Kara M Dismuke Week 11 Journal

Electronic Journal Notebook

Summary of what you need to turn in for the individual Week 11 assignment Upload your updated Excel spreadsheet to LionShare that has today's calculations in it. Use the same filename as before so that the download link that you already provided to Drs. Dahlquist and Fitzpatrick will still work. Create, upload to OpenWetWare, and link to a PowerPoint presentation that contains the p value table and the screenshots of your stem results. Each slide in the presentation should have a meaningful title that describes the main message of the slide. These slides will form the basis of your final presentation in the class. [[ Zip together all of the tab-delimited text files that you created for and from stem and upload them to LionShare: the file that was saved from your original spreadsheet that you used to run stem each of the genelist and GOlist files for each of your significant profiles.

Methods

• Project Partner: Kristen Horstmann
• Dahlquist lab microarray data set: Wild type vs. Δzap1
  • Note: Kristen will be analyzing the Wild type strain data and I (Kara) will be analyzing the Δzap1 strain data.

• For dzap1...[File: Dahlquist_Lab_Microarray_Data_dZAP1_20150319_KMD]
  • • t15: 4 replicates
    • t30: 4 replicates
    • t60: 4 replicates
    • t90: 4 replicates
    • t120: 4 replicates
• Procedural Notes:
  • After downloading the data, we used Excel to compute the averages of our data from the replicates from each time point for each gene.
    • column headings: "dZAP1_xbar_t15", "dZAP1_xbar_t30", "dZAP1_xbar_t60", "dZAP1_xbar_t90", "dZAP1_xbar_t120"
  • Then we computed the "grand" average for all of our data for a particular gene. For the case of dZAP1, this could be computed by averaging all the data (this is contains all the data at each time point) or by averaging the averages previously connected because there were 4 replicates for each time point. If done both ways, you should get the same value.
  • column heading: "ZAP1_xbar_grand"
  • We used Excel to compute the sum of squares for the data for each individual gene.
    • column heading: "ZAP1_ss_HO"
  • For each time point, we calculated the sum of squares by squaring the original data, and then subtracted from this the replicates times the average of the time point's data squared.
    • column headings: "ZAP1_ss_t15", "ZAP1_ss_t30", "ZAP1_ss_t60", "ZAP1_ss_t90", "ZAP1_ss_t120"
  • Then, we took these calculated values for each time point and found the average of them.
    • column heading: "ZAP1_SS_full"

• Excel file: Media:Dahlquist_Lab_Microarray_Data_dZAP1_20150326_KMD.xls
• STEM Text file

Results

unadjusted p value <.05

• wild-type: 31.42% (2378/6189)
• dZAP1: 36.58% (2264/6189)

unadjusted p-value < .01

• wild-type: 24.67% (1527/6189)
• dZAP1: 23.35% (1445/6189)

unadjusted p-value < .001

• wild-type: 13.90% (860/6189)
• dZAP1: 12.80% (792/6189)

unadjusted p-value < .0001

• wild-type: 7.43% (460/6189)
• dZAP1: 6.69% (414/6189)

Benjamini & Hochberg-adjusted p-value < .05

• wild-type: 26.76% (1656/6189)
• dzap1: 24.85% (1538/6189)

Bonferroni-adjusted p-value <.05

• wild-type: 3.68% (228/6189)
• dZAP1: 3.10% (192/6189)

• Note: We've organized this data into a table in a PPT slide: Table 1 (wildtype and dZAP1 p-values)‎

• STEM Results

Did this step first: Prepare your microarray data file for loading into STEM.

use B-H adjusted p values, to find ones that are not significant (>.05) delete columns H through T (kept columns with xbar...our averages)