Background
In Synthace, it it possible to perform predefined column based calculations on the data. It is possible to choose from statistical calculations such as mean, standard deviation and variance, as well as the fitted slope of a linear regression.
The first three statistical measures are particularly useful in the case of replicates. If the replicates appear in separate columns, then a new column can be created to capture aggregate measures of those replicates. You can find more information on the statistics behind these calculations here. Briefly, the arithmetic mean is a measure that characterises the central tendency of a set of values, the standard deviation measures the dispersion of a set of values with respect to their mean, and the variance of a set of values is the variability of those values with respect to their mean.
In addition to the statistical calculations above, another predefined column calculation available in Synthace is Fitted Slope. This describes the average rate of change of a measure, with respect to another.
How to use the custom data transformation
To perform a column based calculation, navigate to the Select & Transform tab.
Performing a column calculation
On the left hand side of the page, from the Response or Transformation dropdown menu, choose the response which is to be transformed (e.g. ’R1’).
Underneath the menu, click Copy.
This will trigger a prompt to provide a name for the response which is to be copied (e.g. ‘R1 transformed’). It is advisable to do so, in order to leave the original response (’R1’) unaltered when proceeding with the transformation.
From the Apply new transform dropdown menu, choose the desired type of column calculation. Mean, Variance, Standard Deviation or Fitted Slope.
For Mean, Variance or Standard Deviation, a selection box appears under the dropdown menu. Clicking on it will expand a selection menu. Select, one by one, the columns of interest from which you want to calculate your new data.
After selecting the columns of interest, click Evaluate in order to compute the resulting column based on the selected calculation. Leaving the checkbox Ignore NaNs ticked means that the calculation will be performed while excluding NaN values. These values can be included by deselecting the checkbox.
In the case of an erroneous selection, any of the columns selected for the calculation can be removed by clicking X
For Fitted Slope, a selection box and a text box appear under the dropdown menu.
Clicking the selection box will expand a selection menu. Select, one by one, the columns of interest from which you want to calculate your new data.
In the case of an erroneous selection, any of the columns selected for the calculation can be removed by clicking X.
In the text box, enter the values with respect to which the slope should be calculated. e.g. if the selected columns were read at 2 min, 4 min, and 5 min, then enter 2, 4, 5. The result will provide the average rate of change with respect to minutes. However, if seconds are preferred, then enter the equivalent 120, 240, 300, so that the resulting average rate of change is computed with respect to seconds.
After selecting the columns of interest and typing in the reference points, click Evaluate in order to compute the resulting column based on the selected calculation. Leaving the checkbox Ignore NaNs ticked means that the calculation will be performed while excluding NaN values. These values can be included by deselecting the checkbox.
When satisfied with the transformation, click Save under the Response or Transformation dropdown menu.
Alternatively, clicking Save As will provide the opportunity to rename the transformation.
If unsatisfied with the transformation, clicking Cancel will cancel all unsaved edits.
Well done on making it to the end of this tutorial.
To learn about the statistics behind transformations, click here.
To learn to select and save subsets of your data, click here.
To learn how to apply a predefined transformation to your data, click here.
To learn how to apply custom column based calculations to your data, click here.
To learn how to explore your models and make predictions, click here.