In miLAG: Calculates Microbial Lag Duration (on the Population Level) from Provided Growth Curve Data

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This vignette describes the plot_lag_fit function, which is used to plot a single growth curve along with the calculated lag and the rationale for lag calculation.

The plot_lag_fit Function

Introduction

The plot_lag_fit function creates a plot for a provided growth curve using data generated by the Calculate.Lag function. The plot includes the growth curve, calculated lag, and additional information related to the lag calculation method.

Usage

The function takes the following parameters:

• data_new: A data frame generated by the Calculate.Lag function with required columns such as "time," "biomass," "tangent.point," "predicted.data," "threshold," "N0," "second.deriv.b," "line.intercept," and "line.slope."
• log10_transform: If set to TRUE, it plots the y-axis (biomass) on a log10 scale.
• print_lag_info: If set to TRUE, it prints the lag length on the graph. The function returns a ggplot object with the growth curve and other plot elements.

Examples

# Load required libraries
library(dplyr)

# Generate example data using Calculate.Lag function
set.seed(123)
time <- 1:10
biomass <- c(0.1, 0.3, 0.7, 1.5, 3.0, 5.0, 8.0, 12.0, 18.0, 25.0)
tangent.point <- c(0.3, 0.5, 0.9, 2.0, 4.0, 6.0, 9.0, 12.0, 17.0, 24.0)
predicted.data <- c(0.1, 0.4, 0.8, 1.6, 3.2, 6.0, 8.8, 12.5, 18.2, 25.1)
threshold <- c(0.3, 0.8, 1.3, 2.3, 4.3, 7.0, 10.2, 15.0, 21.0, 28.0)
N0 <- c(0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1)
second.deriv.b <- c(0.02, 0.04, 0.09, 0.2, 0.4, 0.6, 0.9, 1.2, 1.7, 2.4)
line.intercept <- c(0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1)
line.slope <- c(0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2)

data_new <- data.frame(
  time = time,
  biomass = biomass,
  tangent.point = tangent.point,
  predicted.data = predicted.data,
  threshold = threshold,
  N0 = N0,
  second.deriv.b = second.deriv.b,
  line.intercept = line.intercept,
  line.slope = line.slope
)

# Plot the growth curve with lag information
plot <- plot_lag_fit(data_new, print_lag_info = TRUE, log10_transform = TRUE)

# Print the plot
print(plot)

Conclusion

The plot_lag_fit function is useful for visualizing a growth curve and the calculated lag, providing valuable insights into bacterial growth analysis.

miLAG documentation built on April 3, 2025, 8:09 p.m.