Difference between revisions of "Bklein7 Week 8"

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==Files==
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==Files Generated in the This Week's Analysis==
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*'''Part I Spreadsheet: [[File:Merrell Compiled Raw Data Vibrio BK 20151015.xls]]'''
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Links to files below can all be found within the electronic notebook at the point where they were created. For easy access, they are listed here as well.
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*Part I tab delimited File: [[File:Merrell Compiled Raw Data Vibrio BK 20151015- Tab Delimited.txt]]
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*Current .gex File: [[File:Merrell Compiled Raw Data Vibrio BK 20151015.gex]]
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# Analyzed microarray data: [[File:Merrell Compiled Raw Data Vibrio BK 20151015.xls]].
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*[[File:GenMAPP errors message.PNG]]
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# For GenMAPP text file: [[File:Merrell Compiled Raw Data Vibrio BK 20151015- Tab Delimited.txt]].
 +
# Exceptions file: [[File:Merrell Compiled Raw Data Vibrio BK 20151015.EX.txt]].
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# Expression Dataset files:
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#*Original- [[File:Merrell Compiled Raw Data Vibrio BK 20151015.gex]].
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#*After Adding Color Set- [[File:Merrell Compiled Raw Data Vibrio BK 20151025.gex]].
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# GO term MAPP: [[File:Polyribonucleotide nucleotidyltransferase activity.mapp]].
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# MAPPFinder Results:
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#* Original- [[File:Merrell Compiled Raw Data Vibrio BK 20151015-Criterion1-GO.txt]].
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#* Filtered in Excel- [[File:Merrell Compiled Raw Data Vibrio BK 20151015-Criterion1-GO(FILTERED).xlsx]].
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# .gmf file: [[File:Merrell Compiled Raw Data Vibrio BK 20151025.gmf]].
  
 
==Statistical Analysis of Vibrio cholerae Microarray Data (Part 1)==
 
==Statistical Analysis of Vibrio cholerae Microarray Data (Part 1)==
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*Downloaded the [Merrell_Compiled_Raw_Data_Vibrio.xls] file.
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<!--The following information was adapted from the instructions available on OpenWetWare-->
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**Renamed the file to Merrell_Compiled_Raw_Data_Vibrio_BK_20151015.xls
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*Attaining the unanalyzed Vibrio cholerae DNA microarray data
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===Normalizing the log ratios for the set of slides in the experiment===
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**The Vibrio cholerae data was accessed by downloading the following file: [[File:Merrell_Compiled_Raw_Data_Vibrio.xls]].  
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*Copied data from the ''compiled_raw_data'' tab to a new tab, entitled ''scaled_centered''
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**The above file was renamed to [[File:Merrell_Compiled_Raw_Data_Vibrio_BK_20151015.xls]] for the purposes of this section.
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**Inserted two rows below the ''ID'' row, labelled ''Average'' and ''StDev''.
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*The analysis performed on the Vibrio cholera is detailed in the sections below:
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***''Average'': Calculated the averages of the data in each column using the function <code>=AVERAGE()</code>
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****Sample- for row B the function is <code>=AVERAGE(B4:B5224)</code>
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***''StDev'': Calculated the standard deviations of the data in each column using the function <code>=STDEV()</code>
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****Sample- for row B the function is <code>=STDEV(B4:B5224)</code>
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**Created a new series of columns for data sets A1-C4, adding the labels "_scaled_centered" (e.g. A1_scaled_centered)
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***Wrote a function to normalize each value in the data set
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****For the first value in column B (data set A1), the function was <code>=(B4-B$2)/B$3</code>
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****Extended this to all data values in the new ''scaled_centered'' columns
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*Created a new tab entitled ''statistics''
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===Normalizing the Log Ratios for the Set of Slides in the Experiment===
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**Copied over:
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***The first column of gene ''ID'' values from the ''compiled_raw_data'' tab
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***The ''_scaled_centered'' columns from the ''scaled_centered'' tab
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**Deleted the blank ''Average'' and ''StDev'' rows
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**Created a new series of columns
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*COPY FROM OPENWETWARE FOR ELECTRONIC LAB NOTEBOOK
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This section dictates the steps necessary to scale and center the raw microarray data:
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===Before we begin...===
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* The data from the Merrell et al. (2002) paper was accessed from [http://smd.princeton.edu/cgi-bin/publication/viewPublication.pl?pub_no=119 this page] at the Stanford Microarray Database (now hosted by Princeton ''&mdash; [[User:Kam D. Dahlquist|Kam D. Dahlquist]] 18:26, 7 October 2013 (EDT)''.
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* The Log<sub>2</sub> of R/G Normalized Ratio (Median) has been copied from the raw data files downloaded from the Stanford Microarray Database.
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**'''Patient A'''
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***Sample 1: 24047.xls (A1)
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***Sample 2: 24048.xls (A2)
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***Sample 3: 24213.xls (A3)
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***Sample 4: 24202.xls (A4)
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**'''Patient B'''
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***Sample 5: 24049.xls (B1)
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***Sample 6: 24050.xls (B2)
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***Sample 7: 24203.xls (B3)
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***Sample 8: 24204.xls (B4)
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**'''Patient C'''
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***Sample 9: 24053.xls (C1)
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***Sample 10: 24054.xls (C2)
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***Sample 11: 24205.xls (C3)
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***Sample 12: 24206.xls (C4)
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**'''Stationary Samples''' (We will not be using these, they are listed here for completeness, but do not appear in your compiled raw data file.)
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***Sample 13: 24059.xls (Stationary-1)
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***Sample 14: 24060.xls (Stationary-2)
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***Sample 15: 24211.xls (Stationary-3)
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***Sample 16: 24212.xls (Stationary-4)
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* Download the [[Media:Merrell_Compiled_Raw_Data_Vibrio.xls | Merrell_Compiled_Raw_Data_Vibrio.xls]] file to your Desktop.
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** Save 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".
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===Normalize the log ratios for the set of slides in the experiment===
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* To begin, I created a new Worksheet in my Excel file entitled "scaled_centered".
 +
* I went back to the original "compiled_raw_data" worksheet and copied over all the data into the new "scaled_centered" worksheet.
 +
* I inserted two rows in between the top row of headers and the first data row entitled "Average" (cell A2) "StdDev" (cell A3).
 +
** I computed the Average log ratio for each chip by inputting the following equation into cell B2 and then pasting it into the rest of the "Average" column: <code>=AVERAGE(B4:B5224)</code>.
 +
** I computed the Standard Deviation of the log ratios on each chip by inputting the following equation into cell B3 and then pasting it into the rest of the "StdDev" column: <code>=STDEV(B4:B5224)</code>.
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* I created a new set of headings for the scaled and centered data by copying over the data column headings and then pasting them to the right of the last data column. I edited the names of the columns so that they now read:  A1_scaled_centered, A2_scaled_centered, etc.
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* In cell N4 (column with the heading A1_scaled_centered), I typed the following equation: <code)=(B4-B$2)/B$3</code>. In this case, we want the data in cell B4 to have the average subtracted from it (cell B2) and be divided by the standard deviation (cell B3).  We use 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 will paste it for the entire column of 5221 genes.  '''''Why is this important?'''''
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**Adding the dollar sign before the 2 and 3 ensured that the equation for each individual gene in column B drew from the overall average and standard deviation for the column. Maintaining these overall values in the equation is critical to yielding scaled and centered outputs for each gene. If the dollar signs were not included, Excel would assume that for each gene, it would subtract by the value two rows above and then divide by the value one row above (e.g. for B80, the equation would be <code>(B80-B78)/B79</code> when in reality we would want <code>(B80-B2)/B3</code>). This would yield extraneous results.
 +
*I copied this equation to the rest of the column and then adapted it for all "_scaled_centered" columns.
  
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To scale and center the data (between chip normalization) perform the following operations:
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===Performing Statistical Analysis on the Ratios===
  
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* Insert a new Worksheet into your Excel file, and name it "scaled_centered".
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This section details the steps necessary to perform statistical analysis on the scaled and centered data produced in the section above:
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* Go back to the "compiled_raw_data" worksheet, Select All and Copy.  Go to your new "scaled_centered" worksheet, click on the upper, left-hand cell (cell A1) and Paste.
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* Insert two rows in between the top row of headers and the first data row.
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* In cell A2, type "Average" and in cell A3, type "StdDev".
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* You will now compute the Average log ratio for each chip (each column of data).  In cell B2, type the following equation:
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=AVERAGE(B4:B5224)
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: and press "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.
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* You will now compute the Standard Deviation of the log ratios on each chip (each column of data).  In cell B3, type the following equation:
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=STDEV(B4:B5224)
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: and press "Enter". 
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* Excel will now do some work for you.  Copy these two equations (cells B2 and B3) and paste 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.
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* You have now computed the average and standard deviation of the log ratios for each chip.  Now we will actually do the scaling and centering based on these values.
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* Copy the column headings for all of your data columns and then paste them to the right of the last data column so that you have a second set of headers above blank colums of cells.  Edit the names of the columns so that they now read: A1_scaled_centered, A2_scaled_centered, etc.
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* In cell N4, type the following equation:
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=(B4-B$2)/B$3
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: In this case, we want the data in cell B4 to have the average subtracted from it (cell B2) and be divided by the standard deviation (cell B3).  We use 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 will paste it for the entire column of 5221 genes.  '''''Why is this important?'''''
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* Copy and paste this equation into the entire column.  One easy way to do this is to click on the original cell with your equation 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 genes.
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* Copy and paste 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.
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===Perform statistical analysis on the ratios===
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* For this section, I created a new worksheet and name it "statistics".
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* I went back to the "scaled_centered" worksheet and copied over both the first column ("ID") and copied the columns that are designated "_scaled_centered".
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We are going to perform this step on the scaled and centered data you produced in the previous step.
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* I deleted rows 2 and 3 where it says "Average" and "StDev" so that the data rows with gene IDs were immediately below the header row 1.
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* I created three new columns to the right of the copied ones with the following headers: "Avg_LogFC_A", "Avg_LogFC_B", and "Avg_LogFC_C".
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* Insert a new worksheet and name it "statistics".
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* I computed the average log fold change for the replicates for each patient by typing the equation:
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* Go back to the "scaling_centering" worksheet and copy the first column ("ID").
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* Paste the data into the first column of your new "statistics" worksheet.
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* Go back to the "scaling_centering" worksheet and copy the columns that are designated "_scaled_centered".
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* 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.
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* 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.
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* 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.
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* Compute the average log fold change for the replicates for each patient by typing the equation:
+
 
  =AVERAGE(B2:E2)
 
  =AVERAGE(B2:E2)
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: into cell N2.  Copy this equation and paste it into the rest of the column. 
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: into cell N2 and then copying/pasting it into the rest of the three columns, adapting it as necessary.
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* Create the equation for patients B and C and paste it into their respective columns.
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* I created a new column to compute the average of the averages with the header "Avg_LogFC_all". I then created the equation to compute the average of the three previous averages you calculated and paste it into this entire column.
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* 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.
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* I created a new column with the header "Tstat" to compute the T statistic for each scaled and centered average log ratio. In this column, I entered the following equation and pasted it into the rest of the column:   
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* 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:   
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  =AVERAGE(N2:P2)/(STDEV(N2:P2)/SQRT(3))
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  =AVERAGE(N2:P2)/(STDEV(N2:P2)/SQRT(number of replicates))
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* I created one last column with the heading "Pvalue". In the cell below the label, I entered the following equation and then copied it into the rest of the column:   
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: (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.
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  =TDIST(ABS(R2),2,2)
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* Label the top cell in the next column "Pvalue". In the cell below the label, enter the equation:   
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  =TDIST(ABS(R2),degrees of freedom,2)
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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.
+
  
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====Calculate the Bonferroni p value Correction====
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====Calculating the Bonferroni p value Correction====
  
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* Now we will perform adjustments to the p value to correct for the [https://xkcd.com/882/ multiple testing problem].  Label the next two columns to the right with the same label, Bonferroni_Pvalue.
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It is necessary to perform adjustments to the p value to correct for the [https://xkcd.com/882/ multiple testing problem].  Therefore, I first calculated the Bonferroni p value correction on the p values calculated in the above section:
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* Type the equation <code>=S2*5221</code>, Upon completion of this single computation, use the trick to copy the formula throughout the column.
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* I labelled the next two columns to the right of "Pvalue" with the same label: "Bonferroni_Pvalue".
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* 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: <code>=IF(T2>1,1,T2)</code>.  Use the trick to copy the formula throughout the column.
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* I typed the equation <code>=S2*5221</code> into the first Bonferroni column and pasted it throughout.
 +
* Next, I replaced any corrected p value that was greater than 1 with the number 1 by typing the following formula into the first cell below the second Bonferroni_Pvalue header: <code>=IF(T2>1,1,T2)</code>.  
  
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====Calculate the Benjamini & Hochberg p value Correction====
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====Calculating the Benjamini & Hochberg p value Correction====
  
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* Insert a new worksheet named "B-H_Pvalue".
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The second p value correction I performed was the Benjamini & Hochberg correction, the methods of which are presented below:
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* Copy and paste the "ID" column from your previous worksheet into the first column of the new worksheet.  
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* I created a new worksheet named "B-H_Pvalue".
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* 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.
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* In this worksheet, I copied over the "ID" column from the previous worksheet into the first column of the new worksheet.  
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** Type a "1" in cell A2 and a "2" in cell A3.
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* Next, I added a new column on the very left and named it "MasterIndex". Under this heading, I added a numbered list from 1 to 5221 (the number of genes on the microarray).
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** 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).  
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* I copied over the unadjusted p values from your previous worksheet and pasted them into Column C.
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* For the following, use Paste special > Paste values.  Copy your unadjusted p values from your previous worksheet and paste it into Column C.
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* I selected all of columns A, B, and C and sorted by ascending values on Column C.
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* 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.
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** This was done by clicking the sort button from A to Z on the toolbar and sorting by column C, smallest to largest.
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* 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.
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* I created a new column and labelled it with the header "Rank" in cell D1 to house another numbered list from 1 to 5221. Because this was done for the sorted columns, these values represented the p value ranks, smallest to largest.   
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* 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: <code>=(C2*5221)/D2</code> and press enter. Copy that equation to the entire column.
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* I created a two new columns to calculate the Benjamini and Hochberg p value correction and labeled them "B-H_Pvalue" (cell E1 and F1).
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* Type "B-H_Pvalue" into cell F1.
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** In the first column, I inputted the following formula and copied it throughout the column: <code>=(C2*5221)/D2</code>.
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* Type the following formula into cell F2: <code>=IF(E2>1,1,E2)</code> and press enter. Copy that equation to the entire column.  
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** In the second column, I inputted the following formula and copied it throughout: <code>=IF(E2>1,1,E2)</code>.  
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* Select columns A through F.  Now sort them by your MasterIndex in Column A in ascending order.
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* Next, I selected columns A through F and sorted them by the MasterIndex in Column A in ascending order.
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* Copy column F and use Paste special > Paste values to paste it into the next column on the right of your "statistics" sheet.
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* Finally, I copied column F into the next column on the right of your "statistics" sheet.
  
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====Prepare file for GenMAPP====
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===Preparing the File for GenMAPP===
  
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* Insert a new worksheet and name it "forGenMAPP".
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* I inserted a new worksheet and named it "forGenMAPP".
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* Go back to the "statistics" worksheet and Select All and Copy.
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* I copied over the entirety of the "statistics" worksheet to this new worksheet.
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* 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.
+
* I selected Columns B through Q (all the fold changes) and formatted the cells to show only 2 decimal places.
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* 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.
+
* I selected all the columns containing p values and formatted the cells to show only 4 decimal places.
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* Select all the columns containing p values.  Select the menu item Format > Cells.  Under the number tab, select 4 decimal places. Click OK.
+
* I deleted the left-most Bonferroni p value column.
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* Delete the left-most Bonferroni p value column, preserving the one that shows the result of your "if" statement.
+
* I inserted a column to the right of the "ID" column entitled "SystemCode" and filled the entire column with the letter "N".
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* 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".
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* After making the above changes, I saved the file as "Text (Tab-delimited) (*.txt)".
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* 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.
+
** This .txt file can be found here: [[File:Merrell Compiled Raw Data Vibrio BK 20151015- Tab Delimited.txt]].
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** 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===
+
===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).
+
To verify the results of the data analysis performed on the Vibrio cholerae DNA microarray data, I assessed the number of genes that were significantly changed at various p value cut-offs and also compared my data analysis with the published results of Merrell et al. (2002). These analyses were done in the "forGenMapp" tab of the Excel spreadsheet used for the data analysis.
  
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* Open your spreadsheet and go to the "forGenMAPP" tab.
+
*Assessing the number of genes significantly changed
−
* 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.
+
** To answer the questions below, I used custom filter to display only the "Pvalue" data that met specific criterion (e.g. < 0.05).
−
* 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)?'''''
 
** '''''How many genes have p value < 0.05? and what is the percentage (out of 5221)?'''''
 +
*** 948 of the 5221 genes (18.2%) had p values that were less than 0.05
 
** '''''What about p < 0.01? and what is the percentage (out of 5221)?'''''
 
** '''''What about p < 0.01? and what is the percentage (out of 5221)?'''''
 +
*** 235 of the 5221 genes (4.50%) had p values that were less than 0.01
 
** '''''What about p < 0.001? and what is the percentage (out of 5221)?'''''
 
** '''''What about p < 0.001? and what is the percentage (out of 5221)?'''''
 +
*** 24 of the 5221 genes (0.46%) had p values that were less than 0.001
 
** '''''What about p < 0.0001? 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.
+
*** 2 of the 5221 (0.038%) genes had p values that were less than 0.0001
−
* 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/ 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:
+
** To apply a more stringent criterion to the p values, I performed the Bonferroni and Benjamini and Hochberg corrections to the unadjusted p values. I then filtered the columns "Bonferroni_Pvalue" and "B-H_Pvalue" columns to only display data that met specific criterion.
 
** '''''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 Bonferroni-corrected p value? and what is the percentage (out of 5221)?'''''
 +
*** No genes (0.00%) had p values less than 0.05 for the Bonferroni-corrected p value
 
** '''''How many genes are p < 0.05 for the Benjamini and Hochberg-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. 
+
*** No genes (0.00%) had p values less than 0.05 for the Benjamini and Hochberg-corrected p value
−
* 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.
+
* Assessing the number of genes with expression changes
−
** 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 %)'''''
+
**The "Avg_LogFC_all" indicated the size of the gene expression changes and their direction.  Positive values increased relative to the control, and negative values decreased 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 less than zero.  '''''How many are there? (and %)'''''
+
*** Keeping the (unadjusted) "Pvalue" filter at p < 0.05, I filtered the "Avg_LogFC_all" column to show all genes with an average log fold change greater than zero.  '''''How many are there? (and %)'''''
−
** '''''What about an average log fold change of > 0.25 and p < 0.05? (and %)'''''
+
**** 352 of the 5221 genes (6.74%) exhibited significant (p < 0.05) increases in gene expression
−
** '''''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.)
+
*** Keeping the (unadjusted) "Pvalue" filter at p < 0.05, I filtered the "Avg_LogFC_all" column to show all genes with an average log fold change less than zero.  '''''How many are there? (and %)'''''
−
* 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.
+
**** 596 of the 5221 genes (11.4%) exhibited significant (p < 0.05) decreases in gene expression
 +
*** '''''What about an average log fold change of > 0.25 and p < 0.05? (and %)'''''
 +
**** 339 of the 5221 genes (6.49%) exhibited significant (p < 0.05) increases in gene expression that exceeded an average log fold change of 0.25
 +
*** '''''Or an average log fold change of < -0.25 and p < 0.05? (and %)'''''
 +
**** 579 of the 5221 genes (11.1%) exhibited significant (p < 0.05) decreases in gene expression that were less than an average log fold change of -0.25
 
* '''''What criteria did Merrell et al. (2002) use to determine a significant gene expression change?  How does it compare to our method?'''''
 
* '''''What criteria did Merrell et al. (2002) use to determine a significant gene expression change?  How does it compare to our method?'''''
 +
**Merrel et al. identified statistically significant changes in gene expression by imputing normalized intensity ratios for expression into the program Statistical Analysis for Microarrays (SAM). Within the program, they ran a two-class SAM analysis using the strain grown in vitro (class I) and each individual stool sample (class II). This test isolated expression levels that were of a significantly different magnitude (at least a twofold change) across all patient samples as statistically significant expression changes. This method for significance differed from ours in two primary ways:
 +
**#It judged significant differences based on order of magnitude changes in the normalized intensity rations (twofold change) as opposed to running T tests and calculating p-values.
 +
**#It looked for consistent significant changes in expression across all patient samples instead of looking for significance in averaged log fold changes for all patients.
 +
*  For the GenMAPP analysis below, I used 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 the analysis.
  
−
===Sanity Check: Compare individual genes with known data===
+
===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?'''''
+
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. I reviewed the "forGenMAPP" Excel worksheet to compare these findings to the results of my personal data analysis.
 +
*'''''What are the fold changes and p values (of these genes)? Are they significantly changed in the analysis?'''''
 +
**VC0028 (2 entries)
 +
***Fold changes: 1.65, 1.27
 +
***P values: 0.0474, 0.0692
 +
***Significantly changed? The expression of the first gene with this ID significantly changed, but that of the second gene with this ID did not.
 +
**VC0941 (2 entires)
 +
***Fold changes: 0.09, -0.28
 +
***P values: 0.6759, 0.1636
 +
***Significantly changed? The expression of neither gene with this ID significantly changed.
 +
**VC0869 (5 entries)
 +
***Fold changes: 1.50, 1.59, 1.95, 2.20, 2.12
 +
***P values: 0.0174, 0.0463, 0.0227, 0.0020, 0.0200
 +
***Significantly changed? The expression of all genes with this ID significantly changed.
 +
**VC0051 (2 entries)
 +
***Fold changes: 1.92, 1.89
 +
***P values: 0.0139, 0.0160
 +
***Significantly changed? The expression of both genes with this ID significantly changed.
 +
**VC0647 (3 entries)
 +
***Fold changes: -1.11, -0.94, -1.05
 +
***P values: 0.0003, 0.0125, 0.0051
 +
***Significantly changed? The expression of all genes with this ID significantly changed.
 +
**VC0468 (1 entry)
 +
***Fold change: -0.17
 +
***P value: 0.3350
 +
***Significantly changed? The expression of this gene did not significantly change.
 +
**VC2350 (1 entry)
 +
***Fold changes: -2.40
 +
***P value: 0.0130
 +
***Significantly changed? The expression of this gene significantly changed.
 +
**VCA0583 (1 entry)
 +
***Fold change: 1.06
 +
***P value: 0.1011
 +
***Significantly changed? The expression of this gene did not significantly change.
  
 
==MAPPFinder Analysis of Vibrio cholerae Microarray Data (Part 2)==
 
==MAPPFinder Analysis of Vibrio cholerae Microarray Data (Part 2)==
  
−
===Map Onto Biological Pathways (GenMAPP & MAPPFinder)===
+
===Mapping Onto Biological Pathways (GenMAPP & MAPPFinder)===
  
−
'''Fall 2015:''' Beginning point for class on Tuesday, October 20 as part of the [https://xmlpipedb.cs.lmu.edu/biodb/fall2015/index.php/Week_8# Week 8] journal assignment.
+
Before beginning the mapping process, it is necessary to load the correct gene database into GenMAPP:
−
 
+
* I download the 2009 ''Vibrio cholerae'' gene database by following [http://sourceforge.net/projects/xmlpipedb/files/V.%20cholerae%20Gene%20Database/V.%20cholerae%2020090622/Vc-Std_External_20090622.zip/download this link to the XMLPipeDB SourceForge Download page]. My homework partner Veronica downloaded the 2010 version.
−
'''Fall 2013:'''  Beginning point for class on Tuesday, October 15 as part of the [https://xmlpipedb.cs.lmu.edu/biodb/fall2013/index.php/Week_8 Week 8] journal assignment.
+
* I saved the above file into the folder C:\GenMAPP 2 Data\Gene Databases and extracted it.
−
 
+
* Within GenMAPP, I loaded the 2009 gene database by selecting Data > Choose Gene Database and choosing the appropriate file from the directory C:\GenMAPP 2 Data\Gene Databases.
−
'''Fall 2010:'''  Beginning point for class on Tuesday, October 26 and for the [[BIOL367/F10#Week_9_In-class_Exercise_and_Journal_Assignment | 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, [http://sourceforge.net/projects/xmlpipedb/files/V.%20cholerae%20Gene%20Database/V.%20cholerae%2020090622/Vc-Std_External_20090622.zip/download follow '''''this link''''' to the XMLPipeDB SourceForge Download page].
+
−
***'''NOTE: THIS IS THE FILE I DOWNLOADED (2009), whereas veronica downloaded the 2010 file'''
+
−
** 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, [http://sourceforge.net/projects/xmlpipedb/files/V.%20cholerae%20Gene%20Database/V.%20cholerae%2020101022/Vc-Std_External_20101022.zip/download 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====
 
====GenMAPP Expression Dataset Manager Procedure====
  
−
* Launch the GenMAPP Program.  Check to make sure the correct Gene Database is loaded.
+
Once the appropriate gene database has been loaded into GenMAPP, the expression dataset can be uploaded and configured:
−
** 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.
+
−
**'''NOTE: "772 ERORRS WERE DETECTED IN YOUR RAW DATA... YOUR EXPRESSION DATASET HAS BEEN CREATED. IF THE ERRORS IN THE ABOVE FILE ARE CRITICAL, YOU MAY CORRECT THEM AND PROCESS THE EXCEPTIONS TO RECREATE THE GENE TABLE" VS. 121 ERRORS FOR VERONICA'''
+
−
** 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:  <code>EX.txt</code> 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.
+
* I opened the Expression Dataset Manger from the Data drop-down list in GenMAPP.
−
::The Ops list also contains the conjunctions AND and OR, which may be used to make compound criteria. For example:
+
* I selected New Dataset from the Expression Datasets menu and choose the tab-delimited text file formatted for GenMAPP (.txt).
−
[Fold Change] > 1.2 AND [p value] <= 0.05
+
* Upon specifying that all data was numerical, the Expression Dataset Manager converted my data to .gex file. This process took approximately one minute to complete. In addition to converting the data to a .gex file, an exceptions file (.EX.txt) was also produced, as 772 errors were reportedly detected in the raw data.
−
::Parentheses control the order of evaluation. Anything in parentheses is evaluated first. Parentheses may be nested. For example:
+
** The .gex file generated can be found here: [[File:Merrell Compiled Raw Data Vibrio BK 20151015.gex]]
−
[Control Average] = 100 AND ([Exp1 Average] > 100 OR [Exp2 Average] > 100)
+
** The .EX file generated can be found here: [[File:Merrell Compiled Raw Data Vibrio BK 20151015.EX.txt]]
−
::Column names may be used anywhere a value can, for example:
+
** '''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.'''
−
  [Control Average] < [Experiment Average]
+
***[[File:GenMAPP errors message.PNG]]
 +
***The above screenshot shows the error message I received after using the Expression Dataset Manager to convert my raw data file. A reported 772 errors were detected in my raw data.
 +
***Upon opening the .EX.txt file, I found the error message "Gene not found in OrderedLocusNames or any related system." repeated several times.
 +
****To determine if this was the only error message, I uploaded the exceptions file to my LMU CMSI directory using the command <code>scp /Users/brandonklein/Desktop/Merrell_Compiled_Raw_Data_Vibrio_BK_20121015.EX.txt bklein7@my.cs.lmu.edu:~bklein7</code>. In the command line, I used the following command sequence to determine how often the "Gene not found..." error message was repeated: <code>grep "Gene not found"Merrell_Compiled_Raw_Data_Vibrio_BK_20121015.EX.txt | wc</code>. This yielded the output <code> 772  23932  145791</code>, confirming that the above error message was responsible for all 772 reported errors.
 +
*** '''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?'''
 +
****With the 2009 Vibrio cholerae gene database loaded into GenMAPP, I encountered 772 errors when converting the analyzed Merrell et al. microarray data. When converting the exact same data with the 2010 Vibrio cholera gene database loaded into GenMapp, Veronica encountered 121 errors. All error messages for both of us were the same: "Gene not found in OrderedLocusNames or any related system". Therefore, I had more errors. I believe this occured because the 2009 version of the Vibrio cholerae database was less developed/complete than the more recent 2010 version. Thus, it likely had less Gene listings, and therefore less of the Ordered Locus Names used by Merrell matched to the database.
  
−
* After completing a new criterion, add the criterion entry (label, criterion, and color) to the Criteria List by clicking the Add button.
+
* I customized the new Expression Dataset by creating a Color Sets= with instructions to GenMAPP for displaying data on MAPPs. The new Color Set was entitled "LogFoldChange".
−
** 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.
+
**First, I created a criterion for this color set to label genes that demonstrated a significant ''increase'' in their expression.
−
** You may continue to add criteria to the Color Set by using the previous steps.
+
***I specified the Gene value as "Avg_LogFC_all" for the Vibrio dataset.
−
*** 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.
+
***I activated the Criteria Builder by clicking the New button and named the criterion "Increased".
−
* 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.
+
***I selected the color for this criterion as red using the color box.
−
* 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.
+
***I stated the criterion as follows and added it to the Criteria List: <code>[Avg_LogFC_all] > 0.25 AND [Pvalue] < 0.05</code>
−
* '''Upload your .gex file to your journal entry page for later retrieval.'''
+
**Second, I created a criterion for this color set to label genes that demonstrated a significant ''decrease'' in their expression.
 +
***I specified the Gene value as "Avg_LogFC_all" for the Vibrio dataset.
 +
***I activated the Criteria Builder by clicking the New button and named the criterion "Decreased".
 +
***I selected the color for this criterion as green using the color box.
 +
***I stated the criterion as follows and added it to the Criteria List: <code>[Avg_LogFC_all] < -0.25 AND [Pvalue] < 0.05</code>
 +
* Upon entering these color sets, I savedthe entire Expression Dataset by selecting Save from the Expression Dataset menu.
 +
** The updated .gex fie produced by this procedure can be found here: [[File:Merrell Compiled Raw Data Vibrio BK 20151025.gex]]
  
 
====MAPPFinder Procedure====
 
====MAPPFinder Procedure====
 +
* I launched the MAPPFinder program from within GenMAPP and ensured that the 2009 Gene Database was still loaded into GenMAPP.
 +
* I clicked on the button "Calculate New Results" followed by "Find File", at which point I chose my .gex file.
 +
* Veronica and I both chose to filter the data with the "Decreased" criterion present within the LogFoldChange Color Set.
 +
* I checked the boxes next to "Gene Ontology" and "p value", specified the results file, and then clicked "Run MAPPFinder".
 +
**This analysis took several minutes to complete.
 +
* I clicked on the menu item "Show Ranked List" to see a list of the most significant Gene Ontology terms.
 +
** '''List the top 10 Gene Ontology terms.'''
 +
***[[File:TOP10.PNG]]
 +
**#protein folding
 +
**#chorismate metabolic process
 +
**#aromatic amino acid family biosynthetic process
 +
**#unfolded protein binding
 +
**#cytoplasm
 +
**#intracellular part
 +
**#localization
 +
**#nucleotide catabolic process
 +
**#locomotion
 +
**#aromatic amino acid family metabolic process
 +
** '''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?'''
 +
***Veronica's top 10 Gene Ontology terms using the 2010 Vibrio database were as follows:
 +
****#signal transduction
 +
****#molecular transducer activity
 +
****#signal transducer activity
 +
****#membrane
 +
****#peptidyl-histidine modification
 +
****#peptidyl-histidine phosphorylation
 +
****#two-component sensor activity
 +
****#protein histidine kinase activity
 +
****#peptidyl-amino acid modification
 +
****#phosphotransferase activity, nitrogenous group as acceptor
 +
***The above list of Veronica's top 10 Gene Ontology terms was entirely different from my own, despite the fact that we both input the same microarray data into GenMAPP. However, there are several reasons that explain why this happened. First, Veronica used a more recent version of the Vibrio database (2010 vs. 2009), which matched GO terms to a large number of genes included in the microarray data that did not perviously have GO terms in the 2009 database. As evidence of this, I only retrieved GO terms for two of the gene ID's in the question below using the 2009 database, whereas Veronica retrieved GO terms for each of the gene ID's. Additionally, it is possible that new GO terms were added to the 2010 database that were not present prior. For example, when I searched one of the GO terms Veronica found associated with the gene ID VC0028, dihydroxy-acid dehydratase activity, I did not find any matches to this result. Finally, each time a MAPP is generated, a slightly different list of top Gene Ontology terms can be produced. I tested this by running multiple MAPPFinder analyses on my microarray data using the same 2009 database and retrieved slightly different results more than once.
 +
* I used MAPPFinder to find the Gene Ontology term(s) with which the following genes mentioned by Merrell et al. (2002) were associated with: VC0028, VC0941, VC0869, VC0051, VC0647, VC0468, VC2350, and VCA0583. This was done by typing the identifiers for these genes into the MAPPFinder browser gene ID search field, choosing "OrderedLocusNames" from the drop-down menu to the right of the search field, and clicking on the GeneID Search button. The GO term(s) that were associated with the genes were highlighted in blue. '''List the GO terms associated with each of those genes. (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?'''
 +
**Associated GO terms
 +
***VC0028: "No MAPPs or GO terms could be found for that OrderedLocusNames ID."
 +
***VC0941: "No MAPPs or GO terms could be found for that OrderedLocusNames ID."
 +
***VC0869: "No MAPPs or GO terms could be found for that OrderedLocusNames ID."
 +
***VC0051: "No MAPPs or GO terms could be found for that OrderedLocusNames ID."
 +
***VC0647: mRNA catabolic process, RNA processing, cytoplasm, RNA binding, 3'-5'-exoribonuclease activity, transferase activity, nucleotidyltransferase activity, and polyribonucleotide nucleotidyltransferase activity.
 +
***VC0468: "No MAPPs or GO terms could be found for that OrderedLocusNames ID."
 +
***VC2350: "No MAPPs or GO terms could be found for that OrderedLocusNames ID."
 +
***VCA0583: transport, outer membrane-bounded periplasmic space, and transporter activity.
 +
**Comparison with Veronica's results using the 2010 Vibrio database
 +
***As mentioned in the answer to the previous question, Veronica matched all of the above GO terms to gene IDs in the 2010 database. Thus, the 2010 database must have been updated to include a more complete listing of GO terms associated with specific gene IDs, perhaps reflecting research advancements. In addition to this, some of the GO terms we retrieved for the same gene IDs (such as VCA0583) were not the same. This also suggests that more refined or experimentally proven GO terms were matched with genes previously associated with GO terms in the 2010 database.
 +
* VC0647 Investigation: '''List 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 "polyribonucleotide nucleotidyltransferase activity". The MAPP this produced only contained the gene "PNP_VIBCH", which I found in the [[http://www.uniprot.org/uniprot/Q9KU76 UniProt Database]]. The expression of this gene, referenced by the gene ID VC0647, significantly decreased in the microarray experiment.
 +
** I Double-clicked on the above gene box to find out more about the gene PNP_VIBCH. '''Click on the links to find out the function of this gene.'''
 +
***According to the [[http://www.uniprot.org/uniprot/Q9KU76 UniProt Database]], the function of this gene is to degrade mRNA. It does this by coding for the production of the protein polyribonucleotide nucleotidyltransferase, which "catalyzes the phosphorolysis of single-stranded polyribonucleotides processively in the 3'- to 5'-direction".
 +
** The MAPP created while investigating polyribonucleotide nucleotidyltransferase activity can be found here: [[File:Polyribonucleotide nucleotidyltransferase activity.mapp]].
 +
* Overall MAPPFinder results file: [[File:Merrell Compiled Raw Data Vibrio BK 20151015-Criterion1-GO.txt]].
  
−
'''''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 ([[BIOL367/F10#Groups_3 | Fall 2010]] or [https://xmlpipedb.cs.lmu.edu/biodb/fall2013/index.php/Week_8#Groups Fall 2013]) so that we can make sure that as a class we are covering both criteria.'''''
+
* I opened a copy of the .txt file listed above in Excel to filter the results.
−
 
+
** There were rows of information that gave background information on how MAPPFinder made the calculations at the top of the Excel file.  '''Compare this information with your partner who used a different version of the Vibrio Gene Database.  Which numbers are different?  Why are they different?'''
−
* Launch the MAPPFinder program (or from within GenMAPP, select Tools > MAPPFinder).
+
*** When comparing the background information, I found several lines in my output file that were distinct from Veronica's using the 2010 Vibrio database:
−
* 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\.)
+
***#Veronica matched a dramatically larger number of probes (3159 vs. 578) to the decreased criterion. This was unexpected, as I had anticipated our results for this to be equal given that we input the same microarray data. However, it turned out there was a flaw in Veronica's decreased criterion, which read: <code>[Avg_LogFC_All]>-0.25 AND [Pvalue]>0.05</code> instead of <code>[Avg_LogFC_All] < -0.25 AND [Pvalue]<0.05</code>.
−
* Click on the button "Calculate New Results".
+
***#Due to the issue above, an equally dramatic difference was present between the number of probes meeting the criterion that matched with a UniProt ID. This was expected, as the evidence during this exercise suggested that the 2010 database was more complete, including more gene IDs. Indeed, even with the criterion error, the percentage of probes that met her criterion and also matched with a UniProt ID (97.37%) was greater than mine (81.83%). This supports the conclusion that the 2010 database included more gene IDs.
−
* Click on "Find File" and choose the your Expression Dataset file, for example, "MyDataset.gex", and click OK.
+
**#The number of probes meeting the criterion AND linking to a GO term was different for both of us in much the same was as the example above. Although the strict numerical difference was exaggerated due to error (1571 vs. 254), the percentages still supported the fact that the 2010 database matched gene IDs to GO terms more frequently (49.73% vs. 43.94%).
−
** 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.
+
**#Finally, both the number of probes in the entire dataset that linked to UniProt IDs as well as to GO terms followed the trends already detailed above, proving those points. The 2009 database I used did in fact include less links to UniProt IDs and GO terms on the whole than the 2010 database.
−
* 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).
+
** I used the following filters to show the top 20 GO terms represented in my data for both the "Increased" and "Decreased" criteria:
−
* 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.'''
+
−
* 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?'''
+
−
* 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 [http://www.uniprot.org/ 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.'''
+
−
** 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.'''
+
−
** 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.'''
+
−
* 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
 
  Z Score (in column N) greater than 2
 
  PermuteP (in column O) less than 0.05
 
  PermuteP (in column O) less than 0.05
 +
Number Changed (in column I) greater than or equal to 5 AND less than 100
 +
Percent Changed (in column L) greater than or equal to 30%
 +
* '''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.'''
 +
**Over three quarters of the top 20 GO terms represented in the data for the "Increased" and "Decreased" criteria were closely related to one another. These terms were highlighted in the same color in the Excel file, which can be found here: [[File:Merrell Compiled Raw Data Vibrio BK 20151015-Criterion1-GO(FILTERED).xlsx]].
  
−
:You will use these two filters depending on the number of terms you have:
+
* '''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 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.'''
 +
**To begin the interpretation, I used the UniProt database in tandem with my MappFinder results and the filtered Excel file to discern both the function of specific genes included under the top 20 GO terms and whether those genes were down-regulated or up-regulated in the experiment. To facilitate this, I grouped the GO terms that were closely related according to the highlights I made in the filtered Excel file. For the sake of simplicity, I included just an interpretation of the results from this investigation for each group below.
 +
***Group 1: ''protein folding'' and ''unfolded protein binding''
 +
****Decreased expression of genes that promote the proper folding of proteins in stress conditions.
 +
*****Decreased synthesis of chaperone proteins.
 +
****Increased pepsidase activity, which promotes the hydrolysis of proteins.
 +
***Group 2: ''cis-trans isomerase activity'' and ''peptidyl-prolyl cis-trans isomerase activity''
 +
****Decreased expression of genes that promote proper protein folding and prevent protein aggregation (similar to Group 1).
 +
***Group 3: ''zinc ion binding''
 +
****Decreased synthesis of proteins involved in glycolytic processes.
 +
****Increased pepsidase activity.
 +
***Group 4: ''sugar:hydrogen symporter activity'', ''solute:hydrogen symporter activity'', and ''cation:sugar symporter activity''
 +
****Decreased sugar-dependent active transport into membrane.
 +
***Group 5: ''protein-N(PI)-phosphohistidine-sugar phosphotransferase activity'', ''sugar transmembrane transporter activity'', and ''carbohydrate transmembrane transporter activity''
 +
****Effects similar to Group 4.
 +
***Group 6: ''phosphoenolpyruvate-dependent sugar phosphotransferase system'' and ''carbohydrate transport''
 +
****Decreased intake of sugars from the external environment.
 +
****Effects similar to Group 4.
 +
***Group 7: ''translation regulator activity'' and ''translation factor activity, nucleic acid binding''
 +
****Decreased stimulation of aa-tRNA binding, which slows down the rate of protein production in the cell.
 +
***Group 8: ''endonuclease activity''
 +
****Decreased hydrolysis of nucleic acids.
 +
***Group 9: ''glucose metabolic process''
 +
***Decreased synthesis of proteins necessary for glycolytic processes (similar to Group 3).
 +
***Group 10: ''aromatic compound biosynthetic process''
 +
****Decreased synthesis of proteins involved in the chorismate biosynthesis pathway, which produced energy for the cell.
 +
***Group 11: ''intracellular protein transport'' and ''intracellular transport''
 +
****Decreased intracellular protein transport.
 +
*''Overall takeaways from investigating the functions of the top 20 genes that exhibited significant expression changes in the Vibrio cholerae microarray experiment:''
 +
**Genes involved in refolding denatured proteins and preventing protein aggregation were down-regulated, whereas the expression of genes that synthesize enzymes that hydrolyze proteins were up-regulated. The pathogenic cell thus exhibit reduced energetic investment in proper protein folding and increased rates of protein degradation. There is additionally a decreased investment in protein transport.
 +
**Similarly, genes involved in stimulating protein production are being down-regulated in the pathogenic cells. This decreased investment in protein production suggests that these cells are more invested in conserving energy, perhaps in preparation for energy deficits.
 +
**Genes involved in critical energy-producing metabolic pathways such as glycolysis and chorismate biosynthesis and being down-regulated in the pathogenic cells. At the same time, sugar-dependent active transport is being reduced. Both of these processes signify increased conservation of chemical energy by reducing consumption of carbohydrates in cellular processes. These cell thus appear better equipped to handle energy deficit.
  
−
Number Changed (in column I) greater than or equal to 4 or 5 AND less than 100
+
In their 2002 paper "Host-induced epidemic spread of the cholera bacterium", Merrell et al. present the argument that the human gastrointestinal tract provides a suitable growth environment for pathogenic ''Vibrio cholerae'' bacteria, which then enter an induced hyperinfectious state. This transition, however, is completely contingent on the survival of ''V. cholerae'' in the highly acidic conditions of the human gastrointestinal tract. Thus, ingesting a relatively large quantity of ''V. cholerae'' is typically necessary for infection under normal stomach pH conditions (if the pH is lowered, infection is easier). These findings suggest that the acidic environment of the human gastrointestinal tract places a great deal of selective stress on ''V. cholerae'', favoring expression patterns that confer increased resistance to these stresses in pathogenic ''V. cholerae''. The results of my microarray analysis support this hypothesis, as they indicate significantly different expression patterns that confer resistance to the stresses present in the human gastrointestinal tract. Many of these expression patterns revolve around proteins. The pathogenic ''V. cholerae'' in the microarray experiment exhibited decreased investment in the correction of protein folding errors and instead favored faster protein degradation. This expression change enables the pathogenic bacteria to better survive in the low pH conditions of the gastrointestinal tract, where proteins would denature rapidly. Instead of constantly trying to refold the denatured proteins, the cell instead slows down protein synthesis on the whole (which also conserves energy) and accelerates degradation of proteins through hydrolysis, which fights against aggregation of denatured proteins. In addition to this, the pathogenic ''V. cholerae'' exhibited decreased synthesis of proteins necessary for  energy-producing metabolic pathways such as glycolysis and chorismate biosynthesis and decreased sugar-dependent active transport. These changes allow for the conservation of sugars necessary for energy-production, which confers the pathogenic bacteria greater ability to survive in stressed conditions such as in the gastrointestinal tract. In addition, these changes also reduce intake of molecules from the external environment, which could be beneficial to survival in an environment filled with digestive enzymes. Evidently, the pathogenic ''V. cholerae'' in this microarray experiment exhibited expression changes that increased their ability to survive in humans' gastrointestinal tracts and thus incite an opportunistic infection.
−
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 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.'''
 
 
* '''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.'''
 
* '''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.'''
−
 
+
** The .gmf file can be found here: [[File:Merrell Compiled Raw Data Vibrio BK 20151025.gmf]].
−
====Conclusion====
+
−
 
+
−
* Write a paragraph that briefly summarizes and gives a scientific conclusion for the work that you did for part 1 and 2 this week.
+
−
 
+
−
=====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: <code>.EX.txt</code>
+
−
# Your Expression Dataset file: <code>.gex</code>
+
−
# Your GO results file: <code>XXX-CriterionX-GO.txt</code>
+
−
# Your GO results saved as an Excel spreadsheet with filters applied: <code>.xls</code>
+
−
# The MAPP you looked at: <code>.mapp</code>
+
−
# The MAPPFinder GO mappings file: <code>.gmf</code>
+
  
 
==Conclusion==
 
==Conclusion==
 +
This week, I conducted an analysis of the raw data generated by a real DNA microarray experiment. This process started with statistically analyzing the raw data to yield normalized log fold change values for the different genes and calculate their statistical significance. Subsequently, this data was reformatted and imported into the program GenMAPP, which used the results of this statistical analysis to create a global gene expression profile for the microarray data. By identifying the most significant gene ontology terms in this expression profile, insights about the expression changes in the experimental microarray group were gleaned. These expression changes were applied to the experimental context of the experiment performed by Merrell et al. In doing so, conclusions were drawn about why pathogenic ''V. cholerae'' exhibit these expression changes, which I argued was because these pathogenic bacteria require higher resistance to the stresses present in the human gastrointestinal tract to survive.
  
 
==Links==
 
==Links==

Latest revision as of 01:18, 27 October 2015

Files Generated in the This Week's Analysis

Links to files below can all be found within the electronic notebook at the point where they were created. For easy access, they are listed here as well.

  1. Analyzed microarray data: File:Merrell Compiled Raw Data Vibrio BK 20151015.xls.
  2. For GenMAPP text file: File:Merrell Compiled Raw Data Vibrio BK 20151015- Tab Delimited.txt.
  3. Exceptions file: File:Merrell Compiled Raw Data Vibrio BK 20151015.EX.txt.
  4. Expression Dataset files:
  5. GO term MAPP: File:Polyribonucleotide nucleotidyltransferase activity.mapp.
  6. MAPPFinder Results:
  7. .gmf file: File:Merrell Compiled Raw Data Vibrio BK 20151025.gmf.

Statistical Analysis of Vibrio cholerae Microarray Data (Part 1)

Normalizing the Log Ratios for the Set of Slides in the Experiment

This section dictates the steps necessary to scale and center the raw microarray data:

  • To begin, I created a new Worksheet in my Excel file entitled "scaled_centered".
  • I went back to the original "compiled_raw_data" worksheet and copied over all the data into the new "scaled_centered" worksheet.
  • I inserted two rows in between the top row of headers and the first data row entitled "Average" (cell A2) "StdDev" (cell A3).
    • I computed the Average log ratio for each chip by inputting the following equation into cell B2 and then pasting it into the rest of the "Average" column: =AVERAGE(B4:B5224).
    • I computed the Standard Deviation of the log ratios on each chip by inputting the following equation into cell B3 and then pasting it into the rest of the "StdDev" column: =STDEV(B4:B5224).
  • I created a new set of headings for the scaled and centered data by copying over the data column headings and then pasting them to the right of the last data column. I edited the names of the columns so that they now read: A1_scaled_centered, A2_scaled_centered, etc.
  • In cell N4 (column with the heading A1_scaled_centered), I typed the following equation: <code)=(B4-B$2)/B$3</code>. In this case, we want the data in cell B4 to have the average subtracted from it (cell B2) and be divided by the standard deviation (cell B3). We use 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 will paste it for the entire column of 5221 genes. Why is this important?
    • Adding the dollar sign before the 2 and 3 ensured that the equation for each individual gene in column B drew from the overall average and standard deviation for the column. Maintaining these overall values in the equation is critical to yielding scaled and centered outputs for each gene. If the dollar signs were not included, Excel would assume that for each gene, it would subtract by the value two rows above and then divide by the value one row above (e.g. for B80, the equation would be (B80-B78)/B79 when in reality we would want (B80-B2)/B3). This would yield extraneous results.
  • I copied this equation to the rest of the column and then adapted it for all "_scaled_centered" columns.

Performing Statistical Analysis on the Ratios

This section details the steps necessary to perform statistical analysis on the scaled and centered data produced in the section above:

  • For this section, I created a new worksheet and name it "statistics".
  • I went back to the "scaled_centered" worksheet and copied over both the first column ("ID") and copied the columns that are designated "_scaled_centered".
  • I deleted rows 2 and 3 where it says "Average" and "StDev" so that the data rows with gene IDs were immediately below the header row 1.
  • I created three new columns to the right of the copied ones with the following headers: "Avg_LogFC_A", "Avg_LogFC_B", and "Avg_LogFC_C".
  • I computed the average log fold change for the replicates for each patient by typing the equation:
=AVERAGE(B2:E2)
into cell N2 and then copying/pasting it into the rest of the three columns, adapting it as necessary.
  • I created a new column to compute the average of the averages with the header "Avg_LogFC_all". I then created the equation to compute the average of the three previous averages you calculated and paste it into this entire column.
  • I created a new column with the header "Tstat" to compute the T statistic for each scaled and centered average log ratio. In this column, I entered the following equation and pasted it into the rest of the column: 
=AVERAGE(N2:P2)/(STDEV(N2:P2)/SQRT(3))
  • I created one last column with the heading "Pvalue". In the cell below the label, I entered the following equation and then copied it into the rest of the column: 
=TDIST(ABS(R2),2,2)

Calculating the Bonferroni p value Correction

It is necessary to perform adjustments to the p value to correct for the multiple testing problem. Therefore, I first calculated the Bonferroni p value correction on the p values calculated in the above section:

  • I labelled the next two columns to the right of "Pvalue" with the same label: "Bonferroni_Pvalue".
  • I typed the equation =S2*5221 into the first Bonferroni column and pasted it throughout.
  • Next, I replaced any corrected p value that was greater than 1 with the number 1 by typing the following formula into the first cell below the second Bonferroni_Pvalue header: =IF(T2>1,1,T2).

Calculating the Benjamini & Hochberg p value Correction

The second p value correction I performed was the Benjamini & Hochberg correction, the methods of which are presented below:

  • I created a new worksheet named "B-H_Pvalue".
  • In this worksheet, I copied over the "ID" column from the previous worksheet into the first column of the new worksheet.
  • Next, I added a new column on the very left and named it "MasterIndex". Under this heading, I added a numbered list from 1 to 5221 (the number of genes on the microarray).
  • I copied over the unadjusted p values from your previous worksheet and pasted them into Column C.
  • I selected all of columns A, B, and C and sorted by ascending values on Column C.
    • This was done by clicking the sort button from A to Z on the toolbar and sorting by column C, smallest to largest.
  • I created a new column and labelled it with the header "Rank" in cell D1 to house another numbered list from 1 to 5221. Because this was done for the sorted columns, these values represented the p value ranks, smallest to largest.
  • I created a two new columns to calculate the Benjamini and Hochberg p value correction and labeled them "B-H_Pvalue" (cell E1 and F1).
    • In the first column, I inputted the following formula and copied it throughout the column: =(C2*5221)/D2.
    • In the second column, I inputted the following formula and copied it throughout: =IF(E2>1,1,E2).
  • Next, I selected columns A through F and sorted them by the MasterIndex in Column A in ascending order.
  • Finally, I copied column F into the next column on the right of your "statistics" sheet.

Preparing the File for GenMAPP

  • I inserted a new worksheet and named it "forGenMAPP".
  • I copied over the entirety of the "statistics" worksheet to this new worksheet.
  • I selected Columns B through Q (all the fold changes) and formatted the cells to show only 2 decimal places.
  • I selected all the columns containing p values and formatted the cells to show only 4 decimal places.
  • I deleted the left-most Bonferroni p value column.
  • I inserted a column to the right of the "ID" column entitled "SystemCode" and filled the entire column with the letter "N".
  • After making the above changes, I saved the file as "Text (Tab-delimited) (*.txt)".

Sanity Check: Number of Genes Significantly Changed

To verify the results of the data analysis performed on the Vibrio cholerae DNA microarray data, I assessed the number of genes that were significantly changed at various p value cut-offs and also compared my data analysis with the published results of Merrell et al. (2002). These analyses were done in the "forGenMapp" tab of the Excel spreadsheet used for the data analysis.

  • Assessing the number of genes significantly changed
    • To answer the questions below, I used custom filter to display only the "Pvalue" data that met specific criterion (e.g. < 0.05).
    • How many genes have p value < 0.05? and what is the percentage (out of 5221)?
      • 948 of the 5221 genes (18.2%) had p values that were less than 0.05
    • What about p < 0.01? and what is the percentage (out of 5221)?
      • 235 of the 5221 genes (4.50%) had p values that were less than 0.01
    • What about p < 0.001? and what is the percentage (out of 5221)?
      • 24 of the 5221 genes (0.46%) had p values that were less than 0.001
    • What about p < 0.0001? and what is the percentage (out of 5221)?
      • 2 of the 5221 (0.038%) genes had p values that were less than 0.0001
    • To apply a more stringent criterion to the p values, I performed the Bonferroni and Benjamini and Hochberg corrections to the unadjusted p values. I then filtered the columns "Bonferroni_Pvalue" and "B-H_Pvalue" columns to only display data that met specific criterion.
    • How many genes are p < 0.05 for the Bonferroni-corrected p value? and what is the percentage (out of 5221)?
      • No genes (0.00%) had p values less than 0.05 for the Bonferroni-corrected p value
    • How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 5221)?
      • No genes (0.00%) had p values less than 0.05 for the Benjamini and Hochberg-corrected p value
  • Assessing the number of genes with expression changes
    • The "Avg_LogFC_all" indicated the size of the gene expression changes and their direction. Positive values increased relative to the control, and negative values decreased relative to the control.
      • Keeping the (unadjusted) "Pvalue" filter at p < 0.05, I filtered the "Avg_LogFC_all" column to show all genes with an average log fold change greater than zero. How many are there? (and %)
        • 352 of the 5221 genes (6.74%) exhibited significant (p < 0.05) increases in gene expression
      • Keeping the (unadjusted) "Pvalue" filter at p < 0.05, I filtered the "Avg_LogFC_all" column to show all genes with an average log fold change less than zero. How many are there? (and %)
        • 596 of the 5221 genes (11.4%) exhibited significant (p < 0.05) decreases in gene expression
      • What about an average log fold change of > 0.25 and p < 0.05? (and %)
        • 339 of the 5221 genes (6.49%) exhibited significant (p < 0.05) increases in gene expression that exceeded an average log fold change of 0.25
      • Or an average log fold change of < -0.25 and p < 0.05? (and %)
        • 579 of the 5221 genes (11.1%) exhibited significant (p < 0.05) decreases in gene expression that were less than an average log fold change of -0.25
  • What criteria did Merrell et al. (2002) use to determine a significant gene expression change? How does it compare to our method?
    • Merrel et al. identified statistically significant changes in gene expression by imputing normalized intensity ratios for expression into the program Statistical Analysis for Microarrays (SAM). Within the program, they ran a two-class SAM analysis using the strain grown in vitro (class I) and each individual stool sample (class II). This test isolated expression levels that were of a significantly different magnitude (at least a twofold change) across all patient samples as statistically significant expression changes. This method for significance differed from ours in two primary ways:
      1. It judged significant differences based on order of magnitude changes in the normalized intensity rations (twofold change) as opposed to running T tests and calculating p-values.
      2. It looked for consistent significant changes in expression across all patient samples instead of looking for significance in averaged log fold changes for all patients.
  • For the GenMAPP analysis below, I used 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 the analysis.

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. I reviewed the "forGenMAPP" Excel worksheet to compare these findings to the results of my personal data analysis.

  • What are the fold changes and p values (of these genes)? Are they significantly changed in the analysis?
    • VC0028 (2 entries)
      • Fold changes: 1.65, 1.27
      • P values: 0.0474, 0.0692
      • Significantly changed? The expression of the first gene with this ID significantly changed, but that of the second gene with this ID did not.
    • VC0941 (2 entires)
      • Fold changes: 0.09, -0.28
      • P values: 0.6759, 0.1636
      • Significantly changed? The expression of neither gene with this ID significantly changed.
    • VC0869 (5 entries)
      • Fold changes: 1.50, 1.59, 1.95, 2.20, 2.12
      • P values: 0.0174, 0.0463, 0.0227, 0.0020, 0.0200
      • Significantly changed? The expression of all genes with this ID significantly changed.
    • VC0051 (2 entries)
      • Fold changes: 1.92, 1.89
      • P values: 0.0139, 0.0160
      • Significantly changed? The expression of both genes with this ID significantly changed.
    • VC0647 (3 entries)
      • Fold changes: -1.11, -0.94, -1.05
      • P values: 0.0003, 0.0125, 0.0051
      • Significantly changed? The expression of all genes with this ID significantly changed.
    • VC0468 (1 entry)
      • Fold change: -0.17
      • P value: 0.3350
      • Significantly changed? The expression of this gene did not significantly change.
    • VC2350 (1 entry)
      • Fold changes: -2.40
      • P value: 0.0130
      • Significantly changed? The expression of this gene significantly changed.
    • VCA0583 (1 entry)
      • Fold change: 1.06
      • P value: 0.1011
      • Significantly changed? The expression of this gene did not significantly change.

MAPPFinder Analysis of Vibrio cholerae Microarray Data (Part 2)

Mapping Onto Biological Pathways (GenMAPP & MAPPFinder)

Before beginning the mapping process, it is necessary to load the correct gene database into GenMAPP:

  • I download the 2009 Vibrio cholerae gene database by following this link to the XMLPipeDB SourceForge Download page. My homework partner Veronica downloaded the 2010 version.
  • I saved the above file into the folder C:\GenMAPP 2 Data\Gene Databases and extracted it.
  • Within GenMAPP, I loaded the 2009 gene database by selecting Data > Choose Gene Database and choosing the appropriate file from the directory C:\GenMAPP 2 Data\Gene Databases.

GenMAPP Expression Dataset Manager Procedure

Once the appropriate gene database has been loaded into GenMAPP, the expression dataset can be uploaded and configured:

  • I opened the Expression Dataset Manger from the Data drop-down list in GenMAPP.
  • I selected New Dataset from the Expression Datasets menu and choose the tab-delimited text file formatted for GenMAPP (.txt).
  • Upon specifying that all data was numerical, the Expression Dataset Manager converted my data to .gex file. This process took approximately one minute to complete. In addition to converting the data to a .gex file, an exceptions file (.EX.txt) was also produced, as 772 errors were reportedly detected in the raw data.
    • The .gex file generated can be found here: File:Merrell Compiled Raw Data Vibrio BK 20151015.gex
    • The .EX file generated can be found here: File:Merrell Compiled Raw Data Vibrio BK 20151015.EX.txt
    • 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.
      • GenMAPP errors message.PNG
      • The above screenshot shows the error message I received after using the Expression Dataset Manager to convert my raw data file. A reported 772 errors were detected in my raw data.
      • Upon opening the .EX.txt file, I found the error message "Gene not found in OrderedLocusNames or any related system." repeated several times.
        • To determine if this was the only error message, I uploaded the exceptions file to my LMU CMSI directory using the command scp /Users/brandonklein/Desktop/Merrell_Compiled_Raw_Data_Vibrio_BK_20121015.EX.txt bklein7@my.cs.lmu.edu:~bklein7. In the command line, I used the following command sequence to determine how often the "Gene not found..." error message was repeated: grep "Gene not found"Merrell_Compiled_Raw_Data_Vibrio_BK_20121015.EX.txt | wc. This yielded the output 772 23932 145791, confirming that the above error message was responsible for all 772 reported errors.
      • 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?
        • With the 2009 Vibrio cholerae gene database loaded into GenMAPP, I encountered 772 errors when converting the analyzed Merrell et al. microarray data. When converting the exact same data with the 2010 Vibrio cholera gene database loaded into GenMapp, Veronica encountered 121 errors. All error messages for both of us were the same: "Gene not found in OrderedLocusNames or any related system". Therefore, I had more errors. I believe this occured because the 2009 version of the Vibrio cholerae database was less developed/complete than the more recent 2010 version. Thus, it likely had less Gene listings, and therefore less of the Ordered Locus Names used by Merrell matched to the database.
  • I customized the new Expression Dataset by creating a Color Sets= with instructions to GenMAPP for displaying data on MAPPs. The new Color Set was entitled "LogFoldChange".
    • First, I created a criterion for this color set to label genes that demonstrated a significant increase in their expression.
      • I specified the Gene value as "Avg_LogFC_all" for the Vibrio dataset.
      • I activated the Criteria Builder by clicking the New button and named the criterion "Increased".
      • I selected the color for this criterion as red using the color box.
      • I stated the criterion as follows and added it to the Criteria List: [Avg_LogFC_all] > 0.25 AND [Pvalue] < 0.05
    • Second, I created a criterion for this color set to label genes that demonstrated a significant decrease in their expression.
      • I specified the Gene value as "Avg_LogFC_all" for the Vibrio dataset.
      • I activated the Criteria Builder by clicking the New button and named the criterion "Decreased".
      • I selected the color for this criterion as green using the color box.
      • I stated the criterion as follows and added it to the Criteria List: [Avg_LogFC_all] < -0.25 AND [Pvalue] < 0.05
  • Upon entering these color sets, I savedthe entire Expression Dataset by selecting Save from the Expression Dataset menu.

MAPPFinder Procedure

  • I launched the MAPPFinder program from within GenMAPP and ensured that the 2009 Gene Database was still loaded into GenMAPP.
  • I clicked on the button "Calculate New Results" followed by "Find File", at which point I chose my .gex file.
  • Veronica and I both chose to filter the data with the "Decreased" criterion present within the LogFoldChange Color Set.
  • I checked the boxes next to "Gene Ontology" and "p value", specified the results file, and then clicked "Run MAPPFinder".
    • This analysis took several minutes to complete.
  • I clicked on the menu item "Show Ranked List" to see a list of the most significant Gene Ontology terms.
    • List the top 10 Gene Ontology terms.
      • TOP10.PNG
      1. protein folding
      2. chorismate metabolic process
      3. aromatic amino acid family biosynthetic process
      4. unfolded protein binding
      5. cytoplasm
      6. intracellular part
      7. localization
      8. nucleotide catabolic process
      9. locomotion
      10. aromatic amino acid family metabolic process
    • 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?
      • Veronica's top 10 Gene Ontology terms using the 2010 Vibrio database were as follows:
          1. signal transduction
          2. molecular transducer activity
          3. signal transducer activity
          4. membrane
          5. peptidyl-histidine modification
          6. peptidyl-histidine phosphorylation
          7. two-component sensor activity
          8. protein histidine kinase activity
          9. peptidyl-amino acid modification
          10. phosphotransferase activity, nitrogenous group as acceptor
      • The above list of Veronica's top 10 Gene Ontology terms was entirely different from my own, despite the fact that we both input the same microarray data into GenMAPP. However, there are several reasons that explain why this happened. First, Veronica used a more recent version of the Vibrio database (2010 vs. 2009), which matched GO terms to a large number of genes included in the microarray data that did not perviously have GO terms in the 2009 database. As evidence of this, I only retrieved GO terms for two of the gene ID's in the question below using the 2009 database, whereas Veronica retrieved GO terms for each of the gene ID's. Additionally, it is possible that new GO terms were added to the 2010 database that were not present prior. For example, when I searched one of the GO terms Veronica found associated with the gene ID VC0028, dihydroxy-acid dehydratase activity, I did not find any matches to this result. Finally, each time a MAPP is generated, a slightly different list of top Gene Ontology terms can be produced. I tested this by running multiple MAPPFinder analyses on my microarray data using the same 2009 database and retrieved slightly different results more than once.
  • I used MAPPFinder to find the Gene Ontology term(s) with which the following genes mentioned by Merrell et al. (2002) were associated with: VC0028, VC0941, VC0869, VC0051, VC0647, VC0468, VC2350, and VCA0583. This was done by typing the identifiers for these genes into the MAPPFinder browser gene ID search field, choosing "OrderedLocusNames" from the drop-down menu to the right of the search field, and clicking on the GeneID Search button. The GO term(s) that were associated with the genes were highlighted in blue. List the GO terms associated with each of those genes. (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?
    • Associated GO terms
      • VC0028: "No MAPPs or GO terms could be found for that OrderedLocusNames ID."
      • VC0941: "No MAPPs or GO terms could be found for that OrderedLocusNames ID."
      • VC0869: "No MAPPs or GO terms could be found for that OrderedLocusNames ID."
      • VC0051: "No MAPPs or GO terms could be found for that OrderedLocusNames ID."
      • VC0647: mRNA catabolic process, RNA processing, cytoplasm, RNA binding, 3'-5'-exoribonuclease activity, transferase activity, nucleotidyltransferase activity, and polyribonucleotide nucleotidyltransferase activity.
      • VC0468: "No MAPPs or GO terms could be found for that OrderedLocusNames ID."
      • VC2350: "No MAPPs or GO terms could be found for that OrderedLocusNames ID."
      • VCA0583: transport, outer membrane-bounded periplasmic space, and transporter activity.
    • Comparison with Veronica's results using the 2010 Vibrio database
      • As mentioned in the answer to the previous question, Veronica matched all of the above GO terms to gene IDs in the 2010 database. Thus, the 2010 database must have been updated to include a more complete listing of GO terms associated with specific gene IDs, perhaps reflecting research advancements. In addition to this, some of the GO terms we retrieved for the same gene IDs (such as VCA0583) were not the same. This also suggests that more refined or experimentally proven GO terms were matched with genes previously associated with GO terms in the 2010 database.
  • VC0647 Investigation: List 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 "polyribonucleotide nucleotidyltransferase activity". The MAPP this produced only contained the gene "PNP_VIBCH", which I found in the [UniProt Database]. The expression of this gene, referenced by the gene ID VC0647, significantly decreased in the microarray experiment.
    • I Double-clicked on the above gene box to find out more about the gene PNP_VIBCH. Click on the links to find out the function of this gene.
      • According to the [UniProt Database], the function of this gene is to degrade mRNA. It does this by coding for the production of the protein polyribonucleotide nucleotidyltransferase, which "catalyzes the phosphorolysis of single-stranded polyribonucleotides processively in the 3'- to 5'-direction".
    • The MAPP created while investigating polyribonucleotide nucleotidyltransferase activity can be found here: File:Polyribonucleotide nucleotidyltransferase activity.mapp.
  • Overall MAPPFinder results file: File:Merrell Compiled Raw Data Vibrio BK 20151015-Criterion1-GO.txt.
  • I opened a copy of the .txt file listed above in Excel to filter the results.
    • There were rows of information that gave background information on how MAPPFinder made the calculations at the top of the Excel file. Compare this information with your partner who used a different version of the Vibrio Gene Database. Which numbers are different? Why are they different?
      • When comparing the background information, I found several lines in my output file that were distinct from Veronica's using the 2010 Vibrio database:
        1. Veronica matched a dramatically larger number of probes (3159 vs. 578) to the decreased criterion. This was unexpected, as I had anticipated our results for this to be equal given that we input the same microarray data. However, it turned out there was a flaw in Veronica's decreased criterion, which read: [Avg_LogFC_All]>-0.25 AND [Pvalue]>0.05 instead of [Avg_LogFC_All] < -0.25 AND [Pvalue]<0.05.
        2. Due to the issue above, an equally dramatic difference was present between the number of probes meeting the criterion that matched with a UniProt ID. This was expected, as the evidence during this exercise suggested that the 2010 database was more complete, including more gene IDs. Indeed, even with the criterion error, the percentage of probes that met her criterion and also matched with a UniProt ID (97.37%) was greater than mine (81.83%). This supports the conclusion that the 2010 database included more gene IDs.
      1. The number of probes meeting the criterion AND linking to a GO term was different for both of us in much the same was as the example above. Although the strict numerical difference was exaggerated due to error (1571 vs. 254), the percentages still supported the fact that the 2010 database matched gene IDs to GO terms more frequently (49.73% vs. 43.94%).
      2. Finally, both the number of probes in the entire dataset that linked to UniProt IDs as well as to GO terms followed the trends already detailed above, proving those points. The 2009 database I used did in fact include less links to UniProt IDs and GO terms on the whole than the 2010 database.
    • I used the following filters to show the top 20 GO terms represented in my data for both the "Increased" and "Decreased" criteria:
Z Score (in column N) greater than 2
PermuteP (in column O) less than 0.05
Number Changed (in column I) greater than or equal to 5 AND less than 100
Percent Changed (in column L) greater than or equal to 30%
  • 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.
  • 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.
    • To begin the interpretation, I used the UniProt database in tandem with my MappFinder results and the filtered Excel file to discern both the function of specific genes included under the top 20 GO terms and whether those genes were down-regulated or up-regulated in the experiment. To facilitate this, I grouped the GO terms that were closely related according to the highlights I made in the filtered Excel file. For the sake of simplicity, I included just an interpretation of the results from this investigation for each group below.
      • Group 1: protein folding and unfolded protein binding
        • Decreased expression of genes that promote the proper folding of proteins in stress conditions.
          • Decreased synthesis of chaperone proteins.
        • Increased pepsidase activity, which promotes the hydrolysis of proteins.
      • Group 2: cis-trans isomerase activity and peptidyl-prolyl cis-trans isomerase activity
        • Decreased expression of genes that promote proper protein folding and prevent protein aggregation (similar to Group 1).
      • Group 3: zinc ion binding
        • Decreased synthesis of proteins involved in glycolytic processes.
        • Increased pepsidase activity.
      • Group 4: sugar:hydrogen symporter activity, solute:hydrogen symporter activity, and cation:sugar symporter activity
        • Decreased sugar-dependent active transport into membrane.
      • Group 5: protein-N(PI)-phosphohistidine-sugar phosphotransferase activity, sugar transmembrane transporter activity, and carbohydrate transmembrane transporter activity
        • Effects similar to Group 4.
      • Group 6: phosphoenolpyruvate-dependent sugar phosphotransferase system and carbohydrate transport
        • Decreased intake of sugars from the external environment.
        • Effects similar to Group 4.
      • Group 7: translation regulator activity and translation factor activity, nucleic acid binding
        • Decreased stimulation of aa-tRNA binding, which slows down the rate of protein production in the cell.
      • Group 8: endonuclease activity
        • Decreased hydrolysis of nucleic acids.
      • Group 9: glucose metabolic process
      • Decreased synthesis of proteins necessary for glycolytic processes (similar to Group 3).
      • Group 10: aromatic compound biosynthetic process
        • Decreased synthesis of proteins involved in the chorismate biosynthesis pathway, which produced energy for the cell.
      • Group 11: intracellular protein transport and intracellular transport
        • Decreased intracellular protein transport.
  • Overall takeaways from investigating the functions of the top 20 genes that exhibited significant expression changes in the Vibrio cholerae microarray experiment:
    • Genes involved in refolding denatured proteins and preventing protein aggregation were down-regulated, whereas the expression of genes that synthesize enzymes that hydrolyze proteins were up-regulated. The pathogenic cell thus exhibit reduced energetic investment in proper protein folding and increased rates of protein degradation. There is additionally a decreased investment in protein transport.
    • Similarly, genes involved in stimulating protein production are being down-regulated in the pathogenic cells. This decreased investment in protein production suggests that these cells are more invested in conserving energy, perhaps in preparation for energy deficits.
    • Genes involved in critical energy-producing metabolic pathways such as glycolysis and chorismate biosynthesis and being down-regulated in the pathogenic cells. At the same time, sugar-dependent active transport is being reduced. Both of these processes signify increased conservation of chemical energy by reducing consumption of carbohydrates in cellular processes. These cell thus appear better equipped to handle energy deficit.

In their 2002 paper "Host-induced epidemic spread of the cholera bacterium", Merrell et al. present the argument that the human gastrointestinal tract provides a suitable growth environment for pathogenic Vibrio cholerae bacteria, which then enter an induced hyperinfectious state. This transition, however, is completely contingent on the survival of V. cholerae in the highly acidic conditions of the human gastrointestinal tract. Thus, ingesting a relatively large quantity of V. cholerae is typically necessary for infection under normal stomach pH conditions (if the pH is lowered, infection is easier). These findings suggest that the acidic environment of the human gastrointestinal tract places a great deal of selective stress on V. cholerae, favoring expression patterns that confer increased resistance to these stresses in pathogenic V. cholerae. The results of my microarray analysis support this hypothesis, as they indicate significantly different expression patterns that confer resistance to the stresses present in the human gastrointestinal tract. Many of these expression patterns revolve around proteins. The pathogenic V. cholerae in the microarray experiment exhibited decreased investment in the correction of protein folding errors and instead favored faster protein degradation. This expression change enables the pathogenic bacteria to better survive in the low pH conditions of the gastrointestinal tract, where proteins would denature rapidly. Instead of constantly trying to refold the denatured proteins, the cell instead slows down protein synthesis on the whole (which also conserves energy) and accelerates degradation of proteins through hydrolysis, which fights against aggregation of denatured proteins. In addition to this, the pathogenic V. cholerae exhibited decreased synthesis of proteins necessary for energy-producing metabolic pathways such as glycolysis and chorismate biosynthesis and decreased sugar-dependent active transport. These changes allow for the conservation of sugars necessary for energy-production, which confers the pathogenic bacteria greater ability to survive in stressed conditions such as in the gastrointestinal tract. In addition, these changes also reduce intake of molecules from the external environment, which could be beneficial to survival in an environment filled with digestive enzymes. Evidently, the pathogenic V. cholerae in this microarray experiment exhibited expression changes that increased their ability to survive in humans' gastrointestinal tracts and thus incite an opportunistic infection.

  • 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

This week, I conducted an analysis of the raw data generated by a real DNA microarray experiment. This process started with statistically analyzing the raw data to yield normalized log fold change values for the different genes and calculate their statistical significance. Subsequently, this data was reformatted and imported into the program GenMAPP, which used the results of this statistical analysis to create a global gene expression profile for the microarray data. By identifying the most significant gene ontology terms in this expression profile, insights about the expression changes in the experimental microarray group were gleaned. These expression changes were applied to the experimental context of the experiment performed by Merrell et al. In doing so, conclusions were drawn about why pathogenic V. cholerae exhibit these expression changes, which I argued was because these pathogenic bacteria require higher resistance to the stresses present in the human gastrointestinal tract to survive.

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