Overview
In many biological experiments, especially assay development and enzymatic studies, the key insight doesn't always lie in a single raw measurement like fluorescence or OD600. Instead, you might be more interested in how a variable changes over time — for instance, the slope of a time series indicating enzyme activity or growth rate. To enable optimisation of such responses, we have introduced a new transformation into the data preparation app, called Linear Fit. This transformation applies a simple linear fit to your data set. Visualisations of this fit, and key model parameters, are returned and made available for you to use in downstream analysis.
With the new Linear Fit Transformation in the Synthace Platform, you can now derive responses such as the slope or intercept of a fitted line directly from raw data — at scale and with ease. This article explains when and why you'd use this type of transformation and sets the foundation for future curve-fitting capabilities like logistic fits.
When to Use Linear Fits in Synthace
Use linear fitting when:
Your raw data is a time series or sequence of values, and you want to quantify how one variable changes in relation to another (e.g., signal over time).
You want to derive rate-based metrics such as growth rate, reaction rate, or enzymatic activity.
You're interested in slope, intercept, or R² as analytical parameters rather than raw values.
Typical scenarios include:
Fitting signal intensity across multiple timepoints to derive a reaction velocity.
Modeling the growth phase of a bacterial culture to calculate the doubling time from the slope.
Using the intercept to quantify the background signal or initial conditions.
Traditional Challenges Without Synthace
Historically, this type of analysis involves:
Manually exporting raw data to spreadsheets or external tools like Python/R.
Reshaping the data to match the required input formats for curve fitting.
Writing and debugging scripts for linear regression.
Keeping track of which fit belongs to which experimental run in large DOE datasets.
Re-integrating derived parameters back into a design matrix for analysis.
These steps are time-consuming, error-prone, and not scalable when dealing with hundreds of experimental runs.
How Synthace Solves This
With Synthace’s Linear Fit Transformation, you can now:
Fit lines across hundreds of DOE runs in seconds.
Automatically extract slope, intercept, R², and related statistics as new response columns.
Visualize fit quality instantly using best/worst fit previews.
Carry these values directly into response analysis workflows without leaving the platform.
This significantly shortens the path from raw data to meaningful analysis.
Learn more about fitting linear models here.
What’s Next: Curve Fitting and Beyond
While linear fits cover many use cases, not all biological processes are linear. That’s why Synthace will soon support logistic and other nonlinear curve fits, enabling users to derive parameters like EC50 or Hill slope directly from sigmoidal dose-response curves.
Stay tuned for updates as we expand the platform’s model-fitting capabilities.