Bhamilton18 Week 8 - Revision history

Bhamilton18: fixed typo

2017-10-23T23:38:49Z

fixed typo

Bhamilton18: Added Data Information section at the top

2017-10-23T23:35:51Z

Added Data Information section at the top

Bhamilton18: Added powerpoint slide

2017-10-23T23:28:20Z

Added powerpoint slide

Bhamilton18: Added analysis and summary to my gene average folds and the overall summary.

2017-10-23T23:22:52Z

Added analysis and summary to my gene average folds and the overall summary.

Bhamilton18: Added more sanity check answers

2017-10-23T22:58:39Z

Added more sanity check answers

Bhamilton18: added values to sanity check section

2017-10-23T22:47:26Z

added values to sanity check section

Bhamilton18: Updated Hochberg section

2017-10-23T22:05:10Z

Updated Hochberg section

Bhamilton18: Added p-value 0.5 criteria answer, and Bonferroni

2017-10-23T21:53:08Z

Added p-value 0.5 criteria answer, and Bonferroni

Bhamilton18: Fixed Links template

2017-10-23T20:54:46Z

Fixed Links template

Bhamilton18: Added to Acknowledgments and Category and Links

2017-10-23T20:53:58Z

Added to Acknowledgments and Category and Links

@@ Line 76: / Line 76: @@
 # 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, dHAP4_Bonferroni_p-value.
-# Type the equation <code>=<AI2*6189</code>, Upon completion of this single computation, use the Step (7) trick to copy the formula throughout the column.
+# Type the equation <code>=AI2*6189</code>, Upon completion of this single computation, use the Step (7) trick to copy the formula throughout the column.
 #*AI2 being the dHAP4_p-value for the gene in the second row.
 # 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 dHAP4_Bonferroni_p-value header: <code>=IF(AJ2>1,1,AJ1)</code>. Use the Step (7) trick to copy the formula throughout the column.

@@ Line 1: / Line 1: @@
-==Zip Files==
+==Data Information==
-[[Media: BIOL367_Fall2017_Dahlquist-microarray-data-master_20171017BVH1.zip | dHAP4]]
+Strain Comparison: dHAP4
-[[Media: BIOL367_F17_sample_p-value_slide_BVH.zip |dHAP4 Powerpoint Slide]]
+Individual dataset:[[Media: BIOL367_Fall2017_Dahlquist-microarray-data-master_20171017BVH1.zip | dHAP4 Excel File]]
 ==Notebook==
 # Create a new worksheet, naming it "dHAP4_ANOVA".

@@ Line 1: / Line 1: @@
-==Spreadsheet==
+==Zip Files==
 [[Media: BIOL367_Fall2017_Dahlquist-microarray-data-master_20171017BVH1.zip | dHAP4]]
 ==Notebook==

@@ Line 111: / Line 111: @@
 * We will compare the numbers we get between the wild type strain and the other strains studied, organized as a table.  Use this [[Media:BIOL367_F17_sample_p-value_slide.pptx | sample PowerPoint slide]] to see how your table should be formatted. Upload your slide to the wiki.
 ** Note that since the wild type data is being analyzed by one of the groups in the class, it will be sufficient for this week to supply just the data for your strain.  We will do the comparison with wild type at a later date.
 * Comparing results with known data:  the expression of the gene ''NSR1'' (ID: YGR159C)is known to be induced by cold shock. '''''Find ''NSR1'' in your dataset.  What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values?  What is its average Log fold change at each of the timepoints in the experiment?'''''  Note that the average Log fold change is what we called "dHAP4_AvgLogFC_(TIME)" in step 3 of the ANOVA analysis. Does ''NSR1'' change expression due to cold shock in this experiment?
 **Unadjusted: 0.016363683
 **Bonferroni-corrected:101.274831
@@ Line 120: / Line 121: @@
 **<code>dHAP4_AvgLogFC_t90</code>:-1.1005673
 **<code>dHAP4_AvgLogFC_t120</code>:-1.797694577
 * For fun, find "your favorite gene" (from your web page) in the dataset.  '''''What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values?  What is its average Log fold change at each of the timepoints in the experiment?'''''  Does your favorite gene change expression due to cold shock in this experiment?
 **Unadjusted: 0.187431493
 **Bonferroni-corrected:1160.01351
@@ Line 129: / Line 132: @@
 **<code>dHAP4_AvgLogFC_t90</code>:-0.63201057
 **<code>dHAP4_AvgLogFC_t120</code>:1.038421855
 ==Acknowledgments==

@@ Line 111: / Line 111: @@
 * We will compare the numbers we get between the wild type strain and the other strains studied, organized as a table.  Use this [[Media:BIOL367_F17_sample_p-value_slide.pptx | sample PowerPoint slide]] to see how your table should be formatted. Upload your slide to the wiki.
 ** Note that since the wild type data is being analyzed by one of the groups in the class, it will be sufficient for this week to supply just the data for your strain.  We will do the comparison with wild type at a later date.
-* Comparing results with known data:  the expression of the gene ''NSR1'' (ID: YGR159C)is known to be induced by cold shock. '''''Find ''NSR1'' in your dataset.  What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values?  What is its average Log fold change at each of the timepoints in the experiment?'''''  Note that the average Log fold change is what we called "STRAIN)_AvgLogFC_(TIME)" in step 3 of the ANOVA analysis. Does ''NSR1'' change expression due to cold shock in this experiment?
+* Comparing results with known data:  the expression of the gene ''NSR1'' (ID: YGR159C)is known to be induced by cold shock. '''''Find ''NSR1'' in your dataset.  What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values?  What is its average Log fold change at each of the timepoints in the experiment?'''''  Note that the average Log fold change is what we called "dHAP4_AvgLogFC_(TIME)" in step 3 of the ANOVA analysis. Does ''NSR1'' change expression due to cold shock in this experiment?
 * For fun, find "your favorite gene" (from your web page) in the dataset.  '''''What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values?  What is its average Log fold change at each of the timepoints in the experiment?'''''  Does your favorite gene change expression due to cold shock in this experiment?
 ==Acknowledgments==

@@ Line 62: / Line 62: @@
 #* Click on the drop-down arrow on your dHAP4_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.
 #* 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.
-#*For this particular section 4953 genes should meet this criteria.
+#*For this particular section 2558 genes should meet this criteria.
 ==== Calculate the Bonferroni and p value Correction ====
@@ Line 91: / Line 91: @@
 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.
-* Go to your (STRAIN)_ANOVA worksheet.
+* Go to the dHAP4_ANOVA worksheet.
 * Select row 1 (the row with your column headers) and select the menu item Data > Filter > Autofilter (The funnel icon on the Data tab).  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.
 * Click on the drop-down arrow for the unadjusted p value.  Set a criterion that will filter your 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)?'''''
 ** '''''How many genes have p < 0.01? and what is the percentage (out of 6189)?'''''
 ** '''''How many genes have p < 0.001? and what is the percentage (out of 6189)?'''''
 ** '''''How many genes have p < 0.0001? and what is the percentage (out of 6189)?'''''
 * 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.
 * We 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)?'''''
 ** '''''How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?'''''
 * In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant".  Instead, it is a moveable confidence level.  If we want to be very confident of our data, use a small p value cut-off.  If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off.
 * We will compare the numbers we get between the wild type strain and the other strains studied, organized as a table.  Use this [[Media:BIOL367_F17_sample_p-value_slide.pptx | sample PowerPoint slide]] to see how your table should be formatted. Upload your slide to the wiki.

@@ Line 74: / Line 74: @@
 ==== Calculate the Benjamini & Hochberg p value Correction ====
-# Insert a new worksheet named "(STRAIN)_ANOVA_B-H".
+# Insert a new worksheet named "dHAP4_ANOVA_B-H".
-# Copy and paste the "MasterIndex", "ID", and "Standard Name" columns from your previous worksheet into the first two columns of the new worksheet.
+# Copy and paste the "MasterIndex", "ID", and "Standard Name" columns from your 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.
 # 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.
 # 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.
-# Now you can calculate the Benjamini and Hochberg p value correction. Type (STRAIN)_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.
+# Now you can calculate the Benjamini and Hochberg p value correction. Type dHAP4_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.
-# Type "STRAIN_B-H_p-value" into cell G1.
+# Type "dHAP4_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.
 # Select columns A through G.  Now sort them by your 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.
-* '''''Zip and upload the .xlsx file that you have just created to the wiki.'''''
+*'''''Current File is uploaded on Bhamilton18 Week 8 assignment page.'''''
 ==== Sanity Check: Number of genes significantly changed ====

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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
-{Bhamilton18}
+{{Bhamilton18}}
 [[Category: Journal Entry]]