Hhinsch Week 10

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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 "dHMO1_stem".
    • Select all of the data from your "dHMO1_ANOVA" worksheet and Paste special > paste values into your "dHMO1_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 p value to be > 0.05 (that's greater than in this case).
      • Note: We are using the p-value column for dHMO1 because of the lack of amount of genes in the corrected p-value columns.
        • 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. There are 1055 genes left.
      • Delete all of the data columns EXCEPT for the Average Log Fold change columns for each timepoint (for example, dHMO1_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, e.g. "dHMO1_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
      • 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, e.g. "dHMO1_profile#_GOlist.txt", where you use "dHMO1", etc. to indicate the dataset and where you replace the number symbol with the actual profile number. At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
  5. Analyzing and Interpreting STEM Results
    1. Select the one of the profiles you saved in the previous step for further interpretation 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.
      • I selected Profile 45 because it had a lot of genes assigned(158) and a few of the genes had a very large reaction to the cold shock both initially and as time went on.
      • 158 Genes belong to this profile.
      • 19.1 Genes were expected to belong to this profile.
      • The p-value for the enrichment of genes in this profile is 7.9E-93.(p-value=7.9E-93(significant)) 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. There are 142 GO terms associated with this profile at p < 0.05. 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. There are 12 GO terms associated with this profile with a corrected p value < 0.05.
      • Select 6 Gene Ontology terms from your filtered list (either p < 0.05 or 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.
          • GO:0090502:RNA phosphodiester bond hydrolysis, endonucleolytic Ontology:biological_process Definition:The chemical reactions and pathways involving the hydrolysis of internal 3',5'-phosphodiester bonds in one or two strands of ribonucleotides. Source: GOC:dph, GOC:tb.
          • GO:0000966:RNA 5'-end processingOntology:biological_process Definition:Any process involved in forming the mature 5' end of an RNA molecule. Source: GOC:krc.
          • GO:0044238:primary metabolic process Ontology:biological_process Definition:The chemical reactions and pathways involving those compounds which are formed as a part of the normal anabolic and catabolic processes. These processes take place in most, if not all, cells of the organism. Source: GOC:go_curators, http://www.metacyc.org.
          • GO:0006807:nitrogen compound metabolic process Ontology:biological_process Definition:The chemical reactions and pathways involving organic or inorganic compounds that contain nitrogen. Source: GOC:go_curators, CHEBI:51143, GOC:jl, ISBN:0198506732.
          • GO:0000470:maturation of LSU-rRNA Ontology:biological_process Definition:Any process involved in the maturation of a precursor Large SubUnit (LSU) ribosomal RNA (rRNA) molecule into a mature LSU-rRNA molecule. Source: GOC:curators.
          • GO:0000472:endonucleolytic cleavage to generate mature 5'-end of SSU-rRNA from (SSU-rRNA, 5.8S rRNA, LSU-rRNA) Ontology:biological_process Definition:Endonucleolytic cleavage between the 5'-External Transcribed Spacer (5'-ETS) and the 5' end of the SSU-rRNA of a tricistronic rRNA transcript that contains the Small Subunit (SSU) rRNA, the 5.8S rRNA, and the Large Subunit (LSU) rRNA in that order from 5' to 3' along the primary transcript, to produce the mature end of the SSU-rRNA. Source: PMID:10690410, GOC:curators.

This is the end of the Week 10 Assignment

Electronic Workbook summary

  • One should be able to follow the steps above in order to come to the same conclusions that I did.
  • It should again be noted that because dHMO1 is a special case, we used the p-value in the first part of the assignment instead of the corrected p-value like other strains.
  • This assignment was relatively straightforward. I chose Profile #45 because of the large amount of genes and the massive change in expression due to the cold shock.
  • It seems that some of the genes really reduced their expression when the cold shock was induced, but went back to normal as time went by. At least that was the trend of most of the genes. However, a few of the genes changed drastically on the x-axis.
  • There were only a few genes left to choose from for the interpretation of the GO lists, but I decided to choose 6 from the corrected p-value on the Go list because this would have the most stringent constraints. I felt that these genes I chose would be more significant.

Conclusion

I learned that the genes in the dHMO1 strain did not have many significant results, seeing that we had to use the uncorrected p-value to analyze the data. The profile I used(#45) showed a varying change in expression, shutting off the genes then turning them back on as time went on. It seems the cold shock had a large effect on some of the genes, and others were not changed that much. Due to my lack of knowledge of molecular biology, I can only make an assumption, but I presume that the genes that were effected by the cold shock were turned off in order to preserve themselves, then turned back on as time went on and the temperature rose.

Tab-Delimited Text Files created and used for STEM

Updated Excel File

Slides


Acknowledgments

  1. I worked with User:ArashLari over text and in class in order to compare results and make sure we did not use the same profile for the assignment.
  2. Dr. Dahlquist helped by giving detailed steps in order to complete the deliverable for this assignment.
  3. While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.Hhinsch (talk) 22:40, 6 November 2017 (PST)

References

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

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