Sort Rows by Prototype is a function that can identify genes with similar expression patterns. For example, if a gene with an interesting expression pattern has been detected, using Sort Rows by Prototype makes it possible to find other genes that have a similar pattern of intensity values. Although this is most commonly used for changes in gene expression over a time course, it can be applied to other experimental designs as well.
To invoke Sort Rows by Prototype_,_ probe(sets)/genes must be on rows. If you want to use this tool to analyze the main intensity values spreadsheet, the spreadsheet must be transposed prior to analysis. A common way to view and analyze gene expression in a time-series experiment is to include means or LS means in the ANOVA spreadsheet.
Configure the ANOVA dialog to include the factor or interaction of interest
Select Advanced... from the ANOVA dialog
Select LS-Mean or Mean
Use the drop down menus to select the factors or interaction you want the LS mean / mean of
Select Add for each
Select OK (Figure 1)
Figure 1. Using Advanced ANOVA setup to include group means in the ANOVA output
Select OK to close the ANOVA configuration dialog and open the ANOVA spreadsheet
The Sort Rows by Prototype function uses every non-text column in a spreadsheet to build and compare patterns; any columns you do not want to include in the pattern similarity analysis need to be removed before running the function.
If you want to preserve the ANOVA spreadsheet contents, clone the ANOVA spreadsheet prior to deleting columns.
Select columns you want to remove
Right-click on a selected column headers
Select Delete from the pop-up menu
We can now invoke Sort Rows by Prototype on the modified spreadsheet.
Select Tools from the main toolbar
Select Discovery
Select Sort Rows by Prototype... (Figure 2)
Figure 2. Invoking Sort Rows by Prototype on spreadsheet with LS mean values for conditions/time points
The Sort Rows by Prototype dialog will launch (Figure 3).
Figure 3. Sort Rows by Prototype dialog
This dialog allows you to configure the pattern, or prototype, that all probe(sets)/genes will be compared to by Sort Rows by Prototype_._
The Select Dissimilarity Measure drop-down menu allows to select from a wide variety of parametric and non-parametric measures of dissimilarity.
After configuring the prototype and selecting a dissimilarity measure, select Sort to run the function
Select Cancel to close the dialog
A new column 1 will be added to the spreadsheet and the rows will be reordered (Figure 4). The new column contains the dissimilarity score for each row; the lower the value, the more similar the row is to the prototype. The row with the highest similarity to the prototype is listed first, with the other rows listed in descending similarity to the prototype.
Figure 4. Result of sorting by prototype. The prototype gene is in the first row, while the other genes are listed based on their similarity to the prototype gene. Smaller proximity values imply more similarity to the selected shape
To view the results, we can generate a profile plot of several of the rows. For example, here we will show the top five most similar probe(sets)/genes.
Select the row headers of the top 5 rows by selecting each while holding the Ctrl key or selecting the first then fifth while holding the Shift key
Select View from the main toolbar
Select Profiles
Select Row Profiles
Select Select for both Plots and X-Axis in the Configure Data Source dialog
The profile plot will open as a new tab (Figure 5).
Figure 5. Profile plot of 5 probe(sets)/genes most similar to the prototype used in Sort rows by prototype
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Select () from the main command bar to save the modified spreadsheet
The Pattern Type options () allow preset shapes to be applied to the prototype within the range specified by the Begin, End, Min, and Max parameters. The final option From Row allows you to select any row number in the spreadsheet to serve as the prototype. This is a useful option if you have a particular gene of interest and want to find other genes with similar expression profiles in your data set. You can also manually configure the prototype by dragging the points.
