Difference between revisions of "Troque Week 8"

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(→‎Part 2: Added the rest of the instructions for Part 2)
m (→‎Things to note: Added last bullet point)
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* For Part 2, I worked on the 2009 data while Erich worked on the 2010 data.
 
* For Part 2, I worked on the 2009 data while Erich worked on the 2010 data.
 
* On Thursday (October 22), we were assigned to analyze decreased expressions of our data using GenMAPP.
 
* On Thursday (October 22), we were assigned to analyze decreased expressions of our data using GenMAPP.
 +
* I met with Erich on Monday (October 26) to work on this assignment.
  
 
== Part 1 ==
 
== Part 1 ==

Revision as of 02:14, 27 October 2015

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Sources

  • The methods described in Part 1 of this page are taken from this openwetware page.
  • The methods for Part 2 have been adapted from this page.

Files created

  • The Excel file created on Thursday (October 15, 2015) can be downloaded here.
  • A more updated Excel file with the B-H p-value correction can be downloaded here.
  • The tab delimited txt file can be seen here

Things to note

  • Always save your work when you have a chance.
  • For this assignment, my partner was Erich Yanoschik.
  • For Part 2, I worked on the 2009 data while Erich worked on the 2010 data.
  • On Thursday (October 22), we were assigned to analyze decreased expressions of our data using GenMAPP.
  • I met with Erich on Monday (October 26) to work on this assignment.

Part 1

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

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

  • Inserted a new Worksheet into my Excel file, and named it "scaled_centered".
  • Selected all and copied and pasted everything from the "compiled_raw_data" worksheet into this new "scaled_centered".
  • 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 "StDev".
  • I then computed the Average log ratio for each chip (each column of data). In cell B2, I typed the following equation:
=AVERAGE(B4:B5224)

(Note: We tried to do a keyboard shortcut using CTRL + Shift + Down buttons, but row 363 has a missing data so we had to manually type in "B5224" for the end of the all the data.)

and pressed "Enter". Excel then computed the average value of the cells specified in the range given inside the parentheses. Another approach for selecting all of the cells we needed was, instead of typing the cell designations, we could have clicked on the beginning cell, scrolled down to the bottom of the worksheet, and shift-clicked on the ending cell.
  • I then computed the Standard Deviation of the log ratios on each chip (each column of data). 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. Excel automatically changed the equation to match the cell designations for those columns.
  • Now that the average and standard deviations of the log ratios have been computed for each chip, it's time for the scaling and centering based on these values.
  • I copied the column headings for all of your 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 colums of cells. I 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
In this case, I wanted the data in cell B4 to have the average subtracted from it (cell B2) and be divided by the standard deviation (cell B3). I used the dollar sign symbols in front of the "2" and "3" to tell Excel to always reference that row in the equation, even though I will paste it for the entire column of 5221 genes. This was important since we only want the first value (i.e. B4 into B5, etc. instead of B2 -> B3 or B3 -> B4)to change when we drag the equation down to the other cells in the column. Since B2 and B3 contained the average and standard deviations for the replicates and the values below them are the actual data for the replicates, we would want to fix the equation so that we are subtracting those fixed cells from the cells below them.
  • I copied and pasted this equation into the entire column. One easy way to do this is to click on the original cell with the equation and position the cursor at the bottom right corner. The cursor then change into a thin black plus sign (not a chubby white one). Double clicking this when it does will make the formula be magically copied to the entire column of genes.
  • I then copied and pastes the scaling and centering equation for each of the columns of data with the "_scaled_centered" column header.

Perform statistical analysis on the ratios

This step uses the scaled and centered data produced in the previous step. The following operations are what I executed:

  • I inserted a new worksheet and name it "statistics" and copied the first column ("ID") from the "scaled_centered" worksheet into this new worksheet.
  • I pasted the data into the first column of the new "statistics" worksheet.
  • I went back to the "scaled_centered" worksheet and copied the columns that are designated "_scaled_centered".
  • I then went to my new worksheet and clicked on the B1 cell and selected "Paste Special" from the Edit menu. A window opened; I clicked on the radio button for "Values" and clicked OK. This pasted the numerical result into my new worksheet instead of the equation which must make calculations on the fly.
  • I then deleted Rows 2 and 3 where it says "Average" and "StDev" so that the data rows with gene IDs are immediately below the header row 1.
  • Next, I went to a new column on the right of my worksheet and typed the header "Avg_LogFC_A", "Avg_LogFC_B", and "Avg_LogFC_C" into the top cell of the next three columns.
  • Excel compute the average log fold change for the replicates for each patient when I typed the equation:
=AVERAGE(B2:E2)
into cell N2. I copied this equation and pasted it into the rest of the column.
  • I created the equation for patients B and C as well and pasted it into their respective columns.
  • I then needed to compute the average of the averages. I typed the header "Avg_LogFC_all" into the first cell in the next empty column and created the equation that will compute the average of the three previous averages I calculated and pasted it into this entire column.
  • I inserted a new column next to the "Avg_LogFC_all" column that I computed in the previous step and labeled the column "Tstat". This will compute a T statistic that tells whether the scaled and centered average log ratio is significantly different than 0 (no change). Then I entered the equation: 
=AVERAGE(N2:P2)/(STDEV(N2:P2)/SQRT(number of replicates))
(NOTE: in this case the number of replicates is 3.) Next, I copied the equation and pasted it into all rows in that 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 our case there are 2 degrees of freedom. I copied the equation and pasted it into all rows in that column.

Calculate the Bonferroni p value Correction

  • Before doing the following, I selected all of the first row and clicked on "Sort & Filter" -> "Filter" for the sanity check portion of the assignment. On the dropdown button for the Pvalue header, I went to "Number Filters", then selected "Less Than" and entered "0.05" for the text box next to "is less than". In the bottom left corner of Excel, I got 948 results.
  • Then, I performed adjustments to the p value to correct for the multiple testing problem. I went ahead and labeled the next two columns to the right with the same label, Bonferroni_Pvalue.
  • The equation for this is =(Pvalue)*5221, (in this case, the Pvalue = cell S2) Upon completion of this single computation, I used the trick to copy the formula throughout the column.
  • Then I replaced any corrected p value that is greater than 1 by the number 1 by typing the following formula into the first cell below the second Bonferroni_Pvalue header: =IF(T2>1,1,T2). I also used the trick to copy the formula throughout this column.

Checkpoint: The Excel file created from doing the procedures above can be located here.

Calculate the Benjamini & Hochberg p value Correction

  • For this part, I inserted yet another worksheet and named it "B-H_Pvalue".
  • I copied and pasted the "ID" column from my previous worksheet into the first column of this new worksheet.
  • I inserted a new column on the very left and named it "MasterIndex". I needed to create a numerical index of genes so that I can always sort them back into the same order.
    • This is done by typing a "1" in cell A2 and a "2" in cell A3 and performing the trick for doing it for all the remaining columns:
    • I selected both cells with "1" and "2" and hovered my mouse over the bottom-right corner of the selection until it makes a thin black + sign. Double-clicking on the + sign would then fill the entire column with a series of numbers from 1 to 5221 (the number of genes on the microarray).
  • For the following, I used Paste special > Paste values so that the values (instead of references to the other columns) are pasted. I copied the unadjusted p values from my previous worksheet and pasted it into Column C.
  • I then selected all of columns A, B, and C, sorted by ascending values on Column C, and finally clicked the sort button from A to Z on the toolbar, in the window that appears, sort by column C, smallest to largest.
  • Next, I typed the header "Rank" in cell D1. This is for creating a series of numbers in ascending order from 1 to 5221 in this column. This is the p value rank, smallest to largest. Same with the "MasterIndex"I typed "1" into cell D2 and "2" into cell D3, selected both cells D2 and D3, and double-clicked on the plus sign on the lower right-hand corner of my selection to fill the column with a series of numbers from 1 to 5221.
  • Now I could calculate the Benjamini and Hochberg p value correction. I typed B-H_Pvalue in cell E1. I also entered the following formula in cell E2: =(C2*5221)/D2, which I then copied to the entire column.
  • I also typed "B-H_Pvalue" into cell F1.
  • With this, I typed the following formula into cell F2: =IF(E2>1,1,E2) and pressed enter. I copied that equation to the entire column.
  • Select columns A through F. Now sort them by your MasterIndex in Column A in ascending order.
  • Copy column F and use Paste special > Paste values to paste it into the next column on the right of your "statistics" sheet.

Prepare file for GenMAPP

  • For the actual worksheet to feed into the GenMAPP program, I inserted a new worksheet and named it "forGenMAPP".
  • I then went back to the "statistics" worksheet and chose Select All and Copy.
  • In the new sheet, I clicked on cell A1 and selected Paste Special, clicked on the Values radio button, and clicked OK. The following steps are to now format this worksheet for import into GenMAPP.
  • I selected Columns B through Q (all the fold changes), and selected the menu item Format > Cells. Under the number tab, I selected 2 decimal places and clicked OK.
  • Next, I selected all the columns containing the p values. I selected the menu item Format > Cells, and ender the number tab, selected 4 decimal places.
  • Since they are no longer needed, I deleteed the left-most Bonferroni p value column, preserving the one that shows the result of my "if" statement.
  • I then inserted a column to the right of the "ID" column. I named the header at the top cell of this column "SystemCode"and filled the entire column (each cell) with the letter "N" using the trick to copy values to the rest of the column.
  • Then, I selected the menu item File > Save As, and chose "Text (Tab-delimited) (*.txt)" from the file type drop-down menu. (This will be the file type that I fed into GenMAPP). Excel made me click through a couple of warnings because it doesn't like the user going all independent and choosing a different file type than the native .xls so I just clicked OK in all of them. The new *.txt file is now ready for import into GenMAPP.
    • I uploaded both the .xls and .txt files (seen at the checkpoint below) that I have just created into my journal page in the class wiki and added my initials to differentiate it from the other students' files so that they don't overwrite it.

Checkpoint: The files created can be found here (Excel) and here (txt).

Part 2

I will be working on the 2009 data.

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.
  • 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.

Checkpoint: There were 772 errors in the 2009 data file. The gex file is located here. The EX.txt file can be found here.

Files created:

GenMAPP Expression Dataset Manager Procedure

  • Launch the GenMAPP Program. Check to make sure the correct Gene Database is loaded.
    • Look in the lower, left-hand corner of the main GenMAPP Drafting Board window to see the name of the Gene Database that is loaded. If this is not the correct Gene Database or it says "No Gene Database", then go to the Data > Choose Gene Database menu item to select the Gene Database you need to perform the analysis.
    • Remember, you and your partner are going to use different versions of the Vibrio cholerae Gene Database for this exercise.
  • Select the Data menu from the main Drafting Board window and choose Expression Dataset Manager from the drop-down list. The Expression Dataset Manager window will open.
  • Select New Dataset from the Expression Datasets menu. Select the tab-delimited text file that you formatted for GenMAPP (.txt) in the procedure above from the file dialog box that appears.
    • You may need to download your .txt file from the wiki onto your Desktop if you have not already done so.
  • The Data Type Specification window will appear. GenMAPP is expecting that you are providing numerical data. If any of your columns has text (character) data, you would check the box next to the field (column) name.
    • The Vibrio data we have been working with does not have any text (character) data in it.
  • Allow the Expression Dataset Manager to convert your data.
    • This may take a few minutes depending on the size of the dataset and the computer’s memory and processor speed. When the process is complete, the converted dataset will be active in the Expression Dataset Manager window and the file will be saved in the same folder the raw data file was in, named the same except with a .gex extension; for example, MyExperiment.gex.
    • A message may appear saying that the Expression Dataset Manager could not convert one or more lines of data. Lines that generate an error during the conversion of a raw data file are not added to the Expression Dataset. Instead, an exception file is created. The exception file is given the same name as your raw data file with .EX before the extension (e.g., MyExperiment.EX.txt). The exception file will contain all of your raw data, with the addition of a column named ~Error~. This column contains either error messages or, if the program finds no errors, a single space character.
      • Record the number of errors. For your journal assignment, open the .EX.txt file and use the Data > Filter > Autofilter function to determine what the errors were for the rows that were not converted. Record this information in your individual journal page.
      • It is likely that you will have a different number of errors than your partner who is using a different version of the Vibrio cholerae Gene Database. Which of you has more errors? Why do you think that is? Record your answers in your journal page.
      • Upload your exceptions file: EX.txt to your wiki page.
  • Customize the new Expression Dataset by creating new Color Sets which contain the instructions to GenMAPP for displaying data on MAPPs.
    • Color Sets contain the instructions to GenMAPP for displaying data from an Expression Dataset on MAPPs. Create a Color Set by filling in the following different fields in the Color Set area of the Expression Dataset Manager: a name for the Color Set, the gene value, and the criteria that determine how a gene object is colored on the MAPP. Enter a name in the Color Set Name field that is 20 characters or fewer.
    • The Gene Value is the data displayed next to the gene box on a MAPP. Select the column of data to be used as the Gene Value from the drop down list or select [none]. We will use "Avg_LogFC_all" for the Vibrio dataset you just created.
    • Activate the Criteria Builder by clicking the New button.
    • Enter a name for the criterion in the Label in Legend field.
    • Choose a color for the criterion by left-clicking on the Color box. Choose a color from the Color window that appears and click OK.
    • State the criterion for color-coding a gene in the Criterion field.
      • A criterion is stated with relationships such as "this column greater than this value" or "that column less than or equal to that value". Individual relationships can be combined using as many ANDs and ORs as needed. A typical relationship is
[ColumnName] RelationalOperator Value
with the column name always enclosed in brackets and character values enclosed in single quotes. For example:
[Fold Change] >= 2
[p value] < 0.05
[Quality] = 'high'
This is the equivalent to queries that you performed on the command line when working with the PostgreSQL movie database. GenMAPP is using a graphical user interface (GUI) to help the user format the queries correctly. The easiest and safest way to create criteria is by choosing items from the Columns and Ops (operators) lists shown in the Criteria Builder. The Columns list contains all of the column headings from your Expression Dataset. To choose a column from the list, click on the column heading. It will appear at the location of the cursor in the Criterion box. The Criteria Builder surrounds the column names with brackets.
The Ops (operators) list contains the relational operators that may be used in the criteria: equals ( = ) greater than ( > ), less than ( < ), greater than or equal to ( >= ), less than or equal to ( <= ), is not equal to ( <> ). To choose an operator from the list, click on the symbol. It will appear at the location of the insertion bar (cursor) in the Criterion box. The Criteria Builder automatically surrounds the operators with spaces.
The Ops list also contains the conjunctions AND and OR, which may be used to make compound criteria. For example:
[Fold Change] > 1.2 AND [p value] <= 0.05
Parentheses control the order of evaluation. Anything in parentheses is evaluated first. Parentheses may be nested. For example:
[Control Average] = 100 AND ([Exp1 Average] > 100 OR [Exp2 Average] > 100)
Column names may be used anywhere a value can, for example: 
[Control Average] < [Experiment Average]
  • After completing a new criterion, add the criterion entry (label, criterion, and color) to the Criteria List by clicking the Add button.
    • For the Vibrio dataset, you will create two criterion. "Increased" will be [Avg_LogFC_all] > 0.25 AND [Pvalue] < 0.05 and "Decreased will be [Avg_LogFC_all] < -0.25 AND [Pvalue] < 0.05.
    • You may continue to add criteria to the Color Set by using the previous steps.
      • The buttons to the right of the list represent actions that can be performed on individual criteria. To modify a criterion label, color, or the criterion itself, first select the criterion in the list by left-clicking on it, and then click the Edit button. This puts the selected criterion into the Criteria Builder to be modified. Click the Save button to save changes to the modified criterion; click the Add button to add it to the list as a separate criterion. To remove a criterion from the list, left-click on the criterion to select it, and then click on the Delete button. The order of Criteria in the list has significance to GenMAPP. When applying an Expression Dataset and Color Set to a MAPP, GenMAPP examines the expression data for a particular gene object and applies the color for the first criterion in the list that is true. Therefore, it is imperative that when criteria overlap the user put the most important or least inclusive criteria in the list first. To change the order of the criteria in the list, left-click on the criterion to select it and then click the Move Up or Move Down buttons. No criteria met and Not found are always the last two positions in the list.
  • Save the entire Expression Dataset by selecting Save from the Expression Dataset menu. Changes made to a Color Set are not saved until you do this.
  • Exit the Expression Dataset Manager to view the Color Sets on a MAPP. Choose Exit from the Expression Dataset menu or click the close box in the upper right hand corner of the window.
  • Upload your .gex file to your journal entry page for later retrieval.

MAPPFinder Procedure

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

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

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).

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


Assignment Links

Weekly Assignments

Individual Journal Entries

Shared Journal Entries

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