Kara M Dismuke Week 14 Journal

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Week 14 Electronic Lab Notebook

Initial Run [due 4/23/15]

  • Initially, I attempted to run the script several times, but each time was unsuccessful. After consulting with Dr. Dahlquist, I realized I needed to have put my Excel file into the MATH388GRNMap folder in order for it to run.
  • After 1st successful MATLAB run of Excel sheet, several graphs were generated have been stored in this Run 1 ZIP file (Incorrect).
    • For the sake of privacy, the Excel sheet used has been stored on LionShare.
  • However, after discussing with Kristen, we found some errors that occurred (we still aren't sure why), but so, I had to re-run it. The graphs generated in this run can be found in a zipped file linked in the Results section.

Procedure: Introduction to GRNmap and Gene Regulatory Network Modeling

For this assignment, we ranthe GRNmap model in MATLAB using the Excel file created for the Week 13 Assignment.

  • Note: We compared the estimated weight and production rate parameters outputted by these following two optimization runs:
    1. once where the threshold parameters, b, are not estimated
    2. once where the threshold parameters are estimated

Steps

  1. In the optimization_parameters sheet of my input workbook, i set fix_b to 1, fix_P to 0, and iestimate to 1. And then after I set alpha to 0.01, I ran the GRNmodel.
  2. Saved graphs generated as jpgs (put into a ZIP file) and pasted them into PPT presentation
    • Made sure:
      • labels were clear
      • placed appropriate number of graphs on each slide
      • ensured graphs are same size and aspect ratio hasn't been changed
  3. Created new worksheet in estimation output xlsx workbook and called in estimated_weights
    • In this sheet, created a column of the form ControllerGeneA -> TargetGeneB, and replaced these generic names with the standard gene names for each regulatory pair in your network.
      • Note: columns represent Controllers and rows represent Targets in your network and network_weights sheets
  4. Extracted non-zero optimized weights from their worksheet and put them in a single column next to the corresponding ControllerGeneA -> TargetGeneB label
  5. Now saved this workbook as a new file with a meaningful name (e.g. append "estimate-b" to the previous filename), and changed fix_b to 0 in the "optimization_parameters" worksheet. This is done to ensure that the thresholds will be estimated. Then, I reran the GRNmodel with the new input sheet.
  6. Repeated Steps (2) through (4) with the new output
  7. Created an empty excel workbook, and copied both sets of weights into a worksheet
  8. Created a bar chart so that we can compare the "fixed b" and "estimated b" weights
  9. Repeated steps (7) and (8) with the production rates
  10. Copied the two bar charts into my PPT presentation
  11. Used GRNsight to visualize the output of each of my models (Note: attempts failed...I discuss this later)
  12. Arranged genes in the same order I used to display them in your Week 12 assignment for both of my model output runs
  13. Took a screenshot of each of the results and pasted it into my PPT presentation, ensuring that I clearly labeled which screenshot belongs to which run
  14. Used GRNsight to create a visualization of our various model runs' output, making sure to arrange the genes in the same order as they were arranged in, for my Week 12 assignment
    • Took screenshots of the results and pasted them into my PPT presentation, ensuring that I clearly labeled which screenshot belongs to which run
    • Noted that GRNsight displays results differently after weights have been estimated
    • Sign of Weight
      • If weight is positive, the edge will be a regular (pointy) arrowhead to indicate activation
      • If weight is negative, the edge will be a blunt arrowhead (line segment perpendicular to the edge direction) to indicate repression
    • Thickness of the edge
      • determined by absolute value of the weight
      • more specifically, GRNsight divides all weight values by the absolute value of the matrix's maximum weight to normalize the values (between 0 and 1)
      • then, GRNsight ican change the thicknesses based on this scale between 0 and 1 that's been created
        • Minimum thickness: weights near 0 after normalization (larger magnitude)
        • Maximum thickness: weights near 1 after normalization (smaller magnitude)
    • Color of the edge
      • Magenta: edges with positive normalized weight values from 0.05 to 1
      • Cyan: edges with negative normalized weight values from -0.05 to
        • These are near 0, and thus, have a weak influence on the target gene
  15. Uploaded my PPT, two input workbooks, and two output workbooks to OpenWetWare and linked to them in my individual journal
  16. Interpreted my results.
    • Examined the various graphs, paying special attention to which genes in the model have the closest fit between the model data and actual data
      • Suggested reasons that may account for this
      • Tried to draw a connection for how this helps me interpret the microarray data
    • Looked for the gene(s) that showed the largest dynamics over the time course
    • Looked for the gene(s) that showed the biggest difference in dynamics between wild type and dzap1
      • Attempted to connect this to my results from the GRNsight visualization
    • Examined bar charts that compared the weights and production rates between the two runs.
      • Noted any major differences between the two runs, and tried to offer an explanation for why this was
      • Attempted to connect this to my results from the GRNsight visualization

Results

Incorrect Results from 4/27 Run

Results from (after corrections were made)

FIXED B (b was set to 0)

  • ZIP file of figures generated from MATLAB run
  • Input Workbook
  • Output Workbook
    • NOTE (4/29/15): I realize there is a big error in terms of this output file; however, I think it can be explained by a difference in MATLAB...I ran it using MATLAB_R2013_A rather than MATLAB_R2013_B. I hope to address this issue tomorrow in class and thus have to stop here for the time being. Once addressing the issue, I can move to create the Excel file with the weights, create the subsequent bar graphs, and then run the Excel file through GRNsight to get a visualization of the network.
    • Weights Workbook (fixedb)

ESTIMATED B (b was set to 1)

  • ZIP file of figures generated from MATLAB run
  • File with bar graphs comparing weights and production rates
  • Input Workbook
  • Output Workbook
    • NOTE (4/29/15): I realize there is a big error in terms of this output file; however, I think it can be explained by a difference in MATLAB...I ran it using MATLAB_R2013_A rather than MATLAB_R2013_B. I hope to address this issue tomorrow in class and thus have to stop here for the time being. Once addressing the issue, I can move to create the Excel file with the weights, create the subsequent bar graphs, and then run the Excel file through GRNsight to get a visualization of the network.
    • Weights Workbook (estimateb)

Note that in both output workbooks, there is data for NDT80. I had changed the input workbooks so they are accurate (have ASG1 instead of NDT80), but when I ran them in the library, I did not get a correct Excel file (MATLAB generated error messages). Thus, we will have to use Kristen's for ASG1.

OTHER DOCUMENTS

Interpretations of Results

Examine the graphs that were output by each of the runs.

  • Which genes in the model have the closest fit between the model data and actual data? Why do you think that is? How does this help you to interpret the microarray data?
    • CYC8 and SWI5 seem to both have the closest respective fits between model data and actual data. This would seem to indicate that the experimental errors done upon data collection were at a minimum and the model fits this data quite well. In terms of interpreting the microarray data, I feel as though I could have more confidence in the CYC8 and SWI5 data as it relates to the model.
  • Which genes showed the largest dynamics over the timecourse? Which genes showed differences in dynamics between the wild type and the other strain your group is using? Given the connections in your network (see the visualization in GRNsight), does this make sense? Why or why not?
    • PDR1, RIF1, YLR278C seem to me to show the largest dynamics over the time course.
    • The main gene that showed a good deal of difference in dynamics between the wild type and dzap1 when it comes to the model would be the ACE2 gene. This was the only graph where the wt and dzap models did not match each other.
  • Examine the bar charts comparing the weights and production rates between the two runs. Were there any major differences between the two runs? Why do you think that was? Given the connections in your network (see the visualization in GRNsight), does this make sense? Why or why not?
    • For the relationships involving MIG1, there is a huge discrepancy between the two runs' weight values. This seems to suggest to me there may be some sort of error in the generation of the graphs (because even upon glancing at them, there is clear that something funny is going on).
    • As far as production rates are concerned, there were noticeable discrepancies among the YOX1, YHP1, MSN2, and CIN5 genes.

NOTE (4/27/15): I was unable to get the Excel files to upload in GRNsight in such a way as to generate networks that help display the varying weight values. In reading the directions, I was unsure how to manipulate the output Excel files so as to enable GRNsight to read them correctly. I plan on discussing this with Dr. Fitzpatrick and/or Dr. Dahlquist, and once the issue is solved, then I can add the correct network graphs into my PPT and add more to my answers for questions 2 and 3.
NOTE (4/29/15): I cannot yet answer these questions because I am unable to run the file through GRNsight (for reasons that I have elaborated more on above).
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