Laurmagee: Week 15
Statistical Analysis
- Open the following spreadsheet File:Compiled Ratios and Logs.xls from Laurmagee: Week 13.
- Begin scaling and centering the data by first inserting a new worksheet in Excel labeled "scaled_centered".
- Select and copy all of the data from your original worksheet. Then paste it into cell A1 in new worksheet.
- Insert two rows in between the top row of headers and the first data row. In cell A2, type "Average" and in cell A3, type "StdDev".
You will now compute the Average log ratio for replicant and time period.
- In cell B2, type the following equation:
=AVERAGE(B4:B5224) 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:
=STDEV(B4:B5224) 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.
You have now computed the average and standard deviation of the log ratios for replicant and time period.
- Copy the column headings for all of your data columns and then paste them to the right of the last data column so that you have a second set of headers above blank colums of cells. Edit the names of the columns so that they now read: A1_scaled_centered, A2_scaled_centered, etc.
In cell N4, type the following equation: =(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 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 the scaling and centering equation for each of the columns of data with the "_scaled_centered" column header. Be sure that your equation is correct for the column you are calculating. 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. Insert a new worksheet and name it "statistics". Go back to the "scaling_centering" worksheet and copy the first column ("ID"). Paste the data into the first column of your new "statistics" worksheet. Go back to the "scaling_centering" worksheet and copy the columns that are designated "_scaled_centered". Go to your new worksheet and click on the B1 cell. Select "Paste Special" from the Edit menu. A window will open: click on the radio button for "Values" and click OK. This will paste the numerical result into your new worksheet instead of the equation which must make calculations on the fly. Go to a new column on the right of your worksheet. Type the header "Avg_LogFC_A", "Avg_LogFC_B", and "Avg_LogFC_C" into the top cell of the next three columns. Compute the average log fold change for the replicates for each patient by typing the equation: =AVERAGE(B2:E2) into cell N2. Copy this equation and paste it into the rest of the column. 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. 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)) (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. Label the top cell in the next column "Pvalue". In the cell below the label, enter the equation: =TDIST(ABS(R2),degrees of freedom,2) 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". 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. 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 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. 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.
Sanity Check
- How many genes have p value < 0.05 for the time set of 15 minutes?
- 3613 genes
- How many genes have p value < 0.05 for the time set of 30 minutes?
- 5225 genes
- How many genes have p value < 0.05 for the time set of 60 minutes?
- 5207 genes
- How many genes have p value < 0.05 for the time set of 240 minutes?
- 6790 genes
- How many genes have p value < 0.01 for the time set of 15 minutes?
- 907 genes
- How many genes have p value < 0.01 for the time set of 30 minutes?
- 1518 genes
- How many genes have p value < 0.01 for the time set of 60 minutes?
- 1553 genes
- How many genes have p value < 0.01 for the time set of 240 minutes?
- 2437 genes
- How many genes have p value < 0.001 for the time set of 15 minutes?
- 92 genes
- How many genes have p value < 0.001 for the time set of 30 minutes?
- 179 genes
- How many genes have p value < 0.001 for the time set of 60 minutes?
- 172 genes
- How many genes have p value < 0.001 for the time set of 240 minutes?
- 347 genes
- How many genes have p value < 0.0001 for the time set of 15 minutes?
- 7 genes
- How many genes have p value < 0.0001 for the time set of 30 minutes?
- 15 genes
- How many genes have p value < 0.0001 for the time set of 60 minutes?
- 13 genes
- How many genes have p value < 0.0001 for the time set of 240 minutes?
- 36 genes
- 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 for the time set of 15 minutes?
- 1521 genes
- 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 for the time set of 30 minutes?
- 1926 genes
- 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 for the time set of 60 minutes?
- 2194 genes
- 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 for the time set of 240 minutes?
- 2846 genes
- 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 for the time set of 15 minutes?
- 2092 genes
- 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 for the time set of 30 minutes?
- 3299 genes
- 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 for the time set of 60 minutes?
- 3013 genes
- 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 for the time set of 240 minutes?
- 3944 genes
- What about an average log fold change of > 0.25 and p < 0.05 for the time set of 15 minutes?
- 1476 genes
- What about an average log fold change of > 0.25 and p < 0.05 for the time set of 30 minutes?
- 1890 genes
- What about an average log fold change of > 0.25 and p < 0.05 for the time set of 60 minutes?
- 2129 genes
- What about an average log fold change of > 0.25 and p < 0.05 for the time set of 240 minutes?
- 2763 genes
- Or an average log fold change of < -0.25 and p < 0.05 for the time set of 15 minutes?
- 2052 genes
- Or an average log fold change of < -0.25 and p < 0.05 for the time set of 30 minutes?
- 3256 genes
- Or an average log fold change of < -0.25 and p < 0.05 for the time set of 60 minutes?
- 2942 genes
- Or an average log fold change of < -0.25 and p < 0.05 for the time set of 240 minutes?
- 3866 genes
