BIOL398-01/S10:Sample Microarray Analysis Vibrio cholerae - Revision history

Kam D. Dahlquist: /* Normalize the log ratios for the set of slides in the experiment */ adjusted dollar sign instructions

Kam D. Dahlquist — Wed, 13 Jun 2012 16:30:53 GMT

Normalize the log ratios for the set of slides in the experiment: adjusted dollar sign instructions

←Older revision		Revision as of 16:30, 13 June 2012
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	* Insert a new column to the right of each data column and label the top of the column as follows: A1_scaled_centered, A2_scaled_centered, etc.		* Insert a new column to the right of each data column and label the top of the column as follows: A1_scaled_centered, A2_scaled_centered, etc.
	* In cell C4, type the following equation:		* In cell C4, type the following equation:
-	=(B4-$B$2)/$B$3	+	=(B4-B$2)/B$3
-	In this case, we want the data in cell B4 to have the average subtracted from it (cell B2) and be divided by the standard deviation (cell B3). We use the dollar sign symbols ~~surrounding~~ the "B" 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 B4 to have the average subtracted from it (cell B2) and be divided by the standard deviation (cell B3). We use the dollar sign symbols in front of the "2" and "3" 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. Be sure that your equation is correct for the column you are calculating.

Kam D. Dahlquist: /* Sanity Check: Number of genes significantly changed */ separated questions so that students would filter correctly

Kam D. Dahlquist — Sun, 14 Nov 2010 22:10:07 GMT

Sanity Check: Number of genes significantly changed: separated questions so that students would filter correctly

←Older revision		Revision as of 22:10, 14 November 2010
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	** Keeping the "Pvalue" filter at p < 0.05, filter the "Avg_LogFC_all" column to show all genes with an average log fold change greater than zero. How many are there?		** Keeping the "Pvalue" filter at p < 0.05, filter the "Avg_LogFC_all" column to show all genes with an average log fold change greater than zero. How many are there?
	** Keeping the "Pvalue" filter at p < 0.05, filter the "Avg_LogFC_all" column to show all genes with an average log fold change less than zero. How many are there?		** Keeping the "Pvalue" filter at p < 0.05, filter the "Avg_LogFC_all" column to show all genes with an average log fold change less than zero. How many are there?
-	** What about an average log fold change of > 0.25 or < -0.25? (~~This is a~~ more realistic ~~value~~ for the fold change cut-~~off~~ because it represents about a 20% fold change which is about the level of detection of this technology.)	+	** What about an average log fold change of > 0.25 and p < 0.05?
		+	** Or 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. For the GenMAPP analysis below, we will use the fold change cut-off of greater than 0.25 or less than -0.25 and the p value cut off of p < 0.05 for our analysis because we want to include several hundred genes in our analysis.		* 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. For the GenMAPP analysis below, we will use the fold change cut-off of greater than 0.25 or less than -0.25 and the p value cut off of p < 0.05 for our analysis because we want to include several hundred genes in our analysis.
	* What criteria did Merrell et al. (2002) use to determine a significant gene expression change? How does it compare to our method?		* What criteria did Merrell et al. (2002) use to determine a significant gene expression change? How does it compare to our method?

Kam D. Dahlquist: /* Perform statistical analysis on the ratios */ fixed typo

Kam D. Dahlquist — Sun, 14 Nov 2010 21:51:44 GMT

Perform statistical analysis on the ratios: fixed typo

←Older revision		Revision as of 21:51, 14 November 2010
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	* Insert a new worksheet and name it "forGenMAPP".		* Insert a new worksheet and name it "forGenMAPP".
	* 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 ~~selct~~ 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. We will now format this worksheet for import into GenMAPP.
	* 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 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 Columns R and S. Select the menu item Format > Cells. Under the number tab, select 4 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.

John David N. Dionisio: /* Perform statistical analysis on the ratios */ Clarified the instruction for the Tstat column.

John David N. Dionisio — Sat, 23 Oct 2010 06:25:17 GMT

Perform statistical analysis on the ratios: Clarified the instruction for the Tstat column.

←Older revision		Revision as of 06:25, 23 October 2010
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	* Create the equation for patients B and C and paste it into their respective columns.		* Create the equation for patients B and C and paste it into their respective columns.
	* Now you will compute the average of the averages. Type the header "Avg_LogFC_all" into the first cell in the next empty column. Create the equation that will compute the average of the three previous averages you calculated and paste it into this entire column.		* Now you will compute the average of the averages. Type the header "Avg_LogFC_all" into the first cell in the next empty column. Create the equation that will compute the average of the three previous averages you calculated and paste it into this entire column.
-	* Insert a new column next to ~~each average fold change~~ column that you computed in the previous step. Label the column "Tstat". This will compute a T statistic that tells us whether the scaled and centered average log ratio is significantly different than 0 (no change). Enter the equation:	+	* Insert a new column next to the "Avg_LogFC_all" column that you computed in the previous step. Label the column "Tstat". This will compute a T statistic that tells us whether the scaled and centered average log ratio is significantly different than 0 (no change). Enter the equation:
	=AVERAGE(N2:P2)/(STDEV(N2:P2)/SQRT(number of replicates))		=AVERAGE(N2:P2)/(STDEV(N2:P2)/SQRT(number of replicates))
	(NOTE: in this case the number of replicates is 3. Be careful that you are using the correct number of parentheses.) Copy the equation and paste it into all rows in that column.		(NOTE: in this case the number of replicates is 3. Be careful that you are using the correct number of parentheses.) Copy the equation and paste it into all rows in that column.

Zeb Russo: /* Perform statistical analysis on the ratios */ changed 'fold_change' to 'statistics'

Zeb Russo — Wed, 20 Oct 2010 01:39:10 GMT

Perform statistical analysis on the ratios: changed 'fold_change' to 'statistics'

←Older revision		Revision as of 01:39, 20 October 2010
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	The number of degrees of freedom is the number of replicates minus one, so in our case there are 2 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 2 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 "forGenMAPP".
-	* Go back to the "~~foldchange_stats~~" 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 selct 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 selct Paste Special, click on the Values radio button, and click OK. We will now format this worksheet for import into GenMAPP.
	* 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 Columns B through Q (all the fold changes). Select the menu item Format > Cells. Under the number tab, select 2 decimal places. Click OK.

Kam D. Dahlquist: /* Before we begin... */ put in in sub tabs for log2

Kam D. Dahlquist — Tue, 12 Oct 2010 17:29:20 GMT

Before we begin...: put in in sub tabs for log2

←Older revision		Revision as of 17:29, 12 October 2010
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	* The data from the Merrell et al. (2002) paper was accessed from [http://smd.stanford.edu/cgi-bin/publication/viewPublication.pl?pub_no=119 this page] at the Stanford Microarray Database.		* The data from the Merrell et al. (2002) paper was accessed from [http://smd.stanford.edu/cgi-bin/publication/viewPublication.pl?pub_no=119 this page] at the Stanford Microarray Database.
-	* The Log~~(base2)~~ of R/G Normalized Ratio (Median) has been copied from the raw data files downloaded from the Stanford Microarray Database.	+	* The Log<sub>2</sub> of R/G Normalized Ratio (Median) has been copied from the raw data files downloaded from the Stanford Microarray Database.
	**'''Patient A'''		**'''Patient A'''
	***Sample 1: 24047.xls (A1)		***Sample 1: 24047.xls (A1)

J'aime C. Moehlman: changed steps from type to press

J'aime C. Moehlman — Tue, 13 Apr 2010 18:05:16 GMT

changed steps from type to press

←Older revision		Revision as of 18:05, 13 April 2010
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	* You will now compute the Average log ratio for each chip (each column of data). In cell B2, type the following equation:		* You will now compute the Average log ratio for each chip (each column of data). In cell B2, type the following equation:
	=AVERAGE(B4:B5224)		=AVERAGE(B4:B5224)
-	and ~~type~~ "Enter". Excel is computing the average value of the cells specified in the range given inside the parentheses. Instead of typing the cell designations, you can click on the beginning cell, scroll down to the bottom of the worksheet, and shift-click on the ending cell.	+	and press "Enter". Excel is computing the average value of the cells specified in the range given inside the parentheses. Instead of typing the cell designations, you can click on the beginning cell, scroll down to the bottom of the worksheet, and shift-click on the ending cell.
	* You will now compute the Standard Deviation of the log ratios on each chip (each column of data). In cell B3, type the following equation:		* You will now compute the Standard Deviation of the log ratios on each chip (each column of data). In cell B3, type the following equation:
	=STDEV(B4:B5224)		=STDEV(B4:B5224)
-	and ~~type~~ "Enter".	+	and press "Enter".
	* Excel will now do some work for you. Copy these two equations (cells B2 and B3) and paste them into the empty cells in the rest of the columns. Excel will automatically change the equation to match the cell designations for those columns.		* Excel will now do some work for you. Copy these two equations (cells B2 and B3) and paste them into the empty cells in the rest of the columns. Excel will automatically change the equation to match the cell designations for those columns.
	* You have now computed the average and standard deviation of the log ratios for each chip. Now we will actually do the scaling and centering based on these values.		* You have now computed the average and standard deviation of the log ratios for each chip. Now we will actually do the scaling and centering based on these values.

Kam D. Dahlquist: link back to overview of DNA microarray analysis page

Kam D. Dahlquist — Tue, 13 Apr 2010 04:08:41 GMT

link back to overview of DNA microarray analysis page

←Older revision		Revision as of 04:08, 13 April 2010
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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 page has been written with the analysis of the ''Vibrio cholerae'' dataset in mind. However, these steps are similar to what needs to be performed with ''any'' microarray dataset, although the details will differ with the particular experimental design.	+	This page has been written with the analysis of the ''Vibrio cholerae'' dataset in mind. However, these steps are similar to what needs to be performed with ''any'' microarray dataset (see [[BIOL398-01/S10:DNA_Microarrays#Overview_of_Microarray_Data_Analysis \| Overview of Microarray Data Analysis]], although the details will differ with the particular experimental design.

	== Before we begin... ==		== Before we begin... ==

Kam D. Dahlquist: /* Sanity Check: Number of genes significantly changed and compare individual genes with known data */ separated sections

Kam D. Dahlquist — Tue, 13 Apr 2010 04:05:57 GMT

Sanity Check: Number of genes significantly changed and compare individual genes with known data: separated sections

←Older revision		Revision as of 04:05, 13 April 2010
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	** 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.		** 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.

-	== Sanity Check: Number of genes significantly changed ~~and compare individual genes with known data~~ ==	+	== Sanity Check: Number of genes significantly changed ==

	Before we move on to the GenMAPP/MAPPFinder analysis, 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 Merrell et al. (2002).		Before we move on to the GenMAPP/MAPPFinder analysis, 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 Merrell et al. (2002).
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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. For the GenMAPP analysis below, we will use the fold change cut-off of greater than 0.25 or less than -0.25 and the p value cut off of p < 0.05 for our analysis because we want to include several hundred genes in our analysis.		* 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. For the GenMAPP analysis below, we will use the fold change cut-off of greater than 0.25 or less than -0.25 and the p value cut off of p < 0.05 for our analysis because we want to include several hundred genes in our analysis.
	* What criteria did Merrell et al. (2002) use to determine a significant gene expression change? How does it compare to our method?		* What criteria did Merrell et al. (2002) use to determine a significant gene expression change? How does it compare to our method?
-	* Merrell et al. (2002) report that genes with IDs: VC0028, VC0941, VC0869, VC0051, VC0647, VC0468, VC2350, and VCA0583 were all significantly changed in their data. Look these genes up in your spreadsheet? What are ~~thier~~ fold changes and p values? Are they significantly changed in our analysis?	+
		+	== Sanity Check: Compare individual genes with known data ==
		+
		+	* Merrell et al. (2002) report that genes with IDs: VC0028, VC0941, VC0869, VC0051, VC0647, VC0468, VC2350, and VCA0583 were all significantly changed in their data. Look these genes up in your spreadsheet? What are their fold changes and p values? Are they significantly changed in our analysis?

	</div>		</div>

Kam D. Dahlquist: pasted in sanity check from fall 2009 BiolDB wiki

Kam D. Dahlquist — Tue, 13 Apr 2010 04:04:14 GMT

pasted in sanity check from fall 2009 BiolDB wiki

←Older revision		Revision as of 04:04, 13 April 2010
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	** 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.		** 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.

		+	== Sanity Check: Number of genes significantly changed and compare individual genes with known data ==
		+
		+	Before we move on to the GenMAPP/MAPPFinder analysis, 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 Merrell et al. (2002).
		+
		+	* Open your spreadsheet and go to the "forGenMAPP" tab.
		+	* 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 your "Pvalue" column. Select "Custom". In the window that appears, set a criterion that will filter your data so that the Pvalue 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?
		+	* 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 261 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.
		+	* The "Avg_LogFC_all" tells us the size 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.
		+	** Keeping the "Pvalue" filter at p < 0.05, filter the "Avg_LogFC_all" column to show all genes with an average log fold change greater than zero. How many are there?
		+	** Keeping the "Pvalue" filter at p < 0.05, filter the "Avg_LogFC_all" column to show all genes with an average log fold change less than zero. How many are there?
		+	** What about an average log fold change of > 0.25 or < -0.25? (This is a more realistic value for the fold change cut-off 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. For the GenMAPP analysis below, we will use the fold change cut-off of greater than 0.25 or less than -0.25 and the p value cut off of p < 0.05 for our analysis because we want to include several hundred genes in our analysis.
		+	* What criteria did Merrell et al. (2002) use to determine a significant gene expression change? How does it compare to our method?
		+	* Merrell et al. (2002) report that genes with IDs: VC0028, VC0941, VC0869, VC0051, VC0647, VC0468, VC2350, and VCA0583 were all significantly changed in their data. Look these genes up in your spreadsheet? What are thier fold changes and p values? Are they significantly changed in our analysis?

	</div>		</div>