Week 8 - Revision history

Hhinsch: realized i was editing the wrong page so changed back to original

2017-10-24T07:03:05Z

realized i was editing the wrong page so changed back to original

Hhinsch: corrected more strains to dHMO1

2017-10-24T06:56:55Z

corrected more strains to dHMO1

Hhinsch: fixed more strain to dhmo1's

2017-10-24T06:54:58Z

fixed more strain to dhmo1's

Hhinsch: noticed i missed some strain to dhmo1 corrections

2017-10-24T06:52:37Z

noticed i missed some strain to dhmo1 corrections

Kdahlquist: /* Experimental Design and Getting Ready */ deleted mention of comparison because confusing

2017-10-23T23:01:02Z

‎Experimental Design and Getting Ready: deleted mention of comparison because confusing

Kdahlquist: /* Part 1: Statistical Analysis Part 1 */ delete number of data points note because "COUNTA" function replaced it

2017-10-19T22:24:42Z

‎Part 1: Statistical Analysis Part 1: delete number of data points note because "COUNTA" function replaced it

Kdahlquist: removed under construction flag

2017-10-19T19:30:28Z

removed under construction flag

Kdahlquist: /* Sanity Check: Number of genes significantly changed */ added questions

2017-10-19T19:30:02Z

‎Sanity Check: Number of genes significantly changed: added questions

Kdahlquist: /* Sanity Check: Number of genes significantly changed */ add finding favorite gene

2017-10-19T19:28:48Z

‎Sanity Check: Number of genes significantly changed: add finding favorite gene

Kdahlquist: /* Sanity Check: Number of genes significantly changed */ note about PowerPoint

2017-10-19T19:26:55Z

‎Sanity Check: Number of genes significantly changed: note about PowerPoint

@@ Line 90: / Line 90: @@
 The purpose of the witin-stain ANOVA test is to determine if any genes had a gene expression change that was significantly different than zero at '''''any''''' timepoint.
-# Create a new worksheet, naming it either "dHMO1_ANOVA" as appropriate.  For example, you might call yours "wt_ANOVA" or "dHAP4_ANOVA"
+# Create a new worksheet, naming it either "(STRAIN)_ANOVA" as appropriate.  For example, you might call yours "wt_ANOVA" or "dHAP4_ANOVA"
 # Copy the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for your strain and paste it into your new worksheet.  Copy the columns containing the data for your strain and paste it into your new worksheet.
-# At the top of the first column to the right of your data, create five column headers of the form dHMO1_AvgLogFC_(TIME) where dHMO1 is your strain designation and (TIME) is 15, 30, etc.
+# At the top of the first column to the right of your data, create five column headers of the form (STRAIN)_AvgLogFC_(TIME) where dHMO1 is your strain designation and (TIME) is 15, 30, etc.
-# In the cell below the dHMO1_AvgLogFC_t15 header, type <code>=AVERAGE(</code>
+# In the cell below the (STRAIN)_AvgLogFC_t15 header, type <code>=AVERAGE(</code>
 # Then highlight all the data in row 2 associated with dHMO1 and t15, press the closing paren key (shift 0),and press the "enter" key.
 # This cell now contains 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.
 # Repeat steps (4) through (8) with the t30, t60, t90, and the t120 data.
-# Now in the first empty column to the right of the dHMO1_AvgLogFC_t120 calculation, create the column header dHMO1_ss_HO.
+# Now in the first empty column to the right of the (STRAIN)_AvgLogFC_t120 calculation, create the column header (STRAIN)_ss_HO.
 # In the first cell below this header, type <code>=SUMSQ(</code>
 # Highlight all the LogFC data in row 2 for your dHMO1 (but not the AvgLogFC), press the closing paren key (shift 0),and press the "enter" key.
-# In the next empty column to the right of dHMO1_ss_HO, create the column headers dHMO1_ss_(TIME) as in (3).
+# In the next empty column to the right of (STRAIN)_ss_HO, create the column headers (STRAIN)_ss_(TIME) as in (3).
 # Make a note of how many data points you have at each time point for your strain.  For most of the strains, it will be 4, but for dHAP4 t90 or t120, it will be "3", and for the wild type it will be "4" or "5".  Count carefully. Also, make a note of the total number of data points. Again, for most strains, this will be 20, but for example, dHAP4, this number will be 18, and for wt it should be 23 (double-check).
-# In the first cell below the header dHMO1_ss_t15, type <code>=SUMSQ(<range of cells for logFC_t15>)-COUNTA(<range of cells for logFC_t15>)*<AvgLogFC_t15>^2</code> and hit enter.
+# In the first cell below the header (STRAIN)_ss_t15, type <code>=SUMSQ(<range of cells for logFC_t15>)-COUNTA(<range of cells for logFC_t15>)*<AvgLogFC_t15>^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).
 #* The phrase <range of cells for logFC_t15> should be replaced by the data range associated with t15.
@@ Line 109: / Line 109: @@
 #* Upon completion of this single computation, use 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.
-# In the first column to the right of dHMO1_ss_t120, create the column header dHMO1_SS_full.
+# In the first column to the right of (STRAIN)_ss_t120, create the column header dHMO1_SS_full.
 # In the first row below this header, type <code>=sum(<range of cells containing "ss" for each timepoint>)</code> and hit enter.
-# In the next two columns to the right, create the headers dHMO1_Fstat and dHMO1_p-value.
+# In the next two columns to the right, create the headers (STRAIN)_Fstat and dHMO1_p-value.
 # Recall the number of data points from (13): call that total n.
-# In the first cell of the dHMO1_Fstat column, type <code>=((n-5)/5)*(<dHMO1_ss_HO>-<dHMO1_SS_full>)/<dHMO1_SS_full></code> and hit enter.
+# In the first cell of the (STRAIN)_Fstat column, type <code>=((n-5)/5)*(<(STRAIN)_ss_HO>-<dHMO1_SS_full>)/<dHMO1_SS_full></code> and hit enter.
 #* Don't actually type the n but instead use the number from (13). Also note that "5" is the number of timepoints and the dSWI4 strain has 4 timepoints (it is missing t15).
-#* Replace the phrase dHMO1_ss_HO with the cell designation.
+#* Replace the phrase (STRAIN)_ss_HO with the cell designation.
 #* Replace the phrase <dHMO1_SS_full> with the cell designation.
 #* Copy to the whole column.
-# In the first cell below the dHMO1_p-value header, type <code>=FDIST(<dHMO1_Fstat>,5,n-5)</code> replacing the phrase <dHMO1_Fstat> with the cell designation and the "n" as in (13) with the number of data points total. (Again, note that the number of timepoints is actually "4" for the dSWI4 strain).  Copy to the whole column.
+# In the first cell below the dHMO1_p-value header, type <code>=FDIST(<(STRAIN)_Fstat>,5,n-5)</code> replacing the phrase <(STRAIN)_Fstat> with the cell designation and the "n" as in (13) with the number of data points total. (Again, note that the number of timepoints is actually "4" for the dSWI4 strain).  Copy 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.
 #*  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.
@@ Line 126: / Line 126: @@
 ==== 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, dHMO1_Bonferroni_p-value.
+# 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, (STRAIN)_Bonferroni_p-value.
 # Type the equation <code>=<dHMO1_p-value>*6189</code>, Upon completion of this single computation, use 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 dHMO1_Bonferroni_p-value header: <code>=IF(dHMO1_Bonferroni_p-value>1,1,dHMO1_Bonferroni_p-value)</code>, where "dHMO1_Bonferroni_p-value" refers to the cell in which the first Bonferroni p value computation was made.  Use 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 (STRAIN)_Bonferroni_p-value header: <code>=IF((STRAIN)_Bonferroni_p-value>1,1,(STRAIN)_Bonferroni_p-value)</code>, where "(STRAIN)_Bonferroni_p-value" refers to the cell in which the first Bonferroni p value computation was made.  Use the Step (10) trick to copy the formula throughout the column.
 ==== Calculate the Benjamini & Hochberg p value Correction ====

@@ Line 126: / Line 126: @@
 ==== 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, (STRAIN)_Bonferroni_p-value.
+# 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, dHMO1_Bonferroni_p-value.
-# Type the equation <code>=<(STRAIN)_p-value>*6189</code>, Upon completion of this single computation, use the Step (10) trick to copy the formula throughout the column.
+# Type the equation <code>=<dHMO1_p-value>*6189</code>, Upon completion of this single computation, use 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 (STRAIN)_Bonferroni_p-value header: <code>=IF(STRAIN_Bonferroni_p-value>1,1,STRAIN_Bonferroni_p-value)</code>, where "STRAIN_Bonferroni_p-value" refers to the cell in which the first Bonferroni p value computation was made.  Use 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 dHMO1_Bonferroni_p-value header: <code>=IF(dHMO1_Bonferroni_p-value>1,1,dHMO1_Bonferroni_p-value)</code>, where "dHMO1_Bonferroni_p-value" refers to the cell in which the first Bonferroni p value computation was made.  Use the Step (10) trick to copy the formula throughout the column.
 ==== Calculate the Benjamini & Hochberg p value Correction ====

@@ Line 90: / Line 90: @@
 The purpose of the witin-stain ANOVA test is to determine if any genes had a gene expression change that was significantly different than zero at '''''any''''' timepoint.
-# Create a new worksheet, naming it either "(STRAIN)_ANOVA" as appropriate.  For example, you might call yours "wt_ANOVA" or "dHAP4_ANOVA"
+# Create a new worksheet, naming it either "dHMO1_ANOVA" as appropriate.  For example, you might call yours "wt_ANOVA" or "dHAP4_ANOVA"
 # Copy the first three columns containing the "MasterIndex", "ID", and "Standard Name" from the "Master_Sheet" worksheet for your strain and paste it into your new worksheet.  Copy the columns containing the data for your strain and paste it into your new worksheet.
-# At the top of the first column to the right of your data, create five column headers of the form (STRAIN)_AvgLogFC_(TIME) where (STRAIN) is your strain designation and (TIME) is 15, 30, etc.
+# At the top of the first column to the right of your data, create five column headers of the form dHMO1_AvgLogFC_(TIME) where dHMO1 is your strain designation and (TIME) is 15, 30, etc.
-# In the cell below the (STRAIN)_AvgLogFC_t15 header, type <code>=AVERAGE(</code>
+# In the cell below the dHMO1_AvgLogFC_t15 header, type <code>=AVERAGE(</code>
-# Then highlight all the data in row 2 associated with (STRAIN) and t15, press the closing paren key (shift 0),and press the "enter" key.
+# Then highlight all the data in row 2 associated with dHMO1 and t15, press the closing paren key (shift 0),and press the "enter" key.
 # This cell now contains 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.
 # Repeat steps (4) through (8) with the t30, t60, t90, and the t120 data.
-# Now in the first empty column to the right of the (STRAIN)_AvgLogFC_t120 calculation, create the column header (STRAIN)_ss_HO.
+# Now in the first empty column to the right of the dHMO1_AvgLogFC_t120 calculation, create the column header dHMO1_ss_HO.
 # In the first cell below this header, type <code>=SUMSQ(</code>
-# Highlight all the LogFC data in row 2 for your (STRAIN) (but not the AvgLogFC), press the closing paren key (shift 0),and press the "enter" key.
+# Highlight all the LogFC data in row 2 for your dHMO1 (but not the AvgLogFC), press the closing paren key (shift 0),and press the "enter" key.
-# In the next empty column to the right of (STRAIN)_ss_HO, create the column headers (STRAIN)_ss_(TIME) as in (3).
+# In the next empty column to the right of dHMO1_ss_HO, create the column headers dHMO1_ss_(TIME) as in (3).
 # Make a note of how many data points you have at each time point for your strain.  For most of the strains, it will be 4, but for dHAP4 t90 or t120, it will be "3", and for the wild type it will be "4" or "5".  Count carefully. Also, make a note of the total number of data points. Again, for most strains, this will be 20, but for example, dHAP4, this number will be 18, and for wt it should be 23 (double-check).
-# In the first cell below the header (STRAIN)_ss_t15, type <code>=SUMSQ(<range of cells for logFC_t15>)-COUNTA(<range of cells for logFC_t15>)*<AvgLogFC_t15>^2</code> and hit enter.
+# In the first cell below the header dHMO1_ss_t15, type <code>=SUMSQ(<range of cells for logFC_t15>)-COUNTA(<range of cells for logFC_t15>)*<AvgLogFC_t15>^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).
 #* The phrase <range of cells for logFC_t15> should be replaced by the data range associated with t15.

@@ Line 109: / Line 109: @@
 #* Upon completion of this single computation, use 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.
-# In the first column to the right of (STRAIN)_ss_t120, create the column header (STRAIN)_SS_full.
+# In the first column to the right of dHMO1_ss_t120, create the column header dHMO1_SS_full.
 # In the first row below this header, type <code>=sum(<range of cells containing "ss" for each timepoint>)</code> and hit enter.
-# In the next two columns to the right, create the headers (STRAIN)_Fstat and (STRAIN)_p-value.
+# In the next two columns to the right, create the headers dHMO1_Fstat and dHMO1_p-value.
 # Recall the number of data points from (13): call that total n.
-# In the first cell of the (STRAIN)_Fstat column, type <code>=((n-5)/5)*(<(STRAIN)_ss_HO>-<(STRAIN)_SS_full>)/<(STRAIN)_SS_full></code> and hit enter.
+# In the first cell of the dHMO1_Fstat column, type <code>=((n-5)/5)*(<dHMO1_ss_HO>-<dHMO1_SS_full>)/<dHMO1_SS_full></code> and hit enter.
 #* Don't actually type the n but instead use the number from (13). Also note that "5" is the number of timepoints and the dSWI4 strain has 4 timepoints (it is missing t15).
-#* Replace the phrase (STRAIN)_ss_HO with the cell designation.
+#* Replace the phrase dHMO1_ss_HO with the cell designation.
-#* Replace the phrase <(STRAIN)_SS_full> with the cell designation.
+#* Replace the phrase <dHMO1_SS_full> with the cell designation.
 #* Copy to the whole column.
-# In the first cell below the (STRAIN)_p-value header, type <code>=FDIST(<(STRAIN)_Fstat>,5,n-5)</code> replacing the phrase <(STRAIN)_Fstat> with the cell designation and the "n" as in (13) with the number of data points total. (Again, note that the number of timepoints is actually "4" for the dSWI4 strain).  Copy to the whole column.
+# In the first cell below the dHMO1_p-value header, type <code>=FDIST(<dHMO1_Fstat>,5,n-5)</code> replacing the phrase <dHMO1_Fstat> with the cell designation and the "n" as in (13) with the number of data points total. (Again, note that the number of timepoints is actually "4" for the dSWI4 strain).  Copy 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.
 #*  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.
-#* Click on the drop-down arrow on your (STRAIN)_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.
+#* Click on the drop-down arrow on your dHMO1_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.

@@ Line 77: / Line 77: @@
 ** Each of the column headings from the data begin with the experiment name ("wt" for wild type ''S. cerevisiae'' data, "dASH1" for the ''Δash1'' data, etc.).  "LogFC" stands for "Log<sub>2</sub> Fold Change" which is the Log<sub>2</sub> red/green ratio.  The timepoints are designated as "t" followed by a number in minutes.  Replicates are numbered as "-0", "-1", "-2", etc. after the timepoint.
 *** The timepoints are t15, t30, t60 (cold shock at 13°C) and t90 and t120 (cold shock at 13°C followed by 30 or 60 minutes of recovery at 30°C).
-* '''''Begin by recording in your wiki, the strain comparison and individual dataset that you will analyze, the filename, the number of replicates for each strain and each time point in your data.'''''
+* '''''Begin by recording in your wiki, the strain that you will analyze, the filename, the number of replicates for each strain and each time point in your data.'''''
 * '''NOTE: before beginning any analysis, immediately change the filename so that it contains your initials to distinguish it from other students' work.'''
 * The first thing you will do is to delete the data columns of the strains that you are not analyzing so that your file contains only the strain's data that you will be working with (to make a smaller and less confusing file).

← Older revision		Revision as of 19:30, 19 October 2017
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−	~~{{Under_Construction}}~~
−
	'''This journal entry is due on Tuesday, October 24, at 12:01 AM PDT.'''		'''This journal entry is due on Tuesday, October 24, at 12:01 AM PDT.'''

@@ Line 166: / Line 166: @@
 * 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.
+* 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?
-* 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?'''''
+* 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?
 ==== Summary Paragraph ====