Johnllopez Week 8 - Revision history

Johnllopez616: Added a link to my powerrpoint

2017-10-24T04:45:33Z

Added a link to my powerrpoint

Johnllopez616: Added my summary, acknowledgements, and references

2017-10-24T04:04:33Z

Added my summary, acknowledgements, and references

Johnllopez616: Added my template

2017-10-24T03:45:01Z

Added my template

Johnllopez616: Added the "experimental design and getting ready" section

2017-10-24T03:43:25Z

Added the "experimental design and getting ready" section

Johnllopez616: Added the result comparison

2017-10-24T03:23:54Z

Added the result comparison

Johnllopez616: adjusted the formulas

2017-10-24T01:51:39Z

adjusted the formulas

Johnllopez616: /* Sanity Check: Number of genes significantly changed */ Added the raw, uninterpreted sanity check numbers

2017-10-24T01:50:38Z

‎Sanity Check: Number of genes significantly changed: Added the raw, uninterpreted sanity check numbers

Johnllopez616: Change to my Statistical Analysis Part 1 values.

2017-10-24T01:32:51Z

Change to my Statistical Analysis Part 1 values.

Johnllopez616: Explained how I did the Benjamini and Hochberg p value Correction.

2017-10-24T01:27:13Z

Explained how I did the Benjamini and Hochberg p value Correction.

Johnllopez616: /* Bonferonni and p value Correction */ Explained how I filled the next column.

2017-10-23T23:12:59Z

‎Bonferonni and p value Correction: Explained how I filled the next column.

@@ Line 15: / Line 15: @@
 #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.
+#I then created a column which applied the sum of squares calculation (=SUMSQ) for the average logs of fc (as explained in the previous step).
+#Next, I performed the following formula in 4 columns for the timepoints 30, 60, 90, and 120:  = SUMSQ([average log of range]-COUNTA([range of cells for the timepoint])) * [Average log of range].
-Our total n would be 16 because we are analyzing 4 time points and we have 4 replicates.
+#In another column, I computed the aum of the values in the previous 2 steps.
+# I decided 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.
@@ Line 31: / Line 31: @@
 ===Sanity Check: Number of genes significantly changed===
-I then sorted through all of the genes using the following criteria:
+I then sorted through all of the genes using the following criteria, with x representing the percentage of time you would see the expression change that deviates far from zero in the formula p < x.
 # 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%.
@@ Line 38: / Line 39: @@
 # 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===
@@ Line 66: / Line 70: @@
 Our ultimate goals were to perform the ANOVA tests to determine if any of the genes we were given had a gene expression change that was significantly different than zero at any time point, despite us using 4 time points. The ANOVA tests gave us p-values, which tells us the expression change. After applying the Bonferonni-correction and Benjamini-Hotchberg p-value corrections, we were informed that several of the genes (given in percentages) had expression changes which deviates far from zero by chance less at least than 5% of the time. The correction tests gave us even more precise deviation percentages. We saw that 45.274%. of the p-values had this, and 3.425% / 33.543% had them when given the B / B-H corrections.
 ==My Documents==
-[[File:JL BIOL367 Fall2017 Dahlquist-microarray-data-master 20171017.zip | Here]]is my document.
+[[Media: JL BIOL367 Fall2017 Dahlquist-microarray-data-master 20171017.zip | Here]] are my spreadsheets.
 ==Acknowledgements and References==
@@ Line 73: / Line 78: @@
 While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.
 ===References===

@@ Line 64: / Line 64: @@
 ===Summary===
 ==My Documents==
 [[File:JL BIOL367 Fall2017 Dahlquist-microarray-data-master 20171017.zip | Here]]is my document.
@@ Line 69: / Line 70: @@
 ==Acknowledgements and References==
 ===Acknowledgements===
 ===References===
 {{Template:johnllopez616}}

@@ Line 61: / Line 61: @@
 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.zip | Here]]is my document.

@@ Line 3: / Line 3: @@
 ===Experimental Design and Getting Ready===
-The strain comparison I used is dSWI_4, the dat individual dataset that you will analyze, the filename, the number of replicates for each strain and each time point in your data.
+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.
-===Classwork===
 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 created a new worksheet, naming it
+# I then renamed the document BIOL367_Fall2017_Dahlquist-microarray-data-master_20171017_JL.
 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===

@@ Line 33: / Line 33: @@
 # Out of the  Benjamini-Hochberg corrected p-value, 2,076 / 6,189 have p < .05, or 33.543%
 ==My Spreadsheet==
 [[File:JL BIOL367 Fall2017 Dahlquist-microarray-data-master 20171017.zip | Here]]is my document.

@@ Line 30: / Line 30: @@
 # 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 < .01, or 3.425%.
+# 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 or 33.543%
+# Out of the  Benjamini-Hochberg corrected p-value, 2,076 / 6,189 have p < .05, or 33.543%
 ==My Spreadsheet==
 [[File:JL BIOL367 Fall2017 Dahlquist-microarray-data-master 20171017.zip | Here]]is my document.

@@ Line 12: / Line 12: @@
 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-5)/5)*(Y2-AD2) and =FDIST(AE2,5,n-5).
+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).
 Next, I filtered through my p-value data to show only p-values less than 0.05. The result was 5475 records found.
@@ Line 21: / Line 21: @@
 ===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.
+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.
 ==My Spreadsheet==
 [[File:JL BIOL367 Fall2017 Dahlquist-microarray-data-master 20171017.zip | Here]]is my document.