Mbalducc Week 8 - Revision history

Mbalducc: /* Sanity Check: Number of genes significantly changed */

2017-12-05T23:20:18Z

‎Sanity Check: Number of genes significantly changed

Mbalducc: /* Microarray Data Analysis Lab Notebook */

2017-12-05T23:18:14Z

‎Microarray Data Analysis Lab Notebook

Mbalducc: /* Acknowledgements */ added more acknowledgements

2017-11-28T23:21:20Z

‎Acknowledgements: added more acknowledgements

Mbalducc: /* Sanity Check: Number of genes significantly changed */

2017-11-28T23:20:15Z

‎Sanity Check: Number of genes significantly changed

Mbalducc: /* Calculate the Benjamini & Hochberg p value Correction */ past tense

2017-11-28T23:17:35Z

‎Calculate the Benjamini & Hochberg p value Correction: past tense

Mbalducc: /* Calculate the Bonferroni and p value Correction */ past tense

2017-11-28T23:14:13Z

‎Calculate the Bonferroni and p value Correction: past tense

Mbalducc: /* Statistical Data Analysis Part 1 */ changed to past tense

2017-11-27T20:16:06Z

‎Statistical Data Analysis Part 1: changed to past tense

Mbalducc: /* Statistical Data Analysis Part 1 */ changing to past tense

2017-11-27T20:07:47Z

‎Statistical Data Analysis Part 1: changing to past tense

Mbalducc: /* Summary Paragraph */

2017-10-23T23:53:13Z

‎Summary Paragraph

Mbalducc: added links to my other pages

2017-10-22T22:34:18Z

added links to my other pages

@@ Line 103: / Line 103: @@
 **AvgLogFC t90: -0.075703512
 **AvgLogFC t120: 0.422687452
-**HSF1 does not appear to change due to heat shock.
+**HSF1 does appear to change expression due to cold shock in the dCIN5 experiment.
 ==== Summary Paragraph ====

@@ Line 1: / Line 1: @@
 =Microarray Data Analysis Lab Notebook=
-I analyzed the strain dCIN5.
+I analyzed the strain dCIN5, there were 4 replicates per data point.
 replacements were made when I deleted the NAs from the spreadsheet.

@@ Line 112: / Line 112: @@
 =Acknowledgements=
-I worked with my homework partner, [[User:Simonwro120|Simon Wroblewski]], on this assignment. We worked together in class and compared results for our formulas in Excel. We also spoke outside of class to compare what we were doing to make sure we were both getting the same results for the same strain, dCIN5.
+I worked with my homework partner, [[User:Simonwro120|Simon Wroblewski]], on this assignment. We worked together in class and compared results for our formulas in Excel. We also spoke outside of class to compare what we were doing to make sure we were both getting the same results for the same strain, dCIN5. I also used the instructions from the [[Week 8]] page in my journal, edited to show that I did them in the past.
 While I worked with the people noted above, this individual journal entry was completed by me and not copied from another source.

@@ Line 62: / Line 62: @@
 ==== Sanity Check: Number of genes significantly changed ====
-Before we move on to further analysis of the data, we want to perform a more extensive 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.
+* Opened my dCIN5_ANOVA worksheet.
+* Selected row 1 and selected the menu item Data > Filter > Autofilter.  This will enabled me to filter the data according to criteria I set.
-* Go to your dCIN5_ANOVA worksheet.
+* Clicked on the drop-down arrow for the unadjusted p value.  Set a criterion to filter my data so that the p value has to be less than 0.05.
 ** '''''How many genes have p < 0.05?  and what is the percentage (out of 6189)?'''''
@@ Line 77: / Line 75: @@
 ***358 genes have p < 0.0001. The percentage is 6%.
-* When we use a p value cut-off of p < 0.05, what we are saying is that you would have seen a gene expression change that deviates this far from zero by chance less than 5% of the time.
+* I have just performed 6189 hypothesis tests.  Another way to state what we are seeing with p < 0.05 is that we would expect to see this a gene expression change for at least one of the timepoints by chance in about 5% of our tests, or 309 times.  Since we have more than 309 genes that pass this cut off, we know that some genes are significantly changed.  However, we don't know ''which'' ones.  To apply a more stringent criterion to our p values, we performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values.  The Bonferroni correction is very stringent.  The Benjamini-Hochberg correction is less stringent.  To see this relationship, 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)?'''''
 ***151 genes are p < 0.05. The percentage is 2%.

@@ Line 47: / Line 47: @@
 ==== Calculate the Benjamini & Hochberg p value Correction ====
-# Insert a new worksheet named "dCIN5_ANOVA_B-H".
+# Inserted a new worksheet named "dCIN5_ANOVA_B-H".
-# Copy and paste the "MasterIndex", "ID", and "Standard Name" columns from your previous worksheet into the first three columns of the new worksheet.
+# Copied and pasted the "MasterIndex", "ID", and "Standard Name" columns from my previous worksheet into the first three columns of the new worksheet.
-# For the following, use Paste special > Paste values.  Copy your unadjusted p values from your ANOVA worksheet and paste it into Column D.
+# Used Paste special > Paste values.  Copied my unadjusted p values from my ANOVA worksheet and pasted it into Column D.
-# Select all of columns A, B, C, and D. Sort by ascending values on Column D. Click the sort button from A to Z on the toolbar, in the window that appears, sort by column D, smallest to largest.
+# Selected all of columns A, B, C, and D. Sorted by ascending values on Column D. Clicked the sort button from A to Z on the toolbar, in the window that appeared, sorted by column D, smallest to largest.
-# Type the header "Rank" in cell E1.  We will create a series of numbers in ascending order from 1 to 6189 in this column.  This is the p value rank, smallest to largest.  Type "1" into cell E2 and "2" into cell E3. Select both cells E2 and E3. 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.
+# Typed the header "Rank" in cell E1.  Created a series of numbers in ascending order from 1 to 6189 in this column.  This was the p value rank, smallest to largest.  Typed "1" into cell E2 and "2" into cell E3. Selected both cells E2 and E3. Double-clicked on the plus sign on the lower right-hand corner of my selection to fill the column with a series of numbers from 1 to 6189.
-# Now you can calculate the Benjamini and Hochberg p value correction. Type dCIN5_B-H_p-value in cell F1. Type the following formula in cell F2: <code>=(D2*6189)/E2</code> and press enter. Copy that equation to the entire column.
+# Typed dCIN5_B-H_p-value in cell F1. Typed the following formula in cell F2: <code>=(D2*6189)/E2</code> and pressed enter. Copied that equation to the entire column.
-# Type "dCIN5_B-H_p-value" into cell G1.
+# Typed "dCIN5_B-H_p-value" into cell G1.
-# Type the following formula into cell G2: <code>=IF(F2>1,1,F2)</code> and press enter. Copy that equation to the entire column.
+# Typed the following formula into cell G2: <code>=IF(F2>1,1,F2)</code> and pressed enter. Copied that equation to the entire column.
-# Select columns A through G.  Now sort them by your MasterIndex in Column A in ascending order.
+# Selected columns A through G.  Sorted them by my MasterIndex in Column A in ascending order.
-# Copy column G and use Paste special > Paste values to paste it into the next column on the right of your ANOVA sheet.
+# Copied column G and used Paste special > Paste values to paste it into the next column on the right of your ANOVA sheet.
-* '''''Zip and upload the .xlsx file that you have just created to the wiki.'''''
+* '''''Zipped and uploaded the .xlsx file that I have created to the wiki.'''''
 ==== Sanity Check: Number of genes significantly changed ====

@@ Line 41: / Line 41: @@
 ==== Calculate the Bonferroni and p value Correction ====
-# Now we will perform adjustments to the p value to correct for the [https://xkcd.com/882/ multiple testing problem].  Label the next two columns to the right with the same label, dCIN5_Bonferroni_p-value.
+# I performed adjustments to the p value to correct for the [https://xkcd.com/882/ multiple testing problem].  Labelled the next two columns to the right with the same label, dCIN5_Bonferroni_p-value.
-# Type the equation <code>=AK2*6189</code>, Upon completion of this single computation, use the Step (10) trick to copy the formula throughout the column.
+# Typed the equation <code>=AK2*6189</code>, Upon completion of this single computation, used the Step (10) trick to copy the formula throughout the column.
-# Replace any corrected p value that is greater than 1 by the number 1 by typing the following formula into the first cell below the second dCIN5_Bonferroni_p-value header: <code>=IF(AL2>1,1,AL2)</code>. Use the Step (10) trick to copy the formula throughout the column.
+# Replaced any corrected p value that is greater than 1 by the number 1 by typing the following formula into the first cell below the second dCIN5_Bonferroni_p-value header: <code>=IF(AL2>1,1,AL2)</code>. Used the Step (10) trick to copy the formula throughout the column.
 ==== Calculate the Benjamini & Hochberg p value Correction ====

@@ Line 18: / Line 18: @@
 # Clicked on this cell and positioned my cursor at the bottom right corner. I saw the cursor change to a thin black plus sign (not a chubby white one). When it did, double clicked, and the formula is copied to the entire column of 6188 other genes.
 # Repeated steps (4) through (8) with the t30, t60, t90, and the t120 data.
-# Now in the first empty column to the right of the dCIN5_AvgLogFC_t120 calculation, create the column header dCIN5_ss_HO.
+# In the first empty column to the right of the dCIN5_AvgLogFC_t120 calculation, created the column header dCIN5_ss_HO.
-# In the first cell below this header, type =SUMSQ(D2:W2)
+# In the first cell below this header, typed =SUMSQ(D2:W2)
-# Highlight all the LogFC data in row 2 for your dCIN5 (but not the AvgLogFC), press the closing paren key (shift 0),and press the "enter" key.
+# Highlighted all the LogFC data in row 2 for the dCIN5 (but not the AvgLogFC), pressed the closing paren key (shift 0), and pressed the "enter" key.
-# In the next empty column to the right of dCIN5_ss_HO, create the column headers dCIN5_ss_(TIME) as in (3).
+# In the next empty column to the right of dCIN5_ss_HO, created the column headers dCIN5_ss_(TIME) as in (3).
-# Make a note of how many data points you have at each time point for your strain.  For dCIN5, there are 4 data points for each time point. There are 20 total data points.
+# Made a note of how many data points I had at each time point for my strain.  For dCIN5, there are 4 data points for each time point. There are 20 total data points.
-# In the first cell below the header dCIN5_ss_t15, type <code>=SUMSQ(D2:G2)-COUNTA(D2:G2)*X2^2</code> and hit enter.
+# In the first cell below the header dCIN5_ss_t15, typed <code>=SUMSQ(D2:G2)-COUNTA(D2:G2)*X2^2</code> and hit enter.
 #* The <code>COUNTA</code> function counts the number of cells in the specified range that have data in them (i.e., does not count cells with missing values).
-#* Upon completion of this single computation, use the Step (7) trick to copy the formula throughout the column.
+#* Upon completion of this single computation, used the Step (7) trick to copy the formula throughout the column.
-# Repeat this computation for the t30 through t120 data points.  Again, be sure to get the data for each time point, type the right number of data points, and get the average from the appropriate cell for each time point, and copy the formula to the whole column for each computation.
+# Repeated this computation for the t30 through t120 data points.
-# In the first column to the right of dCIN5_ss_t120, create the column header dCIN5_SS_full.
+# In the first column to the right of dCIN5_ss_t120, created the column header dCIN5_SS_full.
-# In the first row below this header, type <code>=sum(AD2:AH2)</code> and hit enter.
+# In the first row below this header, typed <code>=sum(AD2:AH2)</code> and hit enter.
-# In the next two columns to the right, create the headers dCIN5_Fstat and dCIN5_p-value.
+# In the next two columns to the right, created the headers dCIN5_Fstat and dCIN5_p-value.
-# Recall the number of data points from (13): 20.
+# Recalled the number of data points from (13): 20.
-# In the first cell of the dCIN5_Fstat column, type <code>=((20-5)/5)*(AC2-AI2>)/AI2</code> and hit enter.
+# In the first cell of the dCIN5_Fstat column, typed <code>=((20-5)/5)*(AC2-AI2>)/AI2</code> and hit enter.
-#* Copy to the whole column.
+#* Copied to the whole column.
-# In the first cell below the dCIN5_p-value header, type <code>=FDIST(AJ2,5,20-5)</code>. Copy to the whole column.
+# In the first cell below the dCIN5_p-value header, typed <code>=FDIST(AJ2,5,20-5)</code>. Copied to the whole column.
-# Before we move on to the next step, we will perform a quick sanity check to see if we did all of these computations correctly.
+# Performed a quick sanity check to see if I did all of these computations correctly.
-#*  Click on cell A1 and click on the Data tab.  Select the Filter icon (looks like a funnel). 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.
+#*  Clicked on cell A1 and clicked on the Data tab.  Selected the Filter icon (looks like a funnel). Little drop-down arrows appeared at the top of each column. This enabled me to filter the data according to criteria I set.
-#* Click on the drop-down arrow on your dCIN5_p-value column. Select "Number Filters". 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.
+#* Clicked on the drop-down arrow on my dCIN5_p-value column. Selected "Number Filters". In the window that appeared, set a criterion that filtered my data so that the p value had to be less than 0.05.
-#* Excel will now only display the rows that correspond to data meeting that filtering criterion.  A number will appear in the lower left hand corner of the window giving you the number of rows that meet that criterion.  We will check our results with each other to make sure that the computations were performed correctly.
+#* Excel now only displayed the rows that corresponded to data meeting that filtering criterion.  A number appeared in the lower left hand corner of the window giving me the number of rows that meet that criterion.
 ==== Calculate the Bonferroni and p value Correction ====

@@ Line 10: / Line 10: @@
 ==Statistical Data Analysis Part 1==
-# Create a new worksheet, naming it dCIN5_ANOVA".
+# Created a new worksheet, naming it dCIN5_ANOVA".
-# Copy the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for the strain dCIN5 and paste it into your new worksheet.  Copy the columns containing the data for dCIN5 and paste it into your new worksheet.
+# Copied the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for the strain dCIN5 and pasted it into my new worksheet.  Copied the columns containing the data for dCIN5 and pasted it into my new worksheet.
-# At the top of the first column to the right of your data, create five column headers of the form dCIN5_AvgLogFC_(TIME) where (TIME) is 15, 30, 60, 90, and 120.
+# At the top of the first column to the right of my data, created five column headers of the form dCIN5_AvgLogFC_(TIME) where (TIME) is 15, 30, 60, 90, and 120.
-# In the cell below the dCIN5_AvgLogFC_t15 header, type =AVERAGE(D2:G2) and hit enter.
+# In the cell below the dCIN5_AvgLogFC_t15 header, typed =AVERAGE(D2:G2) and hit enter.
-# Then highlight all the data in row 2 associated with dCIN5 and t15, press the closing paren key (shift 0),and press the "enter" key.
+# Then highlighted all the data in row 2 associated with dCIN5 and t15, pressed the closing paren key (shift 0),and pressed the "enter" key.
-# This cell now contains the average of the log fold change data from the first gene at t=15 minutes.
+# This cell now contained the average of the log fold change data from the first gene at t=15 minutes.
-# Click on this cell and position your cursor at the bottom right corner. You should see your cursor change to a thin black plus sign (not a chubby white one). When it does, double click, and the formula will magically be copied to the entire column of 6188 other genes.
+# Clicked on this cell and positioned my cursor at the bottom right corner. I saw the cursor change to a thin black plus sign (not a chubby white one). When it did, double clicked, and the formula is copied to the entire column of 6188 other genes.
-# Repeat steps (4) through (8) with the t30, t60, t90, and the t120 data.
+# Repeated steps (4) through (8) with the t30, t60, t90, and the t120 data.
 # Now in the first empty column to the right of the dCIN5_AvgLogFC_t120 calculation, create the column header dCIN5_ss_HO.
 # In the first cell below this header, type =SUMSQ(D2:W2)

@@ Line 110: / Line 110: @@
 ==== Summary Paragraph ====
-* Write a summary paragraph that gives the conclusions from this week's analysis.
+In this experiment, data for the expression of yeast genes in cold shock with the deletion of CIN5 were analyzed. There were 5 time points, each corresponding to different temperatures, first the cold shock, then the recovery. The data shows the log fold change for the expression of each gene at each time point. From this data, the p-values were found for each gene. Then the Bonferroni corrected and B-H corrected p-values were found. The Bonferroni correction gave the most strict results, with only 151 genes (2% of the total) having a p-value of less than 0.05. This is compared to the B-H adjusted, which showed 1453 genes (23% of the total) having a p-value of less than 0.05, and compared to the unadjusted p-value, where 2290 genes (37% of the total) had p-values of less than 0.05.
 =Acknowledgements=

← Older revision		Revision as of 22:34, 22 October 2017
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	LMU BioDB 2017. (2017). Week 8. Retrieved October 19, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_8		LMU BioDB 2017. (2017). Week 8. Retrieved October 19, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_8
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