Difference between revisions of "Jwoodlee Week 8"

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(→‎Electronic Lab Notebook: added answers part 1)
(→‎Part 2 Procedure: added answer clarification)
 
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In cell N4, I typed the following equation:
 
In cell N4, I typed the following equation:
 
=(B4-B$2)/B$3
 
=(B4-B$2)/B$3
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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.
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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).  Why is this imporant? The dollar signs make sure that the proper cells are always used, the values are always taken from B2 and B3 instead of incrementing.
 
I then copied and pasted this equation into the entire column across all rows.  
 
I then copied and pasted this equation into the entire column across all rows.  
  
Line 118: Line 118:
 
foldchange: column q
 
foldchange: column q
 
**VC0028
 
**VC0028
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***fold change: 1.65, pvalue: 0.0474
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***fold change: 1.65, pvalue: 0.0474, significantly changed, the pvalue is less than .05 and the fold change is greater than .25.
 +
***fold change: 1.27, pvalue: 0.0692, not significantly changed, the pvalue is greater than .05
 
**VC0941
 
**VC0941
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***fold change: 0.09, pvalue:0.6759
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***fold change: 0.09, pvalue:0.6759, not significantly changed, the pvalue is greater than .05
 +
***fold change: -0.28, pvalue:0.1636, not significantly changed
 
**VC0869
 
**VC0869
−
***fold change: 1.59, pvalue:0.0463
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***fold change: 1.59, pvalue:0.0463, significantly changed
 +
***fold change: 2.12, pvalue: 0.02  significantly changed
 +
***fold change: 1.50 , pvalue:0.0174 significantly changed
 +
***fold change: 2.20, pvalue: 0.002 significantly changed
 +
***fold change: 1.95, pvalue:0.0227 significantly changed
 
**VC0051
 
**VC0051
−
***fold change: 1.92, pvalue:0.0139
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***fold change: 1.92, pvalue:0.0139 significantly changed
 +
***fold change: 1.89, pvalue:0.016 significantly changed
 
**VC0647
 
**VC0647
−
***fold change: -1.11, pvalue:0.0003
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***fold change: -1.11, pvalue:0.0003 significantly changed
 +
***fold change: -0.94, pvalue:0.0125 significantly changed
 +
***fold change: -1.05, pvalue:0.0051 significantly changed
 
**VC0468
 
**VC0468
−
***fold change: -0.17, pvalue:0.3350
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***fold change: -0.17, pvalue:0.3350 not significantly changed
 
**VC2350
 
**VC2350
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***fold change: -2.40, pvalue:0.0130
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***fold change: -2.40, pvalue:0.0130 significantly changed
 
**VCA0583
 
**VCA0583
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***fold change: 1.06, pvalue:0.1011
+
***fold change: 1.06, pvalue:0.1011 significantly changed
  
 +
===Part 2 Procedure===
  
 +
Procedure from [http://www.openwetware.org/wiki/BIOL367/F10:GenMAPP_and_MAPPFinder_Protocols here].
  
 +
I used the <code>Vc-Std_External_20101022.gdb XMLPipeDB</code> which was downloaded from the SourceForge Download page.
  
−
===Part 2 Procedure===
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After launching the GenMAPP Program, I checked to make sure the correct Gene Database was loaded.
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Each time you launch GenMAPP, you need to make sure that the correct Gene Database (.gdb) is loaded.
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−
Look in the lower left-hand corner of the window to see which Gene Database has been selected.
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I selected the Data menu from the main Drafting Board window and chose Expression Dataset Manager from the drop-down list.
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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.
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−
For the exercise today, you will need to download the appropriate Vibrio cholerae Gene Database.
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From the Expression Datasets menu, I selected the tab-delimited text file that I formatted for GenMAPP (.txt).
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Half of the class will use the Vc-Std_External_20090622.gdb Gene Database that was initially created by the Fall 2008 Biological Databases class.
+
 
−
To download this Gene Database, follow this link to the XMLPipeDB SourceForge Download page.
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The Expression Dataset Manager converted the data and generated a .EX. 121 errors recorded.
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Half of the class will use a new Vc-Std_External_20101022.gdb Gene Database that was created by Drs. Dahlquist and Dionisio a year later.
+
 
−
To download this Gene Database, follow this link to the XMLPipeDB SourceForge Download page.
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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."
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The members of a pair should each choose a different gene database.
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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.
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#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?
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GenMAPP Expression Dataset Manager Procedure
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#*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.
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Launch the GenMAPP Program. Check to make sure the correct Gene Database is loaded.
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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.
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Under Dondi's guidance I made two color sets.
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Remember, you and your partner are going to use different versions of the Vibrio cholerae Gene Database for this exercise.
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I selected the "Avg_LogFC_all" to be used as the Gene Value from the drop down list.
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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.
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Using the formulas <code> [Avg_LogFC_All] > 0.25 AND [Pvalue] < 0.05 </code> and <code>[Avg_LogFC_All] < -0.25 AND [Pvalue] < 0.05</code>, 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.
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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.
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You may need to download your .txt file from the wiki onto your Desktop if you have not already done so.
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MAPPFinder Procedure:
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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.
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The Vibrio data we have been working with does not have any text (character) data in it.
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I launched the MAPPFinder program, clicked on "Calculate New Results".
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Allow the Expression Dataset Manager to convert your data.
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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.
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I clicked on "Find File" and chose the .gex file that was generated.
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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.
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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.
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I chose "decreased" as my criteria of choice.
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It is likely that you will have a different number of errors than your partner who is using a different version of the Vibrio cholerae Gene Database. Which of you has more errors? Why do you think that is? Record your answers in your journal page.
+
I then checked "Gene Ontology" and "pvalue"
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Upload your exceptions file: EX.txt to your wiki page.
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I then ran MappFinder.
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Customize the new Expression Dataset by creating new Color Sets which contain the instructions to GenMAPP for displaying data on MAPPs.
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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.
+
I clicked on show ranked list and got the following results.
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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.
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List the top 10 Gene Ontology terms in your individual journal entry. (decreased, 2010)
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Activate the Criteria Builder by clicking the New button.
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*glucose catabolic process
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Enter a name for the criterion in the Label in Legend field.
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*hexose catabolic process
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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.
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*glycolysis
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State the criterion for color-coding a gene in the Criterion field.
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*monosaccharide catabolic process
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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
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*cytoplasm
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[ColumnName] RelationalOperator Value
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*alcohol catabolic process
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with the column name always enclosed in brackets and character values enclosed in single quotes. For example:
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*cellular carbohydrate catabolic process
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[Fold Change] >= 2
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*glucose metabolic process
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[p value] < 0.05
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*protein folding
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[Quality] = 'high'
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*hexose metabolic process
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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.
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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?
−
The Ops list also contains the conjunctions AND and OR, which may be used to make compound criteria. For example:
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*Due to a miscommunication Mahrad did 2009 increase so for this one problem I compared to Lena's data. The terms are different.  Like before, I would say that either the exact strings associated with the gene ontologies have changed, or my database is more updated.
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[Fold Change] > 1.2 AND [p value] <= 0.05
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−
Parentheses control the order of evaluation. Anything in parentheses is evaluated first. Parentheses may be nested. For example:
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*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?
−
[Control Average] = 100 AND ([Exp1 Average] > 100 OR [Exp2 Average] > 100)
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**VC0028
−
Column names may be used anywhere a value can, for example:
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***lyase activity, dihydroxy-acid dehydratase activity, iron-sulfur cluster binding, 4-iron 4 sulfur cluster binding, branched chain family amino acid biosynthetic process, cellular amino acid biosynthetic process, metabolic process, metal ion binding, catalytic activity
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[Control Average] < [Experiment Average]
+
**VC0941
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After completing a new criterion, add the criterion entry (label, criterion, and color) to the Criteria List by clicking the Add button.
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***glycine hydroxymethyltransferase activity, transferase activity, L-serine metabolic process, one-carbon metabolic process, cytoplasm, pyridoxal phosphate binding, catalytic activity, glycine hydroxymethyltransferase activity,
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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.
+
**VC0869
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You may continue to add criteria to the Color Set by using the previous steps.
+
***nucleotide binding, ATP binding, phosphoribosylformylglycinamidine synthase activity, purine nucleotide biosynthetic process, 'de nova' IMP biosynthetic process, glutamine metabolic process, purine nucleotide biosynthetic process, 'de novo' IMP biosynthetic process, cytoplasm, nucleotide binding, ATP binding, catalytic activity, ligase activity, phosphoribosylformyglycinamidine synthase activity
−
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.
+
**VC0051
−
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.
+
***purine nucleotide biosynthetic process, 'de novo' IMP biosynthetic process, nucleotide binding, ATP binding, catalytic activity, lyase activity, carboxy-lyase activity, phosphoribosylaminoimidazole carboxylase activity
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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.
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**VC0647
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Upload your .gex file to your journal entry page for later retrieval.
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*** mRNA catabolic process, RNA processing, cytoplasm, mitochondion, RNA binding, 3'-5'-exoribonuclease activity, transferase activity, nucleotidyltransferase activity, polyribonucleotide nuclotidyltransferase activity
−
MAPPFinder Procedure
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**VC0468
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Note: You and your partner will both do the same criterion, either "Increased" or "Decreased", but your group does not need to do both "Increased" and "Decreased" Sign up for the criterion you want on the group list ( Fall 2010 or Fall 2013) so that we can make sure that as a class we are covering both criteria.
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*** glutathione biosynthetic process, metal ion binding, nucloetide binding, ATP binding, catalytic activity, ligase activity, glutathione synthase activity
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Launch the MAPPFinder program (or from within GenMAPP, select Tools > MAPPFinder).
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**VC2350
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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\.)
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***deoxyribonucleotide catabolic process, metabolic process, cytoplasm, catalytic activity, lyase activity, deoxyribose-phosphase aldolase activity
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Click on the button "Calculate New Results".
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**VCA0583
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Click on "Find File" and choose the your Expression Dataset file, for example, "MyDataset.gex", and click OK.
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***transport, outer membrane-bounded periplasmic space, transporter activity
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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.
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*They are different than my partner which suggests I used an updated gene database.
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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).
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Check the boxes next to "Gene Ontology" and "p value".
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After searching VC0028 I clicked on metal ion binding GO term which brought me to a page with genes represented as boxes. I then looked at Uniprot for the alternative name, which is ILVD_VIBCH. I then clicked on ILVD_VIBCH, in the MAPP and determined that it has increased expression.  It looks like this gene is "involved in step 3 of the subpathway that synthesizes L-isoleucine from 2-oxobutanoate" from UniProt.
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Click the "Browse" button and create a meaningful filename for your results.
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Click "Run MAPPFinder". The analysis will take several minutes. It may look like the computer is stalled; be patient, it will eventually start running.
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I then uploaded the mapp file that was generated from these steps.
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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.
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I made a copy of the criterion file and opened it up in excel, which put the data in MAPPFinder in tabular excel form.
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To see a list of the most significant Gene Ontology terms, click on the menu item "Show Ranked List".
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List the top 10 Gene Ontology terms in your individual journal entry.
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There are rows of information that give the background information on how MAPPFinder made the calculations. I compared this information with Lena's who used a different version of the Vibrio Gene Database.  
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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.
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*Which numbers are different? Why are they different?
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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?
+
**Numbers different: 565 probes meeting the filter linked to a UniProt ID., 330 genes meeting the criterion linked to a GO term., 5100 Probes linked to a UniProt ID., 2475 Genes linked to a GO term.,The z score is based on an N of 2475 and a R of 330 distinct genes in the GO.  These were the rows that were different. Based on this I would say that the database I am using has more genes and is the more updated version of the database.
−
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.
+
I filtered the list down to 17 genes using the increased and decreased criteria as follows.
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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.
+
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In Windows, make a copy of your results (XXX-CriterionX-GO.txt) file.
+
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"XXX" refers to the name you gave to your results file.
+
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"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).
+
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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
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You will use these two filters depending on the number of terms you have:
+
I used these filters too:
 
Number Changed (in column I) greater than or equal to 4 or 5 AND less than 100
 
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%
 
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.
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I saved my changes in an excel spreadsheet as an Excel workbook .xls.
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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.
+
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? I highlighted this .xls document with GO terms that are closely related to one another with the same color.
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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.
+
 
 +
*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.
  
 +
*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?
 +
**After looking up that catabolic processes break down smaller molecules on google, I was able to assume some information about my gene ontologies.  From what I know I can assume that the hexose catabolic process, glucose catabolic process, monosaccharide catabolic process, alcohol catabolic process, and cellular carbohydrate catabolic process are all processes that breakdown their individual molecules.  For example, I can hypothesize that the glucose catabolic process breaks down glucose.  From this I can assume some helpful functions from the bacteria's perspective.  A Vibrio cholerae bacterium could potentially breakdown the glucose in a human's body to use it as energy, or other anabolic reactions.  Furthermore it could potentially do the same thing for monosaccharide catabolic processes and hexose catabolic processes.  A bacterium could use the alcohol catabolic process to counteract a potentially poisonous dose of ethanol, which would mean death for the bacterium.
  
 
Conclusion
 
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.
 
Write a paragraph that briefly summarizes and gives a scientific conclusion for the work that you did for part 1 and 2 this week.
 +
*As we took the raw data from Merrell et al. I had no idea what we would be doing.  Merrell's conclusions seemed almost unreachable as I started part 1.  By the end of part 2, as a class, we succeeded in checking Merrell et al.'s conclusions and ended up learning a great deal from it all.  Merrell et al. compared in vitro samples to real stool samples in hopes that they would be able to identify pathogenic genes in Vibrio cholerae and they needed statistical analysis that was on point to make this distinction.  From what I can tell, the statistical analysis we executed was a valid way to verify the integrity of their experiment.  After prepping the data for GenMAPP we used GenMAPP's tools to easily see which genes had significant expression, and what each gene did specifically.  Our comparison to Merrell's conclusions was a useful exercise for both our class and Merrell et al. because it gave even more integrity to our statistical analysis of the raw data.
  
  

Latest revision as of 06:33, 27 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). Why is this imporant? The dollar signs make sure that the proper cells are always used, the values are always taken from B2 and B3 instead of incrementing. 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, significantly changed, the pvalue is less than .05 and the fold change is greater than .25.
      • fold change: 1.27, pvalue: 0.0692, not significantly changed, the pvalue is greater than .05
    • VC0941
      • fold change: 0.09, pvalue:0.6759, not significantly changed, the pvalue is greater than .05
      • fold change: -0.28, pvalue:0.1636, not significantly changed
    • VC0869
      • fold change: 1.59, pvalue:0.0463, significantly changed
      • fold change: 2.12, pvalue: 0.02 significantly changed
      • fold change: 1.50 , pvalue:0.0174 significantly changed
      • fold change: 2.20, pvalue: 0.002 significantly changed
      • fold change: 1.95, pvalue:0.0227 significantly changed
    • VC0051
      • fold change: 1.92, pvalue:0.0139 significantly changed
      • fold change: 1.89, pvalue:0.016 significantly changed
    • VC0647
      • fold change: -1.11, pvalue:0.0003 significantly changed
      • fold change: -0.94, pvalue:0.0125 significantly changed
      • fold change: -1.05, pvalue:0.0051 significantly changed
    • VC0468
      • fold change: -0.17, pvalue:0.3350 not significantly changed
    • VC2350
      • fold change: -2.40, pvalue:0.0130 significantly changed
    • VCA0583
      • fold change: 1.06, pvalue:0.1011 significantly changed

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?

  • Due to a miscommunication Mahrad did 2009 increase so for this one problem I compared to Lena's data. The terms are different. Like before, I would say that either the exact strings associated with the gene ontologies have changed, or my database is more updated.
  • 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?
    • VC0028
      • lyase activity, dihydroxy-acid dehydratase activity, iron-sulfur cluster binding, 4-iron 4 sulfur cluster binding, branched chain family amino acid biosynthetic process, cellular amino acid biosynthetic process, metabolic process, metal ion binding, catalytic activity
    • VC0941
      • glycine hydroxymethyltransferase activity, transferase activity, L-serine metabolic process, one-carbon metabolic process, cytoplasm, pyridoxal phosphate binding, catalytic activity, glycine hydroxymethyltransferase activity,
    • VC0869
      • nucleotide binding, ATP binding, phosphoribosylformylglycinamidine synthase activity, purine nucleotide biosynthetic process, 'de nova' IMP biosynthetic process, glutamine metabolic process, purine nucleotide biosynthetic process, 'de novo' IMP biosynthetic process, cytoplasm, nucleotide binding, ATP binding, catalytic activity, ligase activity, phosphoribosylformyglycinamidine synthase activity
    • VC0051
      • purine nucleotide biosynthetic process, 'de novo' IMP biosynthetic process, nucleotide binding, ATP binding, catalytic activity, lyase activity, carboxy-lyase activity, phosphoribosylaminoimidazole carboxylase activity
    • VC0647
      • mRNA catabolic process, RNA processing, cytoplasm, mitochondion, RNA binding, 3'-5'-exoribonuclease activity, transferase activity, nucleotidyltransferase activity, polyribonucleotide nuclotidyltransferase activity
    • VC0468
      • glutathione biosynthetic process, metal ion binding, nucloetide binding, ATP binding, catalytic activity, ligase activity, glutathione synthase activity
    • VC2350
      • deoxyribonucleotide catabolic process, metabolic process, cytoplasm, catalytic activity, lyase activity, deoxyribose-phosphase aldolase activity
    • VCA0583
      • transport, outer membrane-bounded periplasmic space, transporter activity
  • They are different than my partner which suggests I used an updated gene database.

After searching VC0028 I clicked on metal ion binding GO term which brought me to a page with genes represented as boxes. I then looked at Uniprot for the alternative name, which is ILVD_VIBCH. I then clicked on ILVD_VIBCH, in the MAPP and determined that it has increased expression. It looks like this gene is "involved in step 3 of the subpathway that synthesizes L-isoleucine from 2-oxobutanoate" from UniProt.

I then uploaded the mapp file that was generated from these steps. I made a copy of the criterion file and opened it up in excel, which put the data in MAPPFinder in tabular excel form.

There are rows of information that give the background information on how MAPPFinder made the calculations. I compared this information with Lena's who used a different version of the Vibrio Gene Database.

  • Which numbers are different? Why are they different?
    • Numbers different: 565 probes meeting the filter linked to a UniProt ID., 330 genes meeting the criterion linked to a GO term., 5100 Probes linked to a UniProt ID., 2475 Genes linked to a GO term.,The z score is based on an N of 2475 and a R of 330 distinct genes in the GO. These were the rows that were different. Based on this I would say that the database I am using has more genes and is the more updated version of the database.

I filtered the list down to 17 genes using the increased and decreased criteria as follows. Z Score (in column N) greater than 2 PermuteP (in column O) less than 0.05 I used these filters too: 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% I saved my changes in an excel spreadsheet as an Excel workbook .xls.

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? I highlighted this .xls document with GO terms that are closely related to one another with the same color.

  • 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.
  • 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?
    • After looking up that catabolic processes break down smaller molecules on google, I was able to assume some information about my gene ontologies. From what I know I can assume that the hexose catabolic process, glucose catabolic process, monosaccharide catabolic process, alcohol catabolic process, and cellular carbohydrate catabolic process are all processes that breakdown their individual molecules. For example, I can hypothesize that the glucose catabolic process breaks down glucose. From this I can assume some helpful functions from the bacteria's perspective. A Vibrio cholerae bacterium could potentially breakdown the glucose in a human's body to use it as energy, or other anabolic reactions. Furthermore it could potentially do the same thing for monosaccharide catabolic processes and hexose catabolic processes. A bacterium could use the alcohol catabolic process to counteract a potentially poisonous dose of ethanol, which would mean death for the bacterium.

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.

  • As we took the raw data from Merrell et al. I had no idea what we would be doing. Merrell's conclusions seemed almost unreachable as I started part 1. By the end of part 2, as a class, we succeeded in checking Merrell et al.'s conclusions and ended up learning a great deal from it all. Merrell et al. compared in vitro samples to real stool samples in hopes that they would be able to identify pathogenic genes in Vibrio cholerae and they needed statistical analysis that was on point to make this distinction. From what I can tell, the statistical analysis we executed was a valid way to verify the integrity of their experiment. After prepping the data for GenMAPP we used GenMAPP's tools to easily see which genes had significant expression, and what each gene did specifically. Our comparison to Merrell's conclusions was a useful exercise for both our class and Merrell et al. because it gave even more integrity to our statistical analysis of the raw data.


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


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