Difference between revisions of "Jwoodlee Week 8"

Revision as of 06:56, 26 October 2015

Electronic Lab Notebook

I downloaded the raw data from the Merrell et al. experiment, found here.

I then followed the procedure on that page:

Part 1 Procedure

Procedure taken from BIOL398-01:Sample_Microarray_Analysis_Vibrio_cholerae. Reworded to reflect what I did.

I scaled and centered the data (between chip normalization) by performing the following operations: Inserted a new Worksheet into the excel file, and named it "scaled_centered". Went back to the "compiled_raw_data" worksheet, selected all(command A) and copied(command C). Went to the "scaled_centered" worksheet, clicked on the upper, left-hand cell and pasted.

Inserted two rows in between the top row of headers and the first data row. In cell A2, typed "Average" and in cell A3, typed "StdDev". I then computed the average for the first column. In cell B2, I typed the following equation: =AVERAGE(B4:B5224) and pressed "Enter". I then computed the standard deviation for the first column. In cell B3, I typed the following equation: =STDEV(B4:B5224) and pressed "Enter". I copied these two equations (cells B2 and B3) and pasted them into the empty cells in the rest of the columns to calculate for the rest of the columns.

I copied the column headings for all of my data columns and then pasted them to the right of the last data column so that I had a second set of headers above blank columns of cells. I then edited the names of the columns so that they now read: A1_scaled_centered, A2_scaled_centered, etc. In cell N4, I typed the following equation: =(B4-B$2)/B$3 We wanted the data in cell B4 to have the average subtracted from it (cell B2) and be divided by the standard deviation (cell B3). The dollar signs make sure that the proper cells are always used. I then copied and pasted this equation into the entire column across all rows.

In order to perform statistical analysis on the data, I inserted a new worksheet called "statistics". Went back to the "scaled_centered" worksheet and copied the ID column and pasted the data from that column into the first column of the statistics worksheet.

I went back to the "scaling_centering" worksheet and copied the columns that are designated "scaled_centered".

I went back to my new worksheet and clicked on the B1 cell. I then selected "Paste Special" from the Edit menu so I could get the values from the previous worksheet without the equations. I then deleted rows 2 and 3 that were there previously for the average and standard deviation. I then added 3 new headers to blank columns to the right: "Avg_LogFC_A", "Avg_LogFC_B", and "Avg_LogFC_C".

I then computed the average log fold change for the replicates for each patient by typing the equation: =AVERAGE(B2:E2) into cell N2. I then pasted this equation into the rest of the column. I then created the equations for patients B and C and pasted them into their respective columns. The equations are: =AVERAGE(J2:M2) and =AVERAGE(F2:I2) I then got the average of the averages and put them into the next column on the right with the header: "Avg_LogFC_all". The equation is: =AVERAGE(N2:P2)

I inserted a new column next to the "Avg_LogFC_all" column and labeled the column "Tstat". I then entered the equation: =AVERAGE(N2:P2)/(STDEV(N2:P2)/SQRT(number of replicates)) (In this case the number of replicates was 3). I spread this equation out to the whole column. I labeled the top cell in the next column "Pvalue". In the cell below the label, I entered the equation: =TDIST(ABS(R2),degrees of freedom,2)

The number of degrees of freedom is the number of replicates minus one, so in this case there were 2 degrees of freedom. I then pasted the equation throughout the whole column.

Now I had to adjust the pvalues to account for the multiple testing problem. I labeled the next two columns to the right with the same label, Bonferroni_Pvalue. I typed the equation =S2*5221, and copied it to the entire first Bonferroni_Pvalue column. In the second Bonferroni_Pvalue column I replaced any corrected p value that is greater than 1 by the number 1 by typing =IF(T2>1,1,T2) into the first cell below the second Bonferroni_Pvalue header. I then copied that equation into the entire column.

I inserted a new worksheet named "B-H_Pvalue" where the Benjamini and Hochberg Pvalue correction would be calculated. I copy and pasted the ID column from the previous worksheet into this new one. I inserted a new column on the very left and name it "MasterIndex". I typed a "1" in cell A2 and a "2" in cell A3 and used this pattern to fill out the rest of the column with numbers 1-5221. I pasted the only values(not the equations) of my unadjusted pvalues into my new worksheet at column C. I then selected all of columns A, B, and C, and sorted by ascending values on Column C. I added the header "Rank" in cell D1, where a series of numbers in ascending order from 1-5221 will reside. This is the p value rank, smallest to largest. I used the same trick as before to populate this column with ascending numbers. I typed B-H_Pvalue in cell E1, and typed the following formula in cell E2: =(C2*5221)/D2 and pressed enter and applied that to the whole column. I then typed B_HPvalue into cell F1. Then I added =IF(E2>1,1,E2) into cell F2 and pressed enter and applied it to the whole column. I then sorted columns A through F in order by the MasterIndex. I copied column F and used paste special to paste it into the next column on the right of the "statistics" sheet.

Now it was time to prepare the file for GenMapp. I inserted a new worksheet and named it "forGenMAPP". I then pasted the statistic worksheet into the forGenMapp worksheet by values. To prepare the data for GenMapp, I selected Columns B through Q (all the fold changes), selected the menu item Format > Cells, and under the number tab, selected 2 decimal places. I selected all the columns containing p values. Selected the menu item Format > Cells, and under the number tab, selected 4 decimal places. I deleted the left-most Bonferroni p value column, and preserved the one that shows the result of the "if" statement. I inserted a column to the right of the "ID" column, typed the header "SystemCode" into the top cell of this column, and filled the entire column (each cell) with the letter "N". I selected the menu item File > Save As, and choose "Text (Tab-delimited) (*.txt)" from the file type drop-down menu. This text file would be what I use later on when I am working with GenMapp

Sanity Checks:

Before I moved on to the GenMAPP/MAPPFinder analysis, I needed to verify that I did the analysis correctly.

I found out the number of genes that were significantly changed at various p value cut-offs and also compared the data analysis with the published results of Merrell et al. (2002).

I opened my spreadsheet and went to the "forGenMAPP" tab.

I clicked on cell A1 and selected the menu item Data > Filter > Autofilter. I clicked on the drop-down arrow on the "Pvalue" column, selected "Custom", and in the window that appeared, set a criterion that will filter the data so that the Pvalue has to be less than 0.05.

1. How many genes have p value < 0.05? and what is the percentage (out of 5221)?
   • I found that 948 genes had a pvalue of less than 0.05 which is about 18%.
2. What about p < 0.01? and what is the percentage (out of 5221)?
   • 235, 4.5%
3. What about p < 0.001? and what is the percentage (out of 5221)?
   • 24, 0.46%
4. What about p < 0.0001? and what is the percentage (out of 5221)?
   • 2, 0.04%
5. How many genes are p < 0.05 for the Bonferroni-corrected p value? and what is the percentage (out of 5221)?
   • On the "forGenMapp" worksheet there are 0 genes(0%).
6. How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 5221)?
   • On the "forGenMapp" worksheet there are 0 genes(0%).
7. 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 %)
   • 352, 6.7%
8. 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 %)
   • 596, 11.41%
9. What about an average log fold change of > 0.25 and p < 0.05? (and %)
   • 339, 6.49%
10. Or an average log fold change of < -0.25 and p < 0.05? (and %)
    • 579, 11.08%
11. What criteria did Merrell et al. (2002) use to determine a significant gene expression change? How does it compare to our method?
    • They used a program called SAM and an "intensity ratio" which allowed them to identify a statistically significant change. They grew a strain in vitro and compared it to the stool sample and defined a significant change in gene expression as at least a twofold change.

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?

pvalue:column s foldchange: column q

• • VC0028
    • fold change: 1.65, pvalue: 0.0474
  • VC0941
    • fold change: 0.09, pvalue:0.6759
  • VC0869
    • fold change: 1.59, pvalue:0.0463
  • VC0051
    • fold change: 1.92, pvalue:0.0139
  • VC0647
    • fold change: -1.11, pvalue:0.0003
  • VC0468
    • fold change: -0.17, pvalue:0.3350
  • VC2350
    • fold change: -2.40, pvalue:0.0130
  • VCA0583
    • fold change: 1.06, pvalue:0.1011

Part 2 Procedure

Procedure from here.

I used the Vc-Std_External_20101022.gdb XMLPipeDB which was downloaded from the SourceForge Download page.

After launching the GenMAPP Program, I checked to make sure the correct Gene Database was loaded.

I selected the Data menu from the main Drafting Board window and chose Expression Dataset Manager from the drop-down list.

From the Expression Datasets menu, I selected the tab-delimited text file that I formatted for GenMAPP (.txt).

The Expression Dataset Manager converted the data and generated a .EX. 121 errors recorded.

Opening the .EX in excel I determined that I had 121 errors of the same type. The error was: "Gene not found in OrderedLocusNames or any related system."

1. 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?
   • Mahrad had 772 errors using the 2009 database, so he had much more. Besides knowing the creators of the databases and having a general idea of the genes inside, I don't know that much about them. From what I know, I would say that either in 2010 more genes were added to the database or different IDs were used.

Under Dondi's guidance I made two color sets. I selected the "Avg_LogFC_all" to be used as the Gene Value from the drop down list. Using the formulas [Avg_LogFC_All] > 0.25 AND [Pvalue] < 0.05 and [Avg_LogFC_All] < -0.25 AND [Pvalue] < 0.05, I set the criteria. The former was increased and the latter was decreased. I then saved the expression dataset by selecting save from the expression dataset menu.

MAPPFinder Procedure:

I launched the MAPPFinder program, clicked on "Calculate New Results".

I clicked on "Find File" and chose the .gex file that was generated.

I chose "decreased" as my criteria of choice. I then checked "Gene Ontology" and "pvalue" I then ran MappFinder.

I clicked on show ranked list and got the following results. List the top 10 Gene Ontology terms in your individual journal entry. (decreased, 2010)

• glucose catabolic process
• hexose catabolic process
• glycolysis
• monosaccharide catabolic process
• cytoplasm
• alcohol catabolic process
• cellular carbohydrate catabolic process
• glucose metabolic process
• protein folding
• hexose metabolic process

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 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 PermuteP (in column O) less than 0.05 You will use these two filters depending on the number of terms you have: Number Changed (in column I) greater than or equal to 4 or 5 AND less than 100 Percent Changed (in column L) greater than or equal to 25-50% Save your changes to an Excel spreadsheet. Select File > Save As and select Excel workbook (.xls) from the drop-down menu. Your filter settings won’t be saved in a .txt file. Are any of your filtered GO terms closely related to one another, meaning are they a direct child or parent to another term in the list? You can judge this by comparing your spreadsheet with the MAPPFinder browser. Highlight the terms that fit this relationship with the same color in your Excel spreadsheet. Upload your .xls file to your journal page. Interpret your results. Look up the definitions for any GO terms that are unfamiliar to you. The "official" definitions for GO terms can be found at http://www.geneontology.org. You can use one of the online biological dictionaries as a supplement, if needed.

Write a paragraph relating the results of this GO analysis to the experiment performed (comparing laboratory-grown and patient-derived Vibrio cholerae. You need to give a biological interpretation of what do each of these GO terms in your filtered list have to to with the pathogenecity of the bacterium? You may consult with your partner on this, but your explanation on your individual journal page needs to be in your own words.

This is where the real "brain power" comes in with interpreting DNA microarray data. Even experienced scientists struggle with this part. Use your creativity as a scientist to stretch your brain in this question. 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 Write a paragraph that briefly summarizes and gives a scientific conclusion for the work that you did for part 1 and 2 this week.

File:ModifiedDataMerrellJwoodlee.gmf

File:ModifiedDataMerrellJwoodlee-Criterion1-GO filters.xls

File:ModifiedDataMerrellJwoodlee-Criterion1-GO.txt

File:ModifiedDataMerrellJwoodlee.gex

File:ModifiedDataMerrellJwoodlee.EX.txt

File:ModifiedDataMerrellJwoodleeText.txt

File:ModifiedDataMerrellJwoodlee.xls

File:Metal ion binding Jwoodlee.mapp

BIOL 367, Fall 2015, User Page, Team Page