or an average log fold change of < -0.25 and p < 0.05? (and %) (These are more realistic values for the fold change cut-offs because it represents about a 20% fold change which is about the level of detection of this technology.)

352 (6.7%) genes have a positive average log fold change

596 (11.4%) genes have a negative average log fold change

339 (6.4%) genes have an significant positive average log fold change

578 (11.1%) genes have an significant negative average log fold change

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. For the GenMAPP analysis below, we will use the fold change cut-off of greater than 0.25 or less than -0.25 and the unadjusted p value cut off of p < 0.05 for our analysis because we want to include several hundred genes in our analysis.
What criteria did Merrell et al. (2002) use to determine a significant gene expression change? How does it compare to our method?

In the article, it says that Merrell used SAM,Statistical Analysis for Microarrays. The used 2 fold changes as their determining method while we used pvalues for our determining method.

Sanity Check: Compare individual genes with known data

Merrell et al. (2002) report that genes with IDs: VC0028, VC0941, VC0869, VC0051, VC0647, VC0468, VC2350, and VCA0583 were all significantly changed in their data. Look these genes up in your spreadsheet. What are their fold changes and p values? Are they significantly changed in our analysis?

VC0028

Fold Change:1.65, 1.27

P-Value: first entry = 0.0474, 0.0692

Significance: statistically significant, not statistically significant

VC0941

Fold Change:0.09, -0.28

P-Value: 0.6759, 0.1636

Significance:not statistically significant, not statistically significant

VC0869

Fold Change :1.59, 1.95, 2.20, 1.50, 2.12

P-Value:0.0463,0.0227,0.0020,0.0174,0.0200

Significance:significant,significant,significant,significant,significant

VC0051

Fold Change:1.92, 1.89

P-Value:0.0139,0.0160

Significance:statistically significant,statistically significant

VC0468

Fold Change: -0.17

P-Value: 0.3350

Significance: not statistically significant

VC2350

Fold Change: -2.40

P-Value: 0.0130

Significance: statistically significant

VCA0583

Fold Change: 1.06

P-Value: 0.1011

Significance: not statistically significant

MAPPFinder Analysis of Vibrio cholerae Microarray Data (Part 2)

Map Onto Biological Pathways (GenMAPP & MAPPFinder)

Fall 2015: Beginning point for class on Tuesday, October 20 as part of the Week 8 journal assignment.

Fall 2013: Beginning point for class on Tuesday, October 15 as part of the Week 8 journal assignment.

Fall 2010: Beginning point for class on Tuesday, October 26 and for the Week 9 journal assignment.

Each time you launch GenMAPP, you need to make sure that the correct Gene Database (.gdb) is loaded.

Look in the lower left-hand corner of the window to see which Gene Database has been selected.
If you need to change the Gene Database, select Data > Choose Gene Database. Navigate to the directory C:\GenMAPP 2 Data\Gene Databases and choose the correct one for your species.
For the exercise today, you will need to download the appropriate Vibrio cholerae Gene Database.
- Half of the class will use the Vc-Std_External_20090622.gdb Gene Database that was initially created by the Fall 2008 Biological Databases class.
  - To download this Gene Database, follow this link to the XMLPipeDB SourceForge Download page.
  - NOTE: I got the 2010 file and Brandon got the 2009
- Half of the class will use a new Vc-Std_External_20101022.gdb Gene Database that was created by Drs. Dahlquist and Dionisio a year later.
  - To download this Gene Database, follow this link to the XMLPipeDB SourceForge Download page.
- The members of a pair should each choose a different gene database.
Click on the link for the Gene Database to which you have been assigned, download the file, and save it into the folder C:\GenMAPP 2 Data\Gene Databases, and extract it.

GenMAPP Expression Dataset Manager Procedure

Launch the GenMAPP Program. Check to make sure the correct Gene Database is loaded.
- Look in the lower, left-hand corner of the main GenMAPP Drafting Board window to see the name of the Gene Database that is loaded. If this is not the correct Gene Database or it says "No Gene Database", then go to the Data > Choose Gene Database menu item to select the Gene Database you need to perform the analysis.
- Remember, you and your partner are going to use different versions of the Vibrio cholerae Gene Database for this exercise.
Select the Data menu from the main Drafting Board window and choose Expression Dataset Manager from the drop-down list. The Expression Dataset Manager window will open.
Select New Dataset from the Expression Datasets menu. Select the tab-delimited text file that you formatted for GenMAPP (.txt) in the procedure above from the file dialog box that appears.
- You may need to download your .txt file from the wiki onto your Desktop if you have not already done so.
The Data Type Specification window will appear. GenMAPP is expecting that you are providing numerical data. If any of your columns has text (character) data, you would check the box next to the field (column) name.
- The Vibrio data we have been working with does not have any text (character) data in it.
Allow the Expression Dataset Manager to convert your data.
- This may take a few minutes depending on the size of the dataset and the computer’s memory and processor speed. When the process is complete, the converted dataset will be active in the Expression Dataset Manager window and the file will be saved in the same folder the raw data file was in, named the same except with a .gex extension; for example, MyExperiment.gex.

- A message may appear saying that the Expression Dataset Manager could not convert one or more lines of data. Lines that generate an error during the conversion of a raw data file are not added to the Expression Dataset. Instead, an exception file is created. The exception file is given the same name as your raw data file with .EX before the extension (e.g., MyExperiment.EX.txt). The exception file will contain all of your raw data, with the addition of a column named ~Error~. This column contains either error messages or, if the program finds no errors, a single space character.
  - Record the number of errors. For your journal assignment, open the .EX.txt file and use the Data > Filter > Autofilter function to determine what the errors were for the rows that were not converted. Record this information in your individual journal page.
  - It is likely that you will have a different number of errors than your partner who is using a different version of the Vibrio cholerae Gene Database. Which of you has more errors? Why do you think that is? Record your answers in your journal page.

I had 121 errors while Brandon had 772 errors for his file. At 10:18, Brandon checked his error number by copying the file to into the command line. He looked up that particular command on the web and it was

scp/Users/brandonklein/Desktop/Merrell_Compiled_Raw_Data_Vibrio_BK_20151015.EXT.txt bklein7@my.cs.lmu.ed:~bklein7

This enabled a search for the error in the file. This command we learned earlier which is wc. The number came out to 772. This confirms that the numbers of errors is correct.

I think Brandon's had more because his version of the data was older, only by a year but it is clear that is makes a big difference. Renaming genes or new information on genes can all affect the categorization which in turn will effect the number of errors found in the file.

Upload your exceptions file: EX.txt to your wiki page.
Customize the new Expression Dataset by creating new Color Sets which contain the instructions to GenMAPP for displaying data on MAPPs.
- Color Sets contain the instructions to GenMAPP for displaying data from an Expression Dataset on MAPPs. Create a Color Set by filling in the following different fields in the Color Set area of the Expression Dataset Manager: a name for the Color Set, the gene value, and the criteria that determine how a gene object is colored on the MAPP. Enter a name in the Color Set Name field that is 20 characters or fewer.
- The Gene Value is the data displayed next to the gene box on a MAPP. Select the column of data to be used as the Gene Value from the drop down list or select [none]. We will use "Avg_LogFC_all" for the Vibrio dataset you just created.
- Activate the Criteria Builder by clicking the New button.
- Enter a name for the criterion in the Label in Legend field.
- Choose a color for the criterion by left-clicking on the Color box. Choose a color from the Color window that appears and click OK.
- State the criterion for color-coding a gene in the Criterion field.
  - A criterion is stated with relationships such as "this column greater than this value" or "that column less than or equal to that value". Individual relationships can be combined using as many ANDs and ORs as needed. A typical relationship is

[ColumnName] RelationalOperator Value

with the column name always enclosed in brackets and character values enclosed in single quotes. For example:

[Fold Change] >= 2
[p value] < 0.05
[Quality] = 'high'

This is the equivalent to queries that you performed on the command line when working with the PostgreSQL movie database. GenMAPP is using a graphical user interface (GUI) to help the user format the queries correctly. The easiest and safest way to create criteria is by choosing items from the Columns and Ops (operators) lists shown in the Criteria Builder. The Columns list contains all of the column headings from your Expression Dataset. To choose a column from the list, click on the column heading. It will appear at the location of the cursor in the Criterion box. The Criteria Builder surrounds the column names with brackets.

The Ops (operators) list contains the relational operators that may be used in the criteria: equals ( = ) greater than ( > ), less than ( < ), greater than or equal to ( >= ), less than or equal to ( <= ), is not equal to ( <> ). To choose an operator from the list, click on the symbol. It will appear at the location of the insertion bar (cursor) in the Criterion box. The Criteria Builder automatically surrounds the operators with spaces.

The Ops list also contains the conjunctions AND and OR, which may be used to make compound criteria. For example:

[Fold Change] > 1.2 AND [p value] <= 0.05

Parentheses control the order of evaluation. Anything in parentheses is evaluated first. Parentheses may be nested. For example:

[Control Average] = 100 AND ([Exp1 Average] > 100 OR [Exp2 Average] > 100)

Column names may be used anywhere a value can, for example:

[Control Average] < [Experiment Average]

After completing a new criterion, add the criterion entry (label, criterion, and color) to the Criteria List by clicking the Add button.
- For the Vibrio dataset, you will create two criterion. "Increased" will be [Avg_LogFC_all] > 0.25 AND [Pvalue] < 0.05 and "Decreased will be [Avg_LogFC_all] < -0.25 AND [Pvalue] < 0.05.
- You may continue to add criteria to the Color Set by using the previous steps.
  - The buttons to the right of the list represent actions that can be performed on individual criteria. To modify a criterion label, color, or the criterion itself, first select the criterion in the list by left-clicking on it, and then click the Edit button. This puts the selected criterion into the Criteria Builder to be modified. Click the Save button to save changes to the modified criterion; click the Add button to add it to the list as a separate criterion. To remove a criterion from the list, left-click on the criterion to select it, and then click on the Delete button. The order of Criteria in the list has significance to GenMAPP. When applying an Expression Dataset and Color Set to a MAPP, GenMAPP examines the expression data for a particular gene object and applies the color for the first criterion in the list that is true. Therefore, it is imperative that when criteria overlap the user put the most important or least inclusive criteria in the list first. To change the order of the criteria in the list, left-click on the criterion to select it and then click the Move Up or Move Down buttons. No criteria met and Not found are always the last two positions in the list.
Save the entire Expression Dataset by selecting Save from the Expression Dataset menu. Changes made to a Color Set are not saved until you do this.
Exit the Expression Dataset Manager to view the Color Sets on a MAPP. Choose Exit from the Expression Dataset menu or click the close box in the upper right hand corner of the window.
Upload your .gex file to your journal entry page for later retrieval.

MAPPFinder Procedure

Note: You and your partner will both do the same criterion, either "Increased" or "Decreased", but your group does not need to do both "Increased" and "Decreased" Sign up for the criterion you want on the group list ( Fall 2010 or Fall 2013) so that we can make sure that as a class we are covering both criteria.

Launch the MAPPFinder program (or from within GenMAPP, select Tools > MAPPFinder).
Make sure that the Gene Database for the correct species is loaded. The name of the Gene Database appears at the bottom of the window. If this is not the right one, go to File > Choose Gene Database and choose the correct one. (The Gene Databases are stored in the folder C:\GenMAPP 2 Data\Gene Databases\.)
Click on the button "Calculate New Results".
Click on "Find File" and choose the your Expression Dataset file, for example, "MyDataset.gex", and click OK.
- MAPPFinder may have found it for you already if you already had it open in GenMAPP, in which case, you just need to click OK.
Choose the Color Set and Criteria with which to filter the data. Click on either the "Increased" and "Decreased" criteria in the right-hand box, depending on which one your group is doing. (You could select both by holding down the Control key while clicking).
Check the boxes next to "Gene Ontology" and "p value".
Click the "Browse" button and create a meaningful filename for your results.
Click "Run MAPPFinder". The analysis will take several minutes. It may look like the computer is stalled; be patient, it will eventually start running.
When the results have been calculated, a Gene Ontology browser will open showing your results. All of the Gene Ontology terms that have at least 3 genes measured and a p value of less than 0.05 will be highlighted yellow. A term with a p value less than 0.05 is considered a "significant" result. Browse through the tree to see your results.
To see a list of the most significant Gene Ontology terms, click on the menu item "Show Ranked List".
- List the top 10 Gene Ontology terms in your individual journal entry.
- Compare your list with your partner who used a different version of the Gene Database. Are your terms the same or different? Why do you think that is? Record your answer in your individual journal entry.

Top Ten Gene Ontology Terms

Glucose catabolic process
Hexose catabolic process
Glycolysis
Monosaccharide catabolic process
Cytoplasm
Alcohol catabolic process
Cellular carbohydrate catabolic process
Glucose metabolic process
Protein folding
Hexose metabolic process

One of the things you can do in MAPPFinder is to find the Gene Ontology term(s) with which a particular gene is associated. First, in the main MAPPFinder Browser window, click on the button "Collapse the Tree". Then, you can search for the genes that were mentioned by Merrell et al. (2002), VC0028, VC0941, VC0869, VC0051, VC0647, VC0468, VC2350, and VCA0583. Type the identifier for one of these genes into the MAPPFinder browser gene ID search field. Choose "OrderedLocusNames" from the drop-down menu to the right of the search field. Click on the GeneID Search button. The GO term(s) that are associated with that gene will be highlighted in blue. List the GO terms associated with each of those genes in your individual journal. (Note: they might not all be found.) Are they the same as your partner who is using a different Gene Database? Why or why not?

GO Terms for VC0028

branched chain family amino acid biosynthetic process
cellular amino acid biosynthetic process
metabolic process
metal ion binding
iron-sulfur cluster binding
4 iron, 4 sulfur cluster binding
catalytic activity
lyase activity
dihydroxy-acid dehydratase activity

Click on one of the GO terms that are associated with one of the genes you looked up in the previous step. A MAPP will open listing all of the genes (as boxes) associated with that GO term. The genes named within the map are based on the UniProt identification system. To match the gene of interest to its identification go to the UniProt site and type in your gene ID into the search bar. Moreover, the genes on the MAPP will be color-coded with the gene expression data from the microarray experiment. List in your journal entry the name of the GO term you clicked on and whether the expression of the gene you were looking for changed significantly in the experiment.

I clicked on the GO Term "metal binding" and since on the map there were alot of green squares, where green stood for decreased, there was an overall decreased expression of the gene.

- Double-click on the gene box. This will open a Internet Explorer window called the "Backpage" for this gene. This page has links to pages for this gene in the public databases. Click on the links to find out the function of this gene and record your answer in your individual journal page.

Functions of the Gene

catalytic activity
metalloendopeptidase activity
proteolysis
zinc ion binding
metal ion binding

- The MAPP that has just been created is stored in the directory, C:\GenMAPP 2 Data\MAPPs\VC GO. Upload this file and link to it in your journal.
In Windows, make a copy of your results (XXX-CriterionX-GO.txt) file.
- "XXX" refers to the name you gave to your results file.
- "CriterionX" refers to either "Criterion0" or "Criterion1". Since computers start counting at zero, "Criterion0" is the first criterion in the list you clicked on ("Increased" if you followed the directions) and "Criterion1" is the second criterion in the list you clicked on ("Decreased" if you followed the directions).
- Upload your results file to your journal page.
Launch Microsoft Excel. Open the copies of the .txt files in Excel (you will need to "Show all files" and click "Finish" to the wizard that will open your file). This will show you the same data that you saw in the MAPPFinder Browser, but in tabular form.
Look at the top of the spreadsheet. There are rows of information that give you the background information on how MAPPFinder made the calculations. Compare this information with your partner who used a different version of the Vibrio Gene Database. Which numbers are different? Why are they different? Record this information in your individual journal entry.

Originally, I had a mistake in my spreadsheet which made for a massive number discrepancy between Brandon's and my numbers. However once I fixed the mistake and redid my maps and sheet. It turns out that we have the same number for probes met the criterion and probes in the data set. The other numbers were different but only by a little. For example, in Brandon's 2009 data, there were 1990 genes linked to a GO term and in my 2010 data there are 2475 genes linked to a GO term. The time difference between the sets of data is a major factor in the slightly varying numbers between the two spread sheets. New information and better understanding allows for the changes from year to year.

You will filter this list to show the top GO terms represented in your data for both the "Increased" and "Decreased" criteria. You will need to filter your list down to about 20 terms. Click on a cell in the row of headers for the data. Then go to the Data menu and click "Filter > Autofilter". Drop-down arrows will appear in the row of headers. You can now choose to filter the data. Click on the drop-down arrow for the column you wish to filter and choose "(Custom…)". A window will open giving you choices on how you want to filter. You must set these two filters:

Z Score (in column N) greater than 2
PermuteP (in column O) less than 0.05

You will use these two filters depending on the number of terms you have:

Number Changed (in column I) greater than or equal to 4 or 5 AND less than 100
Percent Changed (in column L) greater than or equal to 25-50%

Save your changes to an Excel spreadsheet. Select File > Save As and select Excel workbook (.xls) from the drop-down menu. Your filter settings won’t be saved in a .txt file.
Are any of your filtered GO terms closely related to one another, meaning are they a direct child or parent to another term in the list? You can judge this by comparing your spreadsheet with the MAPPFinder browser. Highlight the terms that fit this relationship with the same color in your Excel spreadsheet. Upload your .xls file to your journal page.
Interpret your results. Look up the definitions for any GO terms that are unfamiliar to you. The "official" definitions for GO terms can be found at http://www.geneontology.org. You can use one of the online biological dictionaries as a supplement, if needed.

Write a paragraph relating the results of this GO analysis to the experiment performed (comparing laboratory-grown and patient-derived Vibrio cholerae. You need to give a biological interpretation of what do each of these GO terms in your filtered list have to to with the pathogenecity of the bacterium? You may consult with your partner on this, but your explanation on your individual journal page needs to be in your own words. This is where the real "brain power" comes in with interpreting DNA microarray data. Even experienced scientists struggle with this part. Use your creativity as a scientist to stretch your brain in this question.

glucose catabolic process:glucose breakdown

hexose catabolic process: hexose breakdown

glycolysis: "The glycolytic process that begins with the conversion of glucose to glucose-6-phosphate by glucokinase activity. Glycolytic processes are the chemical reactions and pathways resulting in the breakdown of a carbohydrate into pyruvate, with the concomitant production of a small amount of ATP"

monosaccharide catabolic process:"The chemical reactions and pathways resulting in the breakdown of monosaccharides, polyhydric alcohols containing either an aldehyde or a keto group and between three to ten or more carbon atoms"

alcohol catabolic process:"The chemical reactions and pathways resulting in the breakdown of alcohols, any of a class of compounds containing one or more hydroxyl groups attached to a saturated carbon atom"

cellular carbohydrate catabolic process:"The chemical reactions and pathways resulting in the breakdown of carbohydrates, any of a group of organic compounds based of the general formula Cx(H2O)y, as carried out by individual cells"

protein folding:"The process of assisting in the covalent and noncovalent assembly of single chain polypeptides or multisubunit complexes into the correct tertiary structure"

organelle organization:"A process that is carried out at the cellular level which results in the assembly, arrangement of constituent parts, or disassembly of an organelle within a cell. An organelle is an organized structure of distinctive morphology and function. Includes the nucleus, mitochondria, plastids, vacuoles, vesicles, ribosomes and the cytoskeleton. Excludes the plasma membrane."

translational elongation:"The successive addition of amino acid residues to a nascent polypeptide chain during protein biosynthesis."

translation elongation factor activity:"Functions in chain elongation during polypeptide synthesis at the ribosome."

chromosome condensation:"The progressive compaction of dispersed interphase chromatin into threadlike chromosomes prior to mitotic or meiotic nuclear division, or during apoptosis, in eukaryotic cells."

DNA packaging: pretty self explanatory; related to DNA packing

chromosome organization:"covalent modifications at the molecular level as well as spatial relationships among the major components of a chromosome"

nucleotide catabolic process: breakdown of nucleotide

pyridine nucleotide metabolic process: breakdown of pyridine

nicotinamide nucleotide metabolic process:"The chemical reactions and pathways involving nicotinamide nucleotides, any nucleotide that contains combined nicotinamide."

protein targeting:"The process of targeting specific proteins to particular membrane-bounded subcellular organelles. Usually requires an organelle specific protein sequence motif."

nucleobase, nucleoside, nucleotide and nucleic acid catabolic process

nucleobase, nucleoside and nucleotide catabolic process

intracellular transport:Decreased intracellular protein transport.

intracellular protein transport:Decreased intracellular protein transport.

unfolded protein binding:Decreased synthesis of chaperone proteins.

Overall, the definitions of the GO terms make it clear that there is much involvement in the catabolic processes of the cell. Not only that but with the decrease in functions of pathways such as intracellular protein transport would cause for malfunction in the cell and I would assume the health of the subject over all. The breakdown of glucose is being decreased that the cell is working much more slowly which can suggest that it is dying or perhaps in extreme conditions such as starvation.

There is one other file you need to save to your journal page. It has a .gmf extension and should be in the same fold as the .gex file that you created with the GenMAPP Expression Dataset Manager. You will need this file to re-open your results in MAPPFinder.

Conclusion

In week 8, there were two different parts to the assignment however both parts had the aim to complete or test run a microarray data analysis for V. cholerae. Part 1 consisted editing and manipulating the data not only for interpretation but to format it to go into the GenMAPP program. We used log fold change numbers for the different genes and the pvalues to map out the data.

We did this on Excel. For Part 2 we then used GenMAPP to visualize the gene expression in the data. We narrowed down the gene ontology terms to create an idea of the effect of V. cholerae. I conclude that the expression shows that the bacteria has a mechanism to be more potent by decreasing catabolic process as if it were in extreme conditions for survival.

List of Files to Upload

It may be easier to zip all of these files together and then upload them as a single zipped file, rather than zipping and uploading individually (for filetypes not allowed by OpenWetware).

Your exceptions file when you imported your data into GenMAPP: .EX.txt
Your Expression Dataset file: .gex
Your GO results file: XXX-CriterionX-GO.txt
Your GO results saved as an Excel spreadsheet with filters applied: .xls
The MAPP you looked at: .mapp
The MAPPFinder GO mappings file: .gmf

Misc. Commentary

October 15, 2015

Media:Merrell Compiled Raw Data Vibrio VP 20151015.xls

October 20,2015

Media:Merrell Compiled Raw Data Vibrio VP 20151020.txt

Media:Merrell Compiled Raw Data Vibrio VP 20151020.gex

There was 121 errors detected in the raw data in GenMAPP 2.1. when the Merrell_Compiled_Raw_Data_Vibrio_VP_20151020.EX.txt was selected. My partner, Brandon, had the 2009 file and had 772 errors when ran through GenMAPP2.1.

October 22,2015

For the class demo, I was part of the decrease group.

October 25, 2015

Brandon and I met up at around 9pm at Seaver 120 to work on the assignment together.

October 26,2015

Brandon pointed out a vital mistake in my criterion at around 5:18pm. This mistake was in the criterion so most of my answers for part 2 were off.Late evening (10:30), I changed them to get more reasonable data. It was great that through the comparison that Brandon was able to catch my mistake. It's greatly appreciated!

Links

Vpachec3 User Page