Difference between revisions of "Johnllopez Week 8"
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===Experimental Design and Getting Ready=== | ===Experimental Design and Getting Ready=== | ||
− | The strain | + | The strain I used is ''dSWI4'', my timepoints I will be analyzing are t30, t60, t90, and t120. Data was not provided for t15. There were 4 replicates for each of the timepoints. |
− | + | ||
The first steps I took to complete this assignment were performed in class as I followed along to Dr. Dahlquist's instructions. Note that each time the list below advances 1 number, I performed a save. | The first steps I took to complete this assignment were performed in class as I followed along to Dr. Dahlquist's instructions. Note that each time the list below advances 1 number, I performed a save. | ||
#After initially downloading the Excel document, I went through and deleted all of the columns that did not relate to me and my partner's strain (dsWI4). Then, I went through the data and replaced cells with "NA" with a blank string. There were 3641 replacements. | #After initially downloading the Excel document, I went through and deleted all of the columns that did not relate to me and my partner's strain (dsWI4). Then, I went through the data and replaced cells with "NA" with a blank string. There were 3641 replacements. | ||
− | # I then created a new worksheet, | + | # I then renamed the document BIOL367_Fall2017_Dahlquist-microarray-data-master_20171017_JL. |
+ | |||
+ | ===Statistical Analysis Part 1=== | ||
+ | |||
+ | #I created a new worksheet named "dSWI4_ANOVA" which would act determine if any of the genes had a significantly different gene expression change han zero at any timepoint. | ||
+ | #I copied the "MasterIndex", "ID", and "Standard Names" columns from the master sheet, and created 5 column headers in the form of "dSWI4_AvgLogFC_(TIME)", and my time values were 15, 30, 60, 90, and 120. | ||
+ | #I populated the columns for t30, t60, t90, and t120 by calculating the average of the 4 replicates, using the =AVERAGE() function. | ||
+ | |||
Our total n would be 16 because we are analyzing 4 time points and we have 4 replicates. | Our total n would be 16 because we are analyzing 4 time points and we have 4 replicates. | ||
− | After letting 16 = n, we applied the two following functions in order to receive our dSWI4_Fstat and dsWI4_p-values: =((n-4)/4)*(Y2-AD2) and =FDIST(AE2,4,n-4). | + | #After letting 16 = n, we applied the two following functions in order to receive our dSWI4_Fstat and dsWI4_p-values: =((n-4)/4)*(Y2-AD2) and =FDIST(AE2,4,n-4). |
− | + | #Finally, I filtered through my p-value data to show only p-values less than 0.05. The result was 5475 records found. | |
− | |||
===Bonferonni and p value Correction=== | ===Bonferonni and p value Correction=== |
Revision as of 03:43, 24 October 2017
Electronic Lab Notebook
Experimental Design and Getting Ready
The strain I used is dSWI4, my timepoints I will be analyzing are t30, t60, t90, and t120. Data was not provided for t15. There were 4 replicates for each of the timepoints.
The first steps I took to complete this assignment were performed in class as I followed along to Dr. Dahlquist's instructions. Note that each time the list below advances 1 number, I performed a save.
- After initially downloading the Excel document, I went through and deleted all of the columns that did not relate to me and my partner's strain (dsWI4). Then, I went through the data and replaced cells with "NA" with a blank string. There were 3641 replacements.
- I then renamed the document BIOL367_Fall2017_Dahlquist-microarray-data-master_20171017_JL.
Statistical Analysis Part 1
- I created a new worksheet named "dSWI4_ANOVA" which would act determine if any of the genes had a significantly different gene expression change han zero at any timepoint.
- I copied the "MasterIndex", "ID", and "Standard Names" columns from the master sheet, and created 5 column headers in the form of "dSWI4_AvgLogFC_(TIME)", and my time values were 15, 30, 60, 90, and 120.
- I populated the columns for t30, t60, t90, and t120 by calculating the average of the 4 replicates, using the =AVERAGE() function.
Our total n would be 16 because we are analyzing 4 time points and we have 4 replicates.
- After letting 16 = n, we applied the two following functions in order to receive our dSWI4_Fstat and dsWI4_p-values: =((n-4)/4)*(Y2-AD2) and =FDIST(AE2,4,n-4).
- Finally, I filtered through my p-value data to show only p-values less than 0.05. The result was 5475 records found.
Bonferonni and p value Correction
I started this section by creating two new colums with the label "dsWI4_Bonferonni_p-value". Next, I filled the entire first column of that using the following equation: (dSWI4_p-value * 6189) and filled the column AG. Letting that result = AG, I filled the column AE by using the following formula: =IF(AG2>1,1,AG2).
Benjamini and Hochberg p value Correction
To do this, I created a new worksheet to represent the Benjamini and Hochberg p value Correction calculations. I copied the "MasterIndex", "ID", and "Standard Names" columns from the master sheet and the "p-values" sheet from the ANOVA sheet. Then, I sorted these values from smallest to largest by p-value. This was necessary to achieve an index from smallest p-value to largest. Then, I applied the 2 Benjamini and Hochberg p-value correction formulas, which were (D2*6189)/E2 and =IF(F2>1,1,F2). Finally, I put the values in ascending order by MasterIndex, and copied the last column into my ANOVA file.
Sanity Check: Number of genes significantly changed
I then sorted through all of the genes using the following criteria:
- I saw that 2,802 / 6,189 genes have p <.05, or 45.274%.
- I saw that 1,842 / 6,189 have p <.01, or 29.762%.
- I saw that 975 / 6,189 have p < .001, or 15.754%
- I saw that 512 / 6,189 have p < .0001, or 8.273%.
- Out of the Bonferonni-corrected p-value, 212 / 6,189 have p < .05, or 3.425%.
- Out of the Benjamini-Hochberg corrected p-value, 2,076 / 6,189 have p < .05, or 33.543%
Result Comparison
NSR1
- Unaltered P-Value: 1.196 E-7
- Bonferonni-corrected P-Value: 0.0007
- B-H-corrected P-Value: 1
- Average Log Fold Change @ 30: 3.253
- Average Log Fold Change @ 60: 3.565
- Average Log Fold Change @ 90: -3.693
- Average Log Fold Change @ 120: -.084
Given the change in expression from 60 to 90, it would appear that NSR1 changes expression due to cold shock in between this interval.
ADH1
- Unaltered P-Value: .161
- Bonferonni-corrected P-Value: 1
- B-H-corrected P-Value: .554
- Average Log Fold Change @ 30: -.252
- Average Log Fold Change @ 60: -1.126
- Average Log Fold Change @ 90: .144
- Average Log Fold Change @ 120: -.554
Given the change in expression from 60 to 90, it would appear that ADH1 changes expression due to cold shock in between this interval.
My Spreadsheet
File:JL BIOL367 Fall2017 Dahlquist-microarray-data-master 20171017.zipis my document.