Logistic Growth Fitter

Typing name : TASK.gws_plate_reader.LogisticGrowthFitter

Brick : gws_plate_reader

Fit logistic growth curves to time-series data with cross-validation

[Generated by Task Expert Agent]

Fits logistic growth curves to time-series absorbance data using cross-validation to determine optimal parameters for each well.

Model

The logistic growth model is:

A(t) = A₀ + (Amax - A₀) / (1 + exp(-μ * (t - tlag)))

Where:

A₀: Initial absorbance
Amax: Maximum absorbance
μ: Growth rate
tlag: Lag time

Process

Pre-processes data with spline smoothing
Performs K-Fold cross-validation to find optimal parameters
Extracts growth parameters for each well
Generates fitted curves and diagnostic plots

Inputs

table: Time-series data with time in first column, wells in subsequent columns

Outputs

parameters: Table with fitted parameters (Max_Absorbance, Growth_Rate, Lag_Time, Initial_Absorbance, Avg_R2)
fitted_curves_plot: Interactive plot showing raw data points and fitted curves with R² values
growth_rate_histogram: Histogram of growth rates distribution across wells

Configuration

n_splits: Number of K-Fold cross-validation splits (default: 3)
spline_smoothing: Smoothing parameter for spline preprocessing (default: 0.045)

Input

Time-series data

Table with time in first column and well absorbance data in subsequent columns

Table

Output

Growth parameters

Table containing fitted logistic growth parameters for each well

Table

Fitted curves plot

Interactive plot showing raw data and fitted logistic curves with R² values

Plotly resource

Growth rate histogram

Histogram showing distribution of growth rates across wells

Plotly resource

Configuration

n_splits

Optional

Number of K-Fold cross-validation splits

Type : int

Default value : 3

spline_smoothing

Optional

Smoothing parameter for spline preprocessing (lower = less smoothing)

Type : float

Default value : 0.045

Model

Process

Inputs

Outputs

Configuration

Input

Output

Configuration

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