ArashLari Week 10

File

File:Work.zip

Electronic Notebook

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 B-H corrected p value to be > 0.05 (that's greater than in this case).
       • It should be noted that because dHMO1 is a special case, we will use the regular p-value instead of the B-H corrected value.
       • 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.
     • 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.
      • Note: for this to work, you must replace all instances of "#DIV/0" with empty text, or else the program will not run properly.
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. "wt_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
        • 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).
      • 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. "wt_profile#_GOlist.txt", where you use "wt", "dGLN3", 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!
        • 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.

For this section, I chose Profile 2

Answer the following:

1. 1. • Why did you select this profile? In other words, why was it interesting to you?
      • I chose this profile because it seemed to have an interesting reaction to cold shock. It showed a strong negative expression change at minute 30, but at minute 90 it seems that the genes seemed to have "bounced back" and expressed a similar (but less extreme) change in the positive range. This is shown by the profile type of "(0, -2, -2, 0, 1 0)"
      • How many genes belong to this profile?
      • There are 49 genes assigned to this profile.
      • How many genes were expected to belong to this profile?
      • There were 18.7 genes expected, so more genes than expected were effected.
      • What is the p value for the enrichment of genes in this profile?
      • The p value for the enrichment of genes in this profile is 2.1 E-9, so very small.

 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.

1. 1. • 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?

There were 9 genes 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. How many GO terms are associated with this profile with a corrected p value < 0.05?

There were actually NO GO terms associated with a corrected p value < 0.05. This makes sense as dHMO1 is a special case.

1. 1. • 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.
        • 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 that was deleted from your strain?
        • 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.

My GO Terms defined

All of this information was taken directly from http://geneontology.org.

Accession: GO:0031399
Name: regulation of protein modification process
Ontology: biological_process
Definition: Any process that modulates the frequency, rate or extent of the covalent alteration of one or more amino acid residues within a protein.

Accession: GO:0050794
Name: regulation of cellular process
Ontology: biological_process
Definition: Any process that modulates the frequency, rate or extent of a cellular process, any of those that are carried out at the cellular level, but are not necessarily restricted to a single cell. For example, cell communication occurs among more than one cell, but occurs at the cellular level.

Accession: GO:0098588
Name: bounding membrane of organelle
Ontology: cellular_component
Definition: The lipid bilayer that forms the outer-most layer of an organelle.

Accession: GO:0050789
Name: regulation of biological process
Ontology: biological_process
Definition: Any process that modulates the frequency, rate or extent of a biological process. Biological processes are regulated by many means; examples include the control of gene expression, protein modification or interaction with a protein or substrate molecule.

Accession: GO:0006796
Name: phosphate-containing compound metabolic process
Ontology: biological_process
Definition: The chemical reactions and pathways involving the phosphate group, the anion or salt of any phosphoric acid.

Accession: GO:0005794
Name: Golgi apparatus
Ontology: cellular_component
Definition: A compound membranous cytoplasmic organelle of eukaryotic cells, consisting of flattened, ribosome-free vesicles arranged in a more or less regular stack. The Golgi apparatus differs from the endoplasmic reticulum in often having slightly thicker membranes, appearing in sections as a characteristic shallow semicircle so that the convex side (cis or entry face) abuts the endoplasmic reticulum, secretory vesicles emerging from the concave side (trans or exit face). In vertebrate cells there is usually one such organelle, while in invertebrates and plants, where they are known usually as dictyosomes, there may be several scattered in the cytoplasm. The Golgi apparatus processes proteins produced on the ribosomes of the rough endoplasmic reticulum; such processing includes modification of the core oligosaccharides of glycoproteins, and the sorting and packaging of proteins for transport to a variety of cellular locations. Three different regions of the Golgi are now recognized both in terms of structure and function: cis, in the vicinity of the cis face, trans, in the vicinity of the trans face, and medial, lying between the cis and trans regions.

This is the stopping point for the Week 10 Assignment. We will pick up the next steps in the analysis in subsequent weeks.

Summary of Knowledge

After processing all of the data from the spreadsheets of dHMO1, and reading the definitions of the genes most effected in profile #2, I learned some interesting information. It appears that dHMO1 "turns off" genes related to protein inscription when it's cold in order to conserve energy, and when it warms up again it seems to kick it into full gear in order to compensate for when it was too cold. Of course my rudimentary, limited knowledge of biology limits my understanding of this process, but this seems to be the main idea.

Acknowledgements

Hayden and I worked on this assignment together, we compare results together in class and texted each other if we had any questions. While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.

References

1. LMU BioDB 2017. (2017). Week 10. Retrieved November 2, 2017, from

https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_10 ArashLari (talk) 19:04, 5 November 2017 (PST)