Difference between revisions of "Emmatyrnauer Week 10"

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#* Download your Excel workbook that you used for your [[Week 8]] assignment.
 
#* Download your Excel workbook that you used for your [[Week 8]] assignment.
 
#* Insert a new worksheet into your Excel workbook, and name it "wt_stem".
 
#* Insert a new worksheet into your Excel workbook, and name it "wt_stem".
−
#* Select all of the data from your "(STRAIN)_ANOVA" worksheet and Paste special > paste values into your "(STRAIN)_stem" worksheet.
+
#* Select all of the data from your "(wt_ANOVA" worksheet and Paste special > paste values into your "wt_stem" worksheet.
 
#** Your leftmost column should have the column header "Master_Index".  Rename this column to "SPOT".  Column B should be named "ID".  Rename this column to "Gene Symbol".  Delete the column named "Standard_Name".
 
#** Your leftmost column should have the column header "Master_Index".  Rename this column to "SPOT".  Column B should be named "ID".  Rename this column to "Gene Symbol".  Delete the column named "Standard_Name".
 
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).
 
#** Filter the data on the B-H corrected p value to be > 0.05 (that's '''greater than''' in this case).

Revision as of 06:29, 7 November 2017

Electronic Notebook

Microarray Data Analysis Part 2: "High-level Analysis"

We will be working on the protocols in class on Tuesday, October 31 and Thursday, November 2. Whatever you do not finish in class will be homework to be completed by the Week 10 journal deadline.

Background

This is a list of steps required to analyze DNA microarray data.

  1. Quantitate the fluorescence signal in each spot
  2. Calculate the ratio of red/green fluorescence
  3. Log2 transform the ratios
    • Steps 1-3 have been performed for you by the GenePix Pro software (which runs the microarray scanner).
  4. Normalize the ratios on each microarray slide
  5. Normalize the ratios for a set of slides in an experiment
  6. Perform statistical analysis on the ratios
  7. Compare individual genes with known data
    • Steps 6-7 are performed in Microsoft Excel
  8. Pattern finding algorithms (clustering)
  9. Map onto biological pathways
    • We will use software called STEM for the clustering and mapping
  10. Identifying regulatory transcription factors responsible for observed changes in gene expression
  11. Dynamical systems modeling of the gene regulatory network (GRNmap)
  12. Viewing modeling results in GRNsight

Clustering and GO Term Enrichment with stem

  1. Prepare your microarray data file for loading into STEM.
    • Download your Excel workbook that you used for your Week 8 assignment.
    • Insert a new worksheet into your Excel workbook, and name it "wt_stem".
    • Select all of the data from your "(wt_ANOVA" worksheet and Paste special > paste values into your "wt_stem" worksheet.
      • Your leftmost column should have the column header "Master_Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol". Delete the column named "Standard_Name".
      • Filter the data on the B-H corrected p value to be > 0.05 (that's greater than in this case).
        • Once the data has been filtered, select all of the rows (except for your header row) and delete the rows by right-clicking and choosing "Delete Row" from the context menu. Undo the filter. This ensures that we will cluster only the genes with a "significant" change in expression and not the noise. Record the number of genes left in your electronic notebook. 2006
      • Delete all of the data columns EXCEPT for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
      • Rename the data columns with just the time and units (for example, 15m, 30m, etc.).
      • Save your work. Then use Save As to save this spreadsheet as Text (Tab-delimited) (*.txt). Click OK to the warnings and close your file.
        • Note that you should turn on the file extensions if you have not already done so.
  2. Now download and extract the STEM software. Click here to go to the STEM web site.
    • Click on the download link, register, and download the stem.zip file to your Desktop.
    • Unzip the file. In Seaver 120, you can right click on the file icon and select the menu item 7-zip > Extract Here.
    • This will create a folder called stem. Inside the folder, double-click on the stem.jar to launch the STEM program.
  3. Running STEM
    1. In section 1 (Expression Data Info) of the the main STEM interface window, click on the Browse... button to navigate to and select your file.
      • Click on the radio button No normalization/add 0.
      • Check the box next to Spot IDs included in the data file.
    2. In section 2 (Gene Info) of the main STEM interface window, select Saccharomyces cerevisiae (SGD), from the drop-down menu for Gene Annotation Source. Select No cross references, from the Cross Reference Source drop-down menu. Select No Gene Locations from the Gene Location Source drop-down menu.
    3. In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
    4. In section 4 (Execute) click on the yellow Execute button to run STEM.
  4. Viewing and Saving STEM Results
    1. A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
      • Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
      • Take a screenshot of this window (on a PC, simultaneously press the Alt and PrintScreen buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
    2. Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
      • Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
      • At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents: wt_profile#_genelist.txt
        • Compress and upload these files to the wiki and link to them on your individual journal page.
      • For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents: "wt_profile#_GOlist.txt. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
        • Upload these files to the wiki and link to them on your individual journal page. (Note that it will be easier to zip all the files together and upload them as one file).
  5. Analyzing and Interpreting STEM Results
    1. Select one of the profiles you saved in the previous step for further intepretation of the data. I suggest that you choose one that has a pattern of up- or down-regulated genes at the cold shock timepoints. Each member of your group should choose a different profile. Answer the following:
      • Why did you select this profile? In other words, why was it interesting to you? I chose profile 45 because it shows a general trend of down regulation of genes at the cold shock time points.
      • How many genes belong to this profile? 580 genes belong to this profile.
      • How many genes were expected to belong to this profile? 52.7 genes were expected to belong to this profile.
      • What is the p value for the enrichment of genes in this profile? 0.00 is the p-value for the enrichment of genes in this profile. Bear in mind that we just finished computing p values to determine whether each individual gene had a significant change in gene expression at each time point. This p value determines whether the number of genes that show this particular expression profile across the time points is significantly more than expected.
      • Open the GO list file you saved for this profile in Excel. This list shows all of the Gene Ontology terms that are associated with genes that fit this profile. Select the third row and then choose from the menu Data > Filter > Autofilter. Filter on the "p-value" column to show only GO terms that have a p value of < 0.05. How many GO terms are associated with this profile at p < 0.05? 247 GO terms are associated with profile 45. The GO list also has a column called "Corrected p-value". This correction is needed because the software has performed thousands of significance tests. Filter on the "Corrected p-value" column to show only GO terms that have a corrected p value of < 0.05. How many GO terms are associated with this profile with a corrected p value < 0.05? 28 GO terms are associated with profile 45 with a corrected p value < 0.05
      • Select 6 Gene Ontology terms from your filtered list of corrected p < 0.05.
        • Each member of the group will be reporting on his or her own cluster in your presentation next week. You should take care to choose terms that are the most significant, but that are also not too redundant. For example, "RNA metabolism" and "RNA biosynthesis" are redundant with each other because they mean almost the same thing.
          • Note whether the same GO terms are showing up in multiple clusters.
        • Look up the definitions for each of the terms at http://geneontology.org. In your final presentation, you will discuss the biological interpretation of these GO terms. In other words, why does the cell react to cold shock by changing the expression of genes associated with these GO terms? Also, what does this have to do with the transcription factor being deleted (for the Δgln3 and Δswi4 groups)?
        • To easily look up the definitions, go to http://geneontology.org.
        • Copy and paste the GO ID (e.g. GO:0044848) into the search field at center top of the page called "Search GO Data".
        • In the results page, click on the button that says "Link to detailed information about <term>, in this case "biological phase"".
        • The definition will be on the next results page, e.g. here.
          • ribosome assembly: The aggregation, arrangement and bonding together of the mature ribosome and of its subunits [1]
          • RNA modification: The covalent alteration of one or more nucleotides within an RNA molecule to produce an RNA molecule with a sequence that differs from that coded genetically [2]
          • ncRNA catabolic process: The chemical reactions and pathways resulting in the breakdown of non-coding RNA transcripts (ncRNAs). Includes the breakdown of cryptic unstable transcripts [3]
          • intracellular membrane-bounded organelle: Organized structure of distinctive morphology and function, bounded by a single or double lipid bilayer membrane and occurring within the cell. Includes the nucleus, mitochondria, plastids, vacuoles, and vesicles. Excludes the plasma membrane [4]
          • tRNA metabolic process: The chemical reactions and pathways involving tRNA, transfer RNA, a class of relatively small RNA molecules responsible for mediating the insertion of amino acids into the sequence of nascent polypeptide chains during protein synthesis. Transfer RNA is characterized by the presence of many unusual minor bases, the function of which has not been completely established [5]
          • ribosomal subunit export from nucleus: The directed movement of a ribosomal subunit from the nucleus into the cytoplasm. [6]

Files

  1. Updated excel spreadsheet: Media:Wt_Microarraydata_ET.zip
  2. Updated powerpoint presentation: Media:Wt_profileimage_presentation_emmat.pptx
  3. File used to run stem, gene list tables, GO list tables: Media:Week10_filesforstem_wildtype_emmat.zip‎

Conclusion

For the assignment this week, we did clustering and GO term enrichment with STEM, downloaded and ran the STEM software using an updated excel spreadsheet, obtained figures and results from the software, and analyzed and interpreted the STEM results. A new worksheet was created in the excel workbook from week 8 that involved renaming and filtering data from the wt_ANOVA worksheet. Using this updated file, STEM created figures and tables representing clusters/profiles of genes with similar responses in terms of expression following cold shock. One profile was chosen (45) for further analysis in terms of the identification of genes associated with different p-values within the profile, and 6 GO terms were selected from the filtered list of corrected p<0.05 and looked up with http://geneontology.org.

Acknowledgements

  1. I worked with my homework partner Eddie Azinge during class and over text.
  2. Dr. Dahlquist for teaching and assisting us with the data analysis
  3. Microsoft Excel to allow for statistical analysis

While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source. Emmatyrnauer (talk) 15:14, 31 October 2017 (PDT)

References

  1. Gene Ontology Consortium. (2017). The Gene Ontology. Retrieved November 6, 2017, from http://geneontology.org
  2. LMU BioDB 2017. (2017). Week 10. Retrieved November 4, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10

Links

  1. My User Page
  2. List of Assignments
  3. List of Journal Entries
  4. List of Shared Journal Entries
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