Getting Started with SQUIRE

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Introduction

SQUIRE (Statistical Quality-Assured Integrated Response Estimation) provides a structured workflow for biological parameter estimation that combines statistical validation with systematic testing of constraint configurations.

Basic Usage

Here’s a simple example using synthetic germination data:

# Generate example data
set.seed(123)
germination_data <- data.frame(
  time = rep(0:7, times = 12),
  treatment = rep(c("Control", "Inhibitor", "Promoter"), each = 32),
  replicate = rep(rep(1:4, each = 8), times = 3),
  response = c(
    # Control: normal germination
    rnorm(32, mean = rep(seq(0, 80, length.out = 8), 4), sd = 3),
    # Inhibitor: reduced germination
    rnorm(32, mean = rep(seq(0, 60, length.out = 8), 4), sd = 3),
    # Promoter: enhanced germination
    rnorm(32, mean = rep(seq(0, 95, length.out = 8), 4), sd = 3)
  )
)

# Run SQUIRE analysis
results <- SQUIRE(
  data = germination_data,
  treatments = c("Control", "Inhibitor", "Promoter"),
  control_treatment = "Control",
  verbose = FALSE
)

# Check results
if (results$optimization_performed) {
  print("Optimization was performed. Parameter estimates:")
  print(results$parameters$parameter_matrix)
} else {
  print(paste("Optimization not performed:", results$validation_results$reason))
}

Understanding the Workflow

SQUIRE follows a three-step process:

Step 1: Statistical Validation

Tests for significant treatment effects using ANOVA
Calculates effect sizes
Only proceeds if effects are statistically significant

Step 2: Constraint Configuration Testing

Systematically tests combinations of:
- T: Log-scale constraints
- P: Positive domain constraints
- E: Euclidean (unconstrained) parameters
Selects the configuration that best fits the data

Step 3: Geometry-Aware Optimization

Applies the selected constraints during optimization
Uses GALAHAD for trust-region methods
Returns parameter estimates for each treatment

Interpreting Results

The results include:

optimization_performed: Whether optimization proceeded (based on validation)
validation_results: Statistical test results and p-values
parameters: Matrix of optimized parameters for each treatment
galahad_settings: The constraint configuration used

Applications

SQUIRE is designed for:

Germination studies
Growth curve analysis
Enzyme kinetics
RT-QuIC kinetic analysis
Dose-response studies

Any time-series biological data with treatment comparisons can benefit from SQUIRE’s systematic approach to parameter estimation.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.