Difference between revisions of "Emmatyrnauer Week 10"

Revision as of 20:45, 19 November 2017

Electronic Notebook

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

Clustering and GO Term Enrichment with stem

1. Preparing the microarray data file for loading into STEM.
   • Downloaded my Excel workbook that you used for my Week 8 assignment.
   • Inserted a new worksheet into my Excel workbook, and named it "wt_stem".
   • Selected all of the data from my "wt_ANOVA" worksheet and Pasted special > pasted values into my "wt_stem" worksheet.
     • Made sure that the leftmost column had the column header "Master_Index". I renamed this column to "SPOT". Made sure Column B was named "ID". I renamed this column to "Gene Symbol". I deleted the column named "Standard_Name".
     • Filtered the data on the B-H corrected p value to be > 0.05 (that's greater than in this case).
       • Once the data was filtered, I selected all of the rows (except for the header row) and deleted the rows by right-clicking and choosing "Delete Row" from the context menu. I then undid the filter. This ensured that only the genes with a "significant" change in expression and not the noise would be clustered. The number of genes left was 1822 genes
     • I then deleted all of the data columns EXCEPT for the Average Log Fold change columns for each timepoint (for example, wt_AvgLogFC_t15, etc.).
     • Renamed the data columns with just the time and units (for example, 15m, 30m, etc.).
     • Saved my work. I used Save As to save this spreadsheet as Text (Tab-delimited) (*.txt), clicked OK to the warnings and closed the file.
       • I made sure all of the file extensions were turned on.
2. I downloaded and extracted the STEM software from here.
   • Clicked on the download link, registered, and downloaded the stem.zip file to the Desktop.
   • Unziped the file.
   • This created a folder called stem. Inside the folder, I double-clicked 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, I clicked on the Browse... button to navigate to and select my file.
      • Clicked on the radio button No normalization/add 0.
      • Checked the box next to Spot IDs included in the data file.
   2. In section 2 (Gene Info) of the main STEM interface window, I selected Saccharomyces cerevisiae (SGD), from the drop-down menu for Gene Annotation Source. I selected No cross references, from the Cross Reference Source drop-down menu. I selected No Gene Locations from the Gene Location Source drop-down menu.
   3. In section 3 (Options) of the main STEM interface window, I made sure that the Clustering Method said "STEM Clustering Method" and did 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) I clicked on the yellow Execute button to run STEM.
4. Viewing and Saving STEM Results
   1. A new window opened called "All STEM Profiles (1)". Each box corresponded to a model expression profile. Colored profiles had a statistically significant number of genes assigned; they were arranged in order from most to least significant p value. Profiles with the same color belonged to the same cluster of profiles. The number in each box was simply an ID number for the profile.
      • I clicked on the button that said "Interface Options...". At the bottom of the Interface Options window that appeared below where it said "X-axis scale should be:", I clicked on the radio button that said "Based on real time," then closed the Interface Options window.
      • I took a screenshot of this window and pasted it into a PowerPoint presentation to save my figures.
   2. I clicked 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.
      • I took a screenshot of each of the individual profile windows and saved the images in my PowerPoint presentation.
      • At the bottom of each profile window, there were two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, I clicked on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appeared, I clicked on the "Save Table" button and saved the file to the desktop. I made sure the filename was descriptive of the contents: wt_profile#_genelist.txt
        • I compressed and uploaded these files to the wiki and linked to them on my individual journal page.
      • For each of the significant profiles, I clicked on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appeared, I clicked on the "Save Table" button and saved the file to the desktop. I made the filename descriptive of the contents: wt_profile#_GOlist.txt. At this point I had saved all of the primary data from the STEM software and began interpretation of the results.
        • I compressed and uploaded these files to the wiki and linked to them on my individual journal page.
5. Analyzing and Interpreting STEM Results
   1. I selected one of the profiles I saved in the previous step for further interpretation of the data.
      • 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

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
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