2010 Data Set for the Vibrio Data compiled with Trixie

Processes Completed in Class

When the data set is cross referencing the genes in my microarray file with the genes in the database there was 121 errors.
- Some of the errors were "Gene not found in OrderedLocusNames or any related system."

Creating Color sets (Decreased expression group - inclass)

Part 1 =

The data from the Merrell et al. (2002) paper was accessed from this page at the Stanford Microarray Database.
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
  - 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

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

Scaled and centered the data (between chip normalization) performed the following operations:

Inserted a new Worksheet into the excel file, and named it "scaled_centered".
Went back to the "compiled_raw_data" worksheet, Selected All and Copied. went to the new "scaled_centered" worksheet, clicked on the upper, left-hand cell (cell A1) and Pasted.
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, typed the following equation:

=AVERAGE(B4:B5224)

and pressed "Enter". Excel computes the average value of the cells specified in the range given inside the parentheses.

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

=STDEV(B4:B5224)

and pressed "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.
Computed the average and standard deviation of the log ratios for each chip. Scaling and centering based on these values.
Copied the column headings for all of the data columns and then pasted them to the right of the last data column so that the second set of headers above blank column of cells. Edited the names of the columns so that they now read: A1_scaled_centered, A2_scaled_centered, etc.
In cell N4, typed the following equation:

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

In this case, we made 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?

This is important because we want to make sure excel is reading the correct data for its computations
Copied and pasted the equation into the entire column.
Copy and paste the scaling and centering equation for each of the columns of data with the "_scaled_centered" column header.

Performed statistical analysis on the ratios

Inserted a new worksheet and name it "statistics".
Went back to the "scaling_centering" worksheet and copied the first column ("ID").
Pasted the data into the first column of the new "statistics" worksheet.
Went back to the "scaling_centering" worksheet and copied the columns that are designated "_scaled_centered".
Went to the new worksheet and clicked on the B1 cell. Selected "Paste Special" from the Edit menu. A window opened: clicked on the radio button for "Values" and clicked OK.
Deleted Rows 2 and 3 where it said "Average" and "StDev" so that the data rows with gene IDs are immediately below the header row 1.
Went to a new column on the right of the worksheet. Typed the header "Avg_LogFC_A", "Avg_LogFC_B", and "Avg_LogFC_C" into the top cell of the next three columns.
Computed the average log fold change for the replicates for each patient by typing the equation:

=AVERAGE(B2:E2)

into cell N2. Copied this equation and pasted it into the rest of the column.

Created the equation for patients B and C and pasted it into their respective columns.
Computed the average of the averages. Typed the header "Avg_LogFC_all" into the first cell in the next empty column. Created the equation that computed the average of the three previous averages we calculated and pasted it into this entire column.
Inserted a new column next to the "Avg_LogFC_all" column that was computed in the previous step. Labeled the column "Tstat". This computed a T statistic that tells us whether the scaled and centered average log ratio is significantly different than 0 (no change). Entered the equation:

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

Labeled the top cell in the next column "Pvalue". In the cell below the label, entered 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.

Calculated the Bonferroni p value Correction

Adjusted the p value to correct for the multiple testing problem. Labeled the next two columns to the right with the same label, Bonferroni_Pvalue.
Typed the equation =S2*5221, Upon completion of this single computation, used the trick to copy the formula throughout the column.
Replaced 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). Used the trick to copy the formula throughout the column.

Calculated the Benjamini & Hochberg p value Correction

Inserted a new worksheet named "B-H_Pvalue".
Copied and pasted the "ID" column from your previous worksheet into the first column of the new worksheet.
InsertED a new column on the very left and nameD it "MasterIndex". We created a numerical index of genes so that we are able to always sort them back into the same order.
- Typed a "1" in cell A2 and a "2" in cell A3.
- Selected both cells. Hovered the mouse over the bottom-right corner of the selection until it maked a thin black + sign. Double-clicked 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, used Paste special > Paste values. Copied the unadjusted p values from the previous worksheet and pasted it into Column C.
Selected all of columns A, B, and C. Sorted by ascending values on Column C. Clicked the sort button from A to Z on the toolbar, in the window that appears, sorted by column C, smallest to largest.
Typed the header "Rank" in cell D1. Created a series of numbers in ascending order from 1 to 5221 in this column. This is the p value rank, smallest to largest. Typed "1" into cell D2 and "2" into cell D3. Selected both cells D2 and D3. Double-clicked on the plus sign on the lower right-hand corner of the selection to fill the column with a series of numbers from 1 to 5221.
calculated the Benjamini and Hochberg p value correction. Type B-H_Pvalue in cell E1. Typed the following formula in cell E2: =(C2*5221)/D2 and pressed enter. Copied that equation to the entire column.
Typed "B-H_Pvalue" into cell F1.
Typed the following formula into cell F2: =IF(E2>1,1,E2) and pressed enter. Copied that equation to the entire column.
Selected columns A through F. Sorted them by your MasterIndex in Column A in ascending order.
Copied column F and used Paste special > Paste values pasted it into the next column on the right of the "statistics" sheet.

Prepared 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)?
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)?
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.)
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?

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?

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Part 2

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.
- 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.
  - 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

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".
- Top 10 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, and hexose metabolic process
- Compared the list with Trixie. The terms are the same but the relative amount of results are different because of the amount of data added or retracted during the time between the 2008 and 2010 data sets.

Media:BioDB EY20152710.png

Media:BioDB TR 20151027.jpg

Our terms are different, we have 2 of the same terms. the 2010 database is more recent and if any updates were made that would explain any discrepancies.
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.
VC0028 GO terms: metabolic process, metal ion binding, iron suliron-sulfur cluster binding, 4 iron, 4 sulfur cluster binding, catalytic activity, lyase activity, dihydroxy-acid dehydratase activity, cellular amino acid biosynthetic process, branched chain family amino acid biosynthetic process
VC0941 GO terms: glycine metabolic process, L-serine metabolic process, one-carbon metabolic process, transferase activity, glycine hydroxymethyltransferase activity, pyridoxal phosphate binding, catalytic activity, cytoplasm, nucleotide binding,
VC0869 GO terms: glutamine metabolic process, 'de novo' IMP biosynthetic process, purine nucleotide biosynthetic process, ATP binding, catalytic activity, ligase activity, phosphoribosylformylglycinamidine synthase activity
VC0051 GO terms: purine nucleotide biosynthetic process, 'de novo' IMP biosynthetic process, nucleotide binding, ATP binding, catalytic activity, lyase activity, carboxyl-lyase activity, phosphoribosylaminoimidazole carboxylase activity
VC0647 GO terms: mRNA catabolic process, RNA processing, cytoplasm, mitochondrion, RNA binding, 3'-5'-exoribonuclease activity, transferase activity, nucleotidyltransferase activity, polyribonucleotide nucleotidyltransferase activity,
VC0468 GO terms: glutathione biosynthetic process, metal ion binding, nucleotide binding, ATP binding, catalytic activity, ligase activity, glutathione synthase activity,
VC2350 GO terms: deoxyribonucleotide catabolic process, metabolic process, cytoplasm, catalytic activity, lyase activity, deoxyribose-phosphate aldolase activity
VCA0583 GO terms: outer membrane-bounded periplasmic space, transporter activity
More genes were found in the 2010 database than the 2008 database because the 2010 database was probably updated.
Specific Gene ID chosen VCA0583; transporter activity. Expression for Q9KM06_VIBCH does not meet the criteria designation, therefore shows neither an increase or decrease. The public database says the gene name is outer membrane-bound periplasmic space.

 File:Transporter activity.mapp

- File:Vibrio EMY BioDB MappFinderResults 20152610-Criterion0-GO.txt
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.
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%

File:Vibrio EMY BioDB MappFinderResults 20152610-Criterion0-GO.xlsx

There are a few subsets of filtered terms that are related to each other because they affect the same process and by either inhibiting or increasing the rate of gene expression. For instance, The ontology names associated with branched chain amino acid metabolic and biosynthetic processes show an increased gene expression. This means that the Vibrio Cholerae is producing more proteins, since other ontology hits such as cell projection and flagellum organization are also showing increased gene expression. One could infer that some of the proteins produced are structural and movement associated proteins. The cell is responding to an environmental condition and is actively producing proteins and speeding up its metabolic process. Also there seems to be inhibition of purine nucleoside and ribonucleoside monophosphate metabolic and biosynthetic processes. Inhibiting the breakdown of these molecules, essential inhibits the breakdown of DNA for which these molecules are monomers of. The overall analysis of the data is that The Vibrio Cholerae is focusing its efforts on protein synthesis over any other biological process.

- Additional files to be uploaded
  - MAPPFinder GO Mappings file Looked at: File:LocalMAPPs.gmf
  - Expression Dataset file: File:Merrell Compiled Raw Data Vibrio EMY 20151510 (1).gex
  - Exceptions file when imported data into GenMAPP: File:Merrell Compiled Raw Data Vibrio EMY 20151510 (1).EX.txt
  - Mapp looked at: in procedure
  - GO results saved as excel with filters: in procedure
    GO results file: in procedure

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.