Tessa A. Morris Week 11

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Revision as of 09:34, 26 March 2015 by Tessa A. Morris (talk | contribs) (change strain to GLN3)
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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: GLN3_xbar_(TIME)
    • Columns H and I label: GLN3_xbar_grand and (STRAIN)_ss_HO.
    • Columns J-N label: GLN3_ss_(TIME)
    • Columns O, P, and Q label: GLN3_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 GLN3 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 GLN3_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 GLN3 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 "GLN3_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

Biomathematical Modeling Navigation

User Page: Tessa A. Morris
Course Page: Biomathematical Modeling

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