BIOL398-01/S11:Week 11 - Revision history

Kam D. Dahlquist: /* Sanity Check: Number of genes significantly changed */ fixed number of t tests

2011-04-08T00:13:50Z

Sanity Check: Number of genes significantly changed: fixed number of t tests

←Older revision		Revision as of 00:13, 8 April 2011
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	*** Answer these questions for each timepoint in your dataset.		*** Answer these questions for each timepoint in your dataset.
	* 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 less than 5% of the time.		* 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 less than 5% of the time.
-	* We have just performed ~~5221~~ T tests for significance. Another way to state what we are seeing with p < 0.05 is that we would expect to see this magnitude of a gene expression change in about 5% of our T tests, or 309 times. Since we have more than 261 genes that pass this cut off, we know that some genes are significantly changed. However, we don't know ''which'' ones.	+	* We have just performed 6189 T tests for significance. Another way to state what we are seeing with p < 0.05 is that we would expect to see this magnitude of a gene expression change in about 5% of our T tests, or 309 times. Since we have more than 261 genes that pass this cut off, we know that some genes are significantly changed. However, we don't know ''which'' ones.
	** There is a simple correction that can be made to the p values to increase the stringency called the Bonferroni correction. To perform this correction, multiply the p value by the number of statistical tests performed (in our case 6189) and see whether any of the p values are still less than 0.05.		** There is a simple correction that can be made to the p values to increase the stringency called the Bonferroni correction. To perform this correction, multiply the p value by the number of statistical tests performed (in our case 6189) and see whether any of the p values are still less than 0.05.
	*** '''''Perform this correction and determine whether and how many of the genes are still significantly changed at p < 0.05 after the Bonferroni correction.'''''		*** '''''Perform this correction and determine whether and how many of the genes are still significantly changed at p < 0.05 after the Bonferroni correction.'''''

Carmen E. Castaneda: changed the days of the week to correspond with new due date

2011-04-04T04:28:35Z

changed the days of the week to correspond with new due date

←Older revision		Revision as of 04:28, 4 April 2011
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	<div style="padding: 10px; width: 720px; border: 5px solid #000000;">		<div style="padding: 10px; width: 720px; border: 5px solid #000000;">

-	'''This journal entry is due on Tuesday, April 5 at midnight PDT''' ''(~~Wednesday~~ night/~~Thursday~~ morning)''. NOTE new due date and that the server records the time as Eastern Daylight Time (EDT). Therefore, midnight will register as 03:00.	+	'''This journal entry is due on Tuesday, April 5 at midnight PDT''' ''(Monday night/Tuesday morning)''. NOTE new due date and that the server records the time as Eastern Daylight Time (EDT). Therefore, midnight will register as 03:00.

	== Individual Journal Assignment ==		== Individual Journal Assignment ==

Kam D. Dahlquist: /* Background */ added group assignments

2011-03-31T21:39:13Z

Background: added group assignments

←Older revision		Revision as of 21:39, 31 March 2011
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	You will download your assigned Excel spreadsheet from LionShare. Because the Dahlquist Lab data is unpublished, please do not post it on this public wiki. Instead, keep the file(s) on LionShare, which is protected by a password.		You will download your assigned Excel spreadsheet from LionShare. Because the Dahlquist Lab data is unpublished, please do not post it on this public wiki. Instead, keep the file(s) on LionShare, which is protected by a password.
		+
		+	* '''Groups:'''
		+	** Schade et al. (2004) data: Carmen, James
		+	** Dahlquist lab wild type data: Sarah, Nick
		+	** Dahlquist lab ''Δgln3'' data: Alondra

	==== Experimental Design ====		==== Experimental Design ====

Kam D. Dahlquist: /* Normalize the ratios for a set of slides in an experiment */ changed $ reference

2011-03-29T17:42:32Z

Normalize the ratios for a set of slides in an experiment: changed $ reference

←Older revision		Revision as of 17:42, 29 March 2011
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	* Insert a new column to the right of each data column and label the top of the column as with the same name as the column to the left, but adding "_sc" for scaled and centered to the name. For example, "wt_LogFC_t15-1_sc"		* Insert a new column to the right of each data column and label the top of the column as with the same name as the column to the left, but adding "_sc" for scaled and centered to the name. For example, "wt_LogFC_t15-1_sc"
	* In cell D4, type the following equation:		* In cell D4, type the following equation:
-	=(C4-$C$2)/$C$3	+	=(C4-C$2)/C$3
-	In this case, we want the data in cell C4 to have the average subtracted from it (cell C2) and be divided by the standard deviation (cell C3). We use the dollar sign symbols ~~surrounding~~ the ~~"C"~~ to tell Excel to always reference that ~~cell~~ in the equation, even though we will paste it for the entire column. '''''Why is this important?'''''	+	In this case, we want the data in cell C4 to have the average subtracted from it (cell C2) and be divided by the standard deviation (cell C3). We use the dollar sign symbols in front of the number to tell Excel to always reference that row in the equation, even though we will paste it for the entire column. '''''Why is this important?'''''
	* Copy and paste this equation into the entire column.		* Copy and paste this equation into the entire column.
-	* Repeat the scaling and centering equation for each of the columns of data. Be sure that your equation is correct for the column you are calculating.	+	* Repeat the scaling and centering equation for each of the columns of data. You can copy and paste the formula above, but be sure that your equation is correct for the column you are calculating.

	==== Perform statistical analysis on the ratios ====		==== Perform statistical analysis on the ratios ====

Kam D. Dahlquist: /* Reflection */ added a reflection question

2011-03-29T16:20:27Z

Reflection: added a reflection question

←Older revision		Revision as of 16:20, 29 March 2011
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	# What aspect of this assignment was the most challenging for you?		# What aspect of this assignment was the most challenging for you?
	# What (yet) do you not understand?		# What (yet) do you not understand?
		+	# Does "crunching" the data yourself help you to understand microarray experiments in general and the Schade paper in particular? Why or why not?

	</div>		</div>

Kam D. Dahlquist: /* Experimental Design */ changed number to bullet

2011-03-29T15:55:26Z

Experimental Design: changed number to bullet

←Older revision		Revision as of 15:55, 29 March 2011
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	For the Dahlquist data (both wild type and ''Δgln3''), 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). Note that the experimental designs are different.		For the Dahlquist data (both wild type and ''Δgln3''), 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). Note that the experimental designs are different.

-	~~# ''~~'''Begin by recording in your wiki the number of replicates for each time point in your data. For the group assigned to the Schade data, compare the number of replicates with what is stated in the Materials and Methods for the paper. Is it the same? If not, how is it different?'''''	+	* '''Begin by recording in your wiki the number of replicates for each time point in your data. For the group assigned to the Schade data, compare the number of replicates with what is stated in the Materials and Methods for the paper. Is it the same? If not, how is it different?'''

	==== Normalize the ratios for a set of slides in an experiment ====		==== Normalize the ratios for a set of slides in an experiment ====

Kam D. Dahlquist: /* Reflection */ pasted in reflection questions

2011-03-29T15:54:41Z

Reflection: pasted in reflection questions

←Older revision		Revision as of 15:54, 29 March 2011
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	=== Reflection ===		=== Reflection ===

-	~~<!--~~	+	# What aspect of this assignment came most easily to you?
-	# ~~Overall, do~~ you ~~think this paper was clearly written? Why or why not~~?	+	# What aspect of this assignment was the most challenging for you?
-	# ~~Based on what is written in~~ the ~~methods section, do you think~~ you ~~could reproduce their experiments and data analysis~~?	+	# What (yet) do you not understand?
-	# What ~~else would~~ you ~~like to know about their methods, results, and future directions~~?	+
-	~~-->~~	+

	</div>		</div>

Kam D. Dahlquist: /* Sanity Check: Number of genes significantly changed */ fleshed out instructions

2011-03-29T15:53:43Z

Sanity Check: Number of genes significantly changed: fleshed out instructions

←Older revision		Revision as of 15:53, 29 March 2011
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	* 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.		* 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.
	* '''What criteria did Schade ''et al''. (2004) use to determine a significant gene expression change? How does it compare to our method?'''		* '''What criteria did Schade ''et al''. (2004) use to determine a significant gene expression change? How does it compare to our method?'''
		+	* The expression of the gene ''NSR1'' (ID: YGR159C)is known to be induced by cold shock. (Recall that it is specifically mentioned in the Schade ''et al''. (2004) paper.) '''Find ''NSR1'' in your dataset. Is it's expression significantly changed at any timepoint? Record the average fold change and p value for ''NSR1'' for each timepoint in your dataset.'''
		+	* '''Which gene has the smallest p value in your dataset (at any timepoint)? You can find this by sorting your data based on p value (''but be careful that you don't cause a mismatch in the rows of your data!'') Look up the function of this gene at the [http://www.yeastgenome.org Saccharomyces Genome Database] and record it in your notebook. Why do you think the cell is changing this gene's expression upon cold shock?'''

	== Shared Journal Assignment ==		== Shared Journal Assignment ==

Kam D. Dahlquist: /* Perform statistical analysis on the ratios */ added Bonferroni correction

2011-03-29T15:48:05Z

Perform statistical analysis on the ratios: added Bonferroni correction

←Older revision		Revision as of 15:48, 29 March 2011
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	* Select all of the columns containing T statistics or P values. Select the menu item Format > Cells. Under the number tab, select 4 decimal places. Click OK.		* Select all of the columns containing T statistics or P values. Select the menu item Format > Cells. Under the number tab, select 4 decimal places. Click OK.
	* '''''Upload the .xls file that you have just created to LionShare.''''' Give Dr. Dahlquist (username kdahlqui) and Dr. Fitzpatrick (username bfitzpatrick) permission to download your file. Send an e-mail to each of us with the link to the file.		* '''''Upload the .xls file that you have just created to LionShare.''''' Give Dr. Dahlquist (username kdahlqui) and Dr. Fitzpatrick (username bfitzpatrick) permission to download your file. Send an e-mail to each of us with the link to the file.
		+
		+	==== Sanity Check: Number of genes significantly changed ====
		+
		+	Before we move on to the biological analysis of the data, we want to perform a 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 and also compare our data analysis with the published results of Schade ''et al''. (2004).
		+
		+	* Open your spreadsheet and go to the "final" worksheet.
		+	* Click on cell A1 and select the menu item Data > Filter > Autofilter. 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 one of your "Pval" columns. Select "Custom". 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.
		+	** '''How many genes have p value < 0.05?
		+	** What about p < 0.01?
		+	** What about p < 0.001?
		+	** What about p < 0.0001?'''
		+	*** Answer these questions for each timepoint in your dataset.
		+	* 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 less than 5% of the time.
		+	* We have just performed 5221 T tests for significance. Another way to state what we are seeing with p < 0.05 is that we would expect to see this magnitude of a gene expression change in about 5% of our T tests, or 309 times. Since we have more than 261 genes that pass this cut off, we know that some genes are significantly changed. However, we don't know ''which'' ones.
		+	** There is a simple correction that can be made to the p values to increase the stringency called the Bonferroni correction. To perform this correction, multiply the p value by the number of statistical tests performed (in our case 6189) and see whether any of the p values are still less than 0.05.
		+	*** '''''Perform this correction and determine whether and how many of the genes are still significantly changed at p < 0.05 after the Bonferroni correction.'''''
		+	* The "AvgLogFC" tells us the magnitude of the gene expression change and in which direction. Positive values are increases relative to the control; negative values are decreases relative to the control. For the timepoint that had the '''''greatest number''''' of genes significantly changed at p < 0.05, answer the following:
		+	** '''Keeping the "Pval" filter at p < 0.05, filter the "AvgLogFC" column to show all genes with an average log fold change greater than zero. How many meet these two criteria?
		+	** Keeping the "Pval" filter at p < 0.05, filter the "AvgLogFC" column to show all genes with an average log fold change less than zero. How many meet these two criteria?
		+	** Keeping the "Pval" filter at p < 0.05, How many have an average log fold change of > 0.25 and p < 0.05?
		+	** How many have an average log fold change of < -0.25 and p < 0.05? (These are more realistic values for the fold change cut-offs because it represents about a 20% fold change which is about the level of detection of this technology.)'''
		+	* 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.
		+	* '''What criteria did Schade ''et al''. (2004) use to determine a significant gene expression change? How does it compare to our method?'''

	== Shared Journal Assignment ==		== Shared Journal Assignment ==

Kam D. Dahlquist: /* Perform statistical analysis on the ratios */ format and upload

2011-03-29T15:35:38Z

Perform statistical analysis on the ratios: format and upload

←Older revision		Revision as of 15:35, 29 March 2011
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	* Repeat the scaling and centering equation for each of the columns of data. Be sure that your equation is correct for the column you are calculating.		* Repeat the scaling and centering equation for each of the columns of data. Be sure that your equation is correct for the column you are calculating.

-	== Perform statistical analysis on the ratios ==	+	==== Perform statistical analysis on the ratios ====

	We are going to perform this step on the scaled and centered data you produced in the previous step.		We are going to perform this step on the scaled and centered data you produced in the previous step.
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	=TDIST(ABS(AE2),3,2)		=TDIST(ABS(AE2),3,2)
	The number of degrees of freedom is the number of replicates minus one, so in our case there are 3 degrees of freedom. Copy the equation and paste it into all rows in that column.		The number of degrees of freedom is the number of replicates minus one, so in our case there are 3 degrees of freedom. Copy the equation and paste it into all rows in that column.
-	* Insert a new worksheet and name it "~~forGenMAPP~~".	+	* Insert a new worksheet and name it "final".
	* Go back to the "statistics" worksheet and Select All and Copy.		* Go back to the "statistics" worksheet and Select All and Copy.
-	* Go to your new sheet and click on cell A1 and select Paste Special, click on the Values radio button, and click OK. ~~We will now format this~~ worksheet ~~for import into GenMAPP~~.	+	* Go to your new sheet and click on cell A1 and select Paste Special, click on the Values radio button, and click OK. This is your final worksheet from which we will perform biological analysis of the data.
-	* Select ~~Columns B through Q (~~all the ~~fold changes)~~. Select the menu item Format > Cells. Under the number tab, select 2 decimal places. Click OK.	+	* Select all of the columns containing Fold Changes. Select the menu item Format > Cells. Under the number tab, select 2 decimal places. Click OK.
-	* Select ~~Columns R and S~~. Select the menu item Format > Cells. Under the number tab, select 4 decimal places. Click OK.	+	* Select all of the columns containing T statistics or P values. Select the menu item Format > Cells. Under the number tab, select 4 decimal places. Click OK.
-	* ~~Select Columns N through S and Cut. Select Column B by left-clicking on~~ the ~~"B" at the top of the column~~. ~~Then right-click on the Column B header and select "Insert Cut Cells". This will insert the data without writing over your existing columns.~~	+	* '''''Upload the .xls file that you have just created to LionShare.''''' Give Dr. Dahlquist (username kdahlqui) and Dr. Fitzpatrick (username bfitzpatrick) permission to download your file. Send an e-mail to each of us with the link to the file.
-	* Delete Rows 2 and 3 where it says "Average" and "StDev" so that ~~your data rows with gene IDs are immediately below the header row 1.~~	+
-	* Insert a column to ~~the right of the "ID" column~~. ~~Type the header "SystemCode" into the top cell of this column~~. ~~Fill the entire column~~ (~~each cell~~) ~~with the letter "N"~~.	+
-	* Select the menu item File > Save As, and choose "Text (~~Tab-delimited~~) ~~(.txt)" from the~~ file ~~type drop-down menu~~. ~~Excel will make you click through a couple~~ of ~~warnings because it doesn't like you going all independent and choosing a different file type than~~ the ~~native .xls. This is OK. Your new .txt file is now ready for import into GenMAPP. But before we do that, we want~~ to ~~know a few things about our data as shown in~~ the ~~next section.~~	+
-	** Upload both the .xls and .txt files that you have just created to your journal page in the class wiki. Make sure that your file name is distinct from your other classmates so that nobody overwrites anyone else's file.	+

	== Shared Journal Assignment ==		== Shared Journal Assignment ==