Calculating Mean
The mean transformation calculates the average response across multiple replicates—essential in experiments like a DOE enzyme assay. First, ensure your data is reshaped by replicate, so each measurement exists in its own column (e.g., Rep1, Rep2, Rep3).
Steps:
Select Mean from the transformation menu in the Calculate tab.
Name the output column, e.g., “Mean Response.”
Check all replicate columns using the hierarchical checkbox menu.
A preview shows the calculated mean values.
Click Apply to insert the new column into your dataset.
You can edit or re-run this transform anytime in future workflows.
Calculating Standard Deviation
Quantifying variability is crucial in experimental data analysis. With replicates already reshaped into wide format, calculating the standard deviation allows you to assess consistency across repeated measurements.
Steps:
Choose the Standard Deviation transformation in the Calculate tab.
Provide a clear column title, such as “Stdev - Response.”
Select all replicate columns via checkboxes.
Review the preview results.
Click Apply—the new column will appear alongside your existing response and mean data.
Calculating Z-factor
The Z-factor (or Z') is a statistical measure to quantify assay quality between negative and positive controls—perfect for datasets with both conditions (e.g., drug = 0 µM vs. 10 µM). Ensure your data is reshaped by both replicate and drug concentration, producing separate columns for each, against every other factor in the DOE design.
Steps:
In the Calculate tab, select Z factor / Z′ factor.
A built-in description explains how Z′ is computed.
Assign a meaningful column name, like “Response - Z-factor.”
Select negative control columns (e.g., Drug 0 µM replicates).
Select positive control columns (e.g., Drug 10 µM replicates).
After previewing, click Apply to add the Z′ column to your dataset.
To create more bespoke data transformations you can use the Column-based arithmetic transform. This allows you to specify your own formulas for processing data columns, and column groups. Learn more here.