Tessa A. Morris Week 11: Difference between revisions

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(→‎Data & Observations:: corrected and pasted in names)
(Correct B-H value)
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**p less than 0.001:404 of 6189 6.53%
**p less than 0.001:404 of 6189 6.53%
**p less than 0.0001:126 of 6189 2.04%
**p less than 0.0001:126 of 6189 2.04%
**B-H less than 0.05: 126 of 6189 2.04%
**B-H less than 0.05: 913 of 6189 14.75%
**Bonferroni less than 0.05: 26 of 6189 0.42%
**Bonferroni less than 0.05: 26 of 6189 0.42%
*Looking at NSR1
*Looking at NSR1

Revision as of 18:26, 5 May 2015

Electronic Lab Notebook

Date:

3/19/2015

Assignment:

Here

Partner:

Alyssa N Gomes

Purpose:

Analyze microarray data, comparing the wild type and a mutant strain, in this experiment Δgln3.

Methods:

Statistical Analysis Part 1: ANOVA

  1. Download microarray data from Lionshare and save to desktop, changing the name of the file to include initials (TM)
  2. Record number of replicates (change the color of the fill for each different time point to make it easier to see)
  3. Create a new worksheet, label it "stats"
  4. Copy the first two columns from sheet 1, "data"
  5. In Row 1:
    • Columns C-G label: dGLN3_xbar_(TIME)
    • Columns H and I label: dGLN3_xbar_grand and (STRAIN)_ss_HO.
    • Columns J-N label: dGLN3_ss_(TIME)
    • Columns O, P, and Q label: dGLN3_SS_full, Fstat and p-value.
  6. For C2 type =AVERAGE( then in the "data" sheet, highlight the data in Row 2 that is associated with the dGLN3 and t15
    • Copy this formula down the row in the stats cell by double clicking the black plus sign in the bottom right hand corner of C2
  7. Repeat for all of the time points
  8. Record total number of data points
  9. For H2 labeled dGLN3_xbar_grand take the average of C2-G2 and copy this formula down the column (Note Step 4)
  10. For I2 type =SUMSQ( then in the "data" sheet, highlight the data in Row 2 that is associated with the dGLN3 and t15 and copy this formula down the column (Note Step 4)
  11. Repeat for all of the time points
  12. For J2 type =SUMSQ(data!C2:F2)-4*stats!C2^2 and copy this formula down the column (Note Step 4)
    • "data!C2:F2" is the data associated with t15 // The number "4" is the number of data points // "stats!c2" gets the average from Step 4 for t15 // "^2" squares thevalue
    • Repeat for Cells K-N
    • To save time, take note of which columns the data for each time points is in, so the formula from J2 can be copied and then adjusted slightly
  13. For O2 type =sum(j2:n2) and copy down the column (Note Step 4)
  14. For P2 type =((n-5)/5)*(i2-o2)/o2, where n is the total number of data points
  15. For Q2 type =FDIST(P2,5,n-5), where n is the total number of data points
  16. To adjust the p-value to correct for the multiple testing problem
    • Label R2 "dGLN3_Bonferroni_p-value"
    • In R2 type =q2*6189 and copy down the column (Note Step 4)
  17. To see how many of the p-values are less than 0.05
    • Sort & Filter >> Filter >> on drop down arrow for Q1 (p-value) Number Filter >> less than >> 0.05 >> OK
    • The number of values less than 0.05 will appear in the bottom left hand of the screen
    • Record this value
  18. To correct p-values that are greater than 1 by the number 1
    • In S2 type =IF(r2>1,1,r2)
  19. Save the data set (Upload to Lionshareand share with professors, Dr. Dahlquist and Dr. Fitzpatrick, and lab partner, Alyssa N Gomes)

Data & Observations:

  • Alyssa and I were assigned to analyze Wild type vs. Δgln3
  • While Alyssa analyzes the wild type, I am going to analyze Δgln3
  • Note about excel: ID: gene id; standard name: gene symbol (more user friendly); each column represents one microarray
  • Time 15: 4 replicates // Time 30: 4 replicates // Time 60: 4 replicates // Time 90: 4 replicates // Time 120: 4 replicates
  • Total Number of data points: 20
  • 15: C-F // 30: G-J // 60: K-N // 90: O-R // 120: S-V
  • 1864 out of 6189 genes have a p-value of less than 0.05
  • Data was shared with Dr. Dahlquist, Dr. Fitzpatrick, and Alyssa N Gomes through Lionshare

Date:

3/26/2015

Methods:

Calculate the Benjamini & Hochberg p value Correction

  1. Insert a new worksheet named "B&H".
  2. First, create an index column by first typing "Index" into cell A1. Then type "1" into cell A2 and "2" into cell A3. Select both cells A2 and A3. Double-click on the plus sign on the lower right-hand corner of your selection to fill the column with a series of numbers from 1 to 6189.
  3. Copy and paste the column of ID's from one of the previous worksheets into column B.
  4. For the following, use Paste special > Paste values. Copy Column Q (the unadjusted p values) from the stats worksheet and paste it into Column C.
  5. Select all of columns A, B, and C. Sort by ascending values on Column C. Click the sort button from A to Z on the toolbar, in the window that appears, sort by column C, smallest to largest.
  6. Type the header "Rank" in cell D1. Repeat what you did in step 2 to create a series of numbers in ascending order from 1 to 6189. This is the p value rank, smallest to largest.
  7. Now you can calculate the Benjamini and Hochberg p value correction. Type "dGLN3_B-H_p-value" in cell E1. Type the following formula in cell E2: =(C2*6189)/D2 and press enter. Copy that equation to the entire column using the trick you learned last week.
  8. Type "dGLN3_B-H_p-value" into cell F1.
  9. Type the following formula into cell F2: =IF(E2>1,1,E2) and press enter. Copy that equation to the entire column using the trick you learned last week.
  10. Select columns A through F. Now sort them by your Index in Column A in ascending order.
  11. Copy column F and use Paste special < Paste values to paste it into column T of your stats sheet.

Sanity Check: Number of genes significantly changed

Before we move on to clustering and the biological analysis of the data, we want to perform a sanity check to make sure that we performed our data analysis correctly. We are going to find out the number of genes that are significantly changed at various p value cut-offs.

  • Go to the "stats" worksheet.
  • Select row A and select the menu item Data > Filter > Autofilter (The funnel icon on the Data tab). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
  • Click on the drop-down arrow on Column Q. Select "Custom". In the window that appears, set a criterion that will filter your data so that the p value has to be less than 0.05.
  • Answer the following questions:
    • How many genes have p < 0.05? and what is the percentage (out of 6189)?
    • How many genes have p < 0.01? and what is the percentage (out of 6189)?
    • How many genes have p < 0.001? and what is the percentage (out of 6189)?
    • How many genes have p < 0.0001? and what is the percentage (out of 6189)?
  • To see the relationship between the Bonferroni and Benjamini-Hochberg corrections, filter your data to determine the following:
    • How many genes are p < 0.05 for the Bonferroni-corrected p value? and what is the percentage (out of 6189)?
    • How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?
  • Compare the results with known data:
    • Find NSR1 in your dataset. What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values? What is its average Log fold change at each of the timepoints in the experiment?
    • Compare the numbers for the wild type (analyzed by Alyssa N Gomes) and the Δgln3
    • Record this information using sample PowerPoint slide making sure to create a title for the slide that gives the "message" of the slide.
    • Upload the slide onto OpenWetWare
    • Upload the updated spreadsheet to LionShare .

Clustering and Gene Ontology Analysis with STEM

  1. Begin by downloading and extracting 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.cmd to launch the STEM program.
      • In Seaver 120, we encountered an issue where the program would not launch on the Windows XP machines due to a lack of memory. (Even though the computers have been upgraded to Windows 7, do this to launch the program.) To get around this problem, launch STEM from the command line.
        • Go to the start menu and click on Programs > Accessories > Command Prompt.
        • You will need to navigate to the directory (folder) in which the STEM program resides. If you followed the instructions above and extracted the stem folder to the Desktop, type the following: cd Desktop\stem and press "Enter".
        • To launch the program then type: java -mx512M -jar stem.jar -d defaults.txt and press "Enter". This will launch the program with less memory allocated to it.
  2. Prepare your microarray data file for loading into STEM.
    • Insert a new worksheet into your Excel workbook, and name it "stem".
    • Copy the "Index" column from your "B&H" worksheet and paste it into column A of your "stem" worksheet. Select all of the data from your "stats" worksheet and Paste special > paste values into your "stem" worksheet, starting with column B.
      • Your leftmost column should have the column header "Index". Rename this column to "SPOT". Column B should be named "ID". Rename this column to "Gene Symbol".
      • 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.
      • Delete all of the data columns EXCEPT for the Average Log Fold change columns for each timepoint (for example, wt_xbar_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 it would be a good idea to turn on the file extensions by following the procedure on the class Help page.
  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 profiles 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.
      • For each of the 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!
  5. Analyzing and Interpreting STEM Results
    1. Select one of the profiles you saved in the previous step for further intepretation of the data. We suggest that you choose one that has a pattern of up- or down-regulated genes at the early (first three) timepoints. Answer the following:
      • Why did you select this profile? In other words, why was it interesting to you?
      • How many genes belong to this profile?
      • How many genes were expected to belong to this profile?
      • What 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? 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?
      • Select 10 Gene Ontology terms from your filtered list (either p < 0.05 or corrected p < 0.05). Look up the definitions for each of the terms at http://geneontology.org. Write a paragraph that describes 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?
        • 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 the upper left 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.

Data & Observations:

Sanity Check: Number of genes significantly changed for Δgln3

  • Looking at p-values for Δgln3
    • p less than 0.05: 1864 of 6189 30.11%
    • p less than 0.01: 1008 of 6189 16.29%
    • p less than 0.001:404 of 6189 6.53%
    • p less than 0.0001:126 of 6189 2.04%
    • B-H less than 0.05: 913 of 6189 14.75%
    • Bonferroni less than 0.05: 26 of 6189 0.42%
  • Looking at NSR1
    • For Δgln3:
      • Uncorrected p-value: 3.056458644
      • Bonferroni-corrected p-value:3.056458644
      • B-H-corrected p-value: 0.010915924
      • Average Log fold change:
        • 15 s: 3.2691
        • 30 s: 4.29565
        • 60 s: 2.53305
        • 90 s: -1.903425
        • 120 s: -1.729975
    • For wild type (from Alyssa's Week 11 individual journal)
      • Uncorrected p-value of: 1.43E-8
      • Bonferroni p-value of 8.86E-5
      • B-H-corrected p-value of 3.85E-6
      • Average Log fold change:
        • 15 s: 3.067775
        • 30 s: 3.3937
        • 60 s: 3.413875
        • 90 s: -1.41454
        • 120 s: -0.57006
    • Compare NSR1 values for Δgln3 and wild type
      • The p-values for the wild type were significantly lower than those of the Δgln3. The average log fold changes were similar for wild type and Δgln3. The values for the time points fom 15 - 60 seconds were all positive values. For the Δgln3 the values for these time points ranged from ~2.5 to 4.3, where for the wild type they ranged from ~3 to 3.4. The range for the wild type was smaller, but the values were similar to the Δgln3. For time points 90 and 120, the values were negative for both the wild type and Δgln3. The values were similar for the 90 s time point, but the value of the Δgln3 at 120 s was significantly more negative.
    • Powerpoint slide is Here
  • Clustering and Gene Ontology Analysis with STEM:Analyzing and Interpreting STEM Results
    • I selected profile 45 because it was the most significant profile according the STEM program. My partner Alyssa N Gomes also selected 45 of the wild type.
    • 366.0 genes are assigned to the profile.
    • 24.1 genes were expected.
    • The p-value was 2.9E-321 (significant)
    • There are 196 GO terms associated with this profile at p < 0.05.
    • There were 74 with a "corrected p-value" < 0.05.
    • 10 Gene Ontology terms with correctedp < 0.05 defined from http://geneontology.org
      1. GO:0022613 ribonucleoprotein complex biogenesis: A cellular process that results in the biosynthesis of constituent macromolecules, assembly, and arrangement of constituent parts of a complex containing RNA and proteins. Includes the biosynthesis of the constituent RNA and protein molecules, and those macromolecular modifications that are involved in synthesis or assembly of the ribonucleoprotein complex
      2. GO:0042254 ribosome biogenesis: A cellular process that results in the biosynthesis of constituent macromolecules, assembly, and arrangement of constituent parts of ribosome subunits; includes transport to the sites of protein synthesis.
      3. GO:0005730 nucleolus:A small, dense body one or more of which are present in the nucleus of eukaryotic cells. It is rich in RNA and protein, is not bounded by a limiting membrane, and is not seen during mitosis. Its prime function is the transcription of the nucleolar DNA into 45S ribosomal-precursor RNA, the processing of this RNA into 5.8S, 18S, and 28S components of ribosomal RNA, and the association of these components with 5S RNA and proteins synthesized outside the nucleolus. This association results in the formation of ribonucleoprotein precursors; these pass into the cytoplasm and mature into the 40S and 60S subunits of the ribosome.
      4. GO:0034470 ncRNA processing: Any process that results in the conversion of one or more primary non-coding RNA (ncRNA) transcripts into one or more mature ncRNA molecules
      5. GO:0006364 rRNA processing: Any process involved in the conversion of a primary ribosomal RNA (rRNA) transcript into one or more mature rRNA molecules
      6. GO:0016072 rRNA metabolic process: The chemical reactions and pathways involving rRNA, ribosomal RNA, a structural constituent of ribosomes.
      7. GO:0030684 preribosome: Any complex of pre-rRNAs, ribosomal proteins, and associated proteins formed during ribosome biogenesis.
      8. GO:0034660 ncRNA metabolic process: The chemical reactions and pathways involving non-coding RNA transcripts (ncRNAs)
      9. GO:0031981 nuclear lumen: The volume enclosed by the nuclear inner membrane.
      10. GO:0016070 RNA metabolic process:The cellular chemical reactions and pathways involving RNA, ribonucleic acid, one of the two main type of nucleic acid, consisting of a long, unbranched macromolecule formed from ribonucleotides joined in 3',5'-phosphodiester linkage.
    • The majority of the 10 Gene Ontology terms selected has some effect on the overall function of the cell, especially pertaining to RNA. When cold shock sets in, probably around the time point 90 s, the normally cellular functions stop functioning properly. There is a decrease in cellular activity and production of proteins and other macromolecules. The cell reacts to cold shock by changing the expression of genes associated with these GO terms, which all have the common theme of RNA production, synthesis, and processing. It appears that during cold shock, the cell seizes production, likely to conserve energy in order to help it survive.

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