TABLE 1.

Summary of the data assembly and analysis protocol for strain variability of growth ability under stress conditions

Protocol step	Specific consideration(s)	Method(s) or example(s)a
1. Measurement of growth ability under stress	Optimize stress condition by piloting strain variability.	Fig. S1
	Randomize pilot strains and the testing order of strains.	Functions to generate random numbers without repeats, e.g., in R or Excel
	Minimize potential technical variation and batch effects:
	Use the same growth medium throughout the entire experiment.	Prepare the required amount all at once
	Have one individual perform all experiments, if possible.	Fig. S4, S5
	Utilize within-experiment technical and biological replicates.	Fig. S11
	Utilize between-experiment control strains.	Fig. S11
	When using Bioscreen, leave the outermost rim of the honeycomb plate blank to avoid test broth evaporation.	Fig. S3, S11
	Detect potential contamination and empty wells; retest, if necessary.	Visualize growth curves during or after experiment; culture honeycomb plate wells with unusual growth and no growth
	Inspect and deal with outliers.	Fig. S2, Text S10: volume 1
	Inspect and normalize potential technical variation and batch effects.	Fig. S6–9, Text S10: volumes 2 and 3
2. Selection of a suitable method for growth parameter calculation	Calculate growth parameters using several methods.	Text S10: volume 4
	Compare the fit and values of the parameter calculation methods.	Text S10: volume 5
3. Comparison of growth patterns between strains	Visualize growth parameters to see overall parameter variation.	Text S10: volume 6
	Visualize growth curves to see entire growth patterns.	Text S10: volumes 3 and 7
	Combine statistical methods and intuitive reasoning to determine a suitable way to quantify strain variability.	Fig. S12–15
4. Biological interpretation of the discovered differences	Classify strain variability and interpret it via growth parameters.	Text S10: volumes 6 and 7
	Investigate strain variability with biological background variables and draw conclusions.	Data exploration and statistical tests (see Materials and Methods for examples)

^aMethods or examples described in the supplemental material published with this article are indicated by their number. Text S10 includes R codes for the data analyses and is divided into volumes 1 to 7 according to their content.