SQUIRE: Statistical Quality-Assured Integrated Response Estimation

Author: Richard A. Feiss
Version: 1.0.0
License: MIT
Institution: Minnesota Center for Prion Research and Outreach (MNPRO), University of Minnesota

Overview

SQUIRE (Statistical Quality-Assured Integrated Response Estimation) is an enhanced biological parameter optimization framework that addresses a critical problem in computational biology: parameter over-interpretation from noisy data.

Unlike conventional optimizers that attempt parameter fitting on any dataset, SQUIRE implements statistical gatekeeping - it first validates whether statistically significant biological effects exist before proceeding with parameter estimation. This prevents computational resources being wasted on noise-fitting and ensures that biological interpretations are statistically justified.

SQUIRE builds upon the established GALAHAD optimization framework, adding comprehensive statistical validation and automated parameter type detection.

The Problem SQUIRE Solves

Traditional Approach (Problematic):

# Standard optimizers fit parameters to ANY data
optimizer(noisy_data) -> parameter_estimates  # Always succeeds!
# Result: False biological interpretations

SQUIRE Approach (Statistically Validated):

# SQUIRE validates BEFORE optimizing
SQUIRE(noisy_data) -> statistical_validation -> {
  if (significant_effects) {
    parameter_optimization -> validated_estimates
  } else {
    "No significant biological effects detected"
  }
}

Key Features

Statistical Quality Assurance

Pre-optimization validation: ANOVA testing for treatment effects
Data quality requirements: Enforced minimum timepoints and replication
Effect size assessment: eta-squared calculation for biological meaningfulness
Negative result handling: Clear guidance when optimization is unjustified

Biological Intelligence

Automatic parameter typing: Distinguishes rates, concentrations, bounds
Geometry-adaptive optimization: Applies appropriate methods per parameter type
Treatment framework: Structured comparison against control conditions
Response type awareness: Optimized for germination, growth, survival data

Robust Data Handling

Missing data tolerance: Adaptive strategies based on data completeness
Mixed parameter spaces: Handles diverse biological model constraints
Uncertainty quantification: Statistical confidence in parameter estimates
Reproducible workflows: Consistent optimization across datasets

Installation

# From CRAN
install.packages("SQUIRE")

# Development version

Quick Start

Basic Usage

library(SQUIRE)

# Load example biological data
data("germination_data")  # Hypothetical dataset

# Statistical quality-assured optimization
results <- SQUIRE(
  data = germination_data,
  treatments = c("Control", "Treatment_A", "Treatment_B"),
  control_treatment = "Control",
  response_type = "germination",
  validation_level = 0.05,
  min_timepoints = 5,
  min_replicates = 3,
  verbose = TRUE
)

# Check results
if (results$optimization_performed) {
  # Significant effects detected - optimization justified
  print(results$parameters)
  print(results$biological_interpretation)
} else {
  # No significant effects - optimization not recommended
  print(results$statistical_advice)
}

Example Output (Positive Result)

# When significant biological effects are detected:
$optimization_performed
[1] TRUE

$statistical_validation
$treatment_effect_pvalue
[1] 0.003

$eta_squared  
[1] 0.74

$parameters
    treatment parameter_1 parameter_2 std_error_1 std_error_2
1     Control      0.12        2.5       0.02        0.3
2 Treatment_A      0.18        3.2       0.03        0.4  
3 Treatment_B      0.24        4.1       0.03        0.5

$biological_interpretation
[1] "Statistically significant treatment effects detected (p=0.003, eta-squared=0.74).
    Parameter optimization justified. Treatment_B shows strongest response."

Example Output (Negative Result)

# When no significant effects are detected:
$optimization_performed
[1] FALSE

$statistical_advice  
[1] "No statistically significant treatment effects detected (p=0.23).
    Consider increasing sample size or re-evaluating experimental design."

$data_quality
$adequate_timepoints: TRUE
$adequate_replication: TRUE
$recommendation: "Insufficient biological signal for parameter optimization"

Detailed Workflow

SQUIRE implements a systematic three-stage validation process:

Stage 1: Data Quality Assessment

Minimum timepoints verification (default: 5)
Minimum replication verification (default: 3)
Data completeness assessment
Treatment structure validation

Stage 2: Statistical Effect Detection

ANOVA for treatment differences
Effect size calculation (eta-squared)
Statistical significance testing (alpha = 0.05)
Biological meaningfulness evaluation

Stage 3: Validated Parameter Optimization

Geometry-adaptive GALAHAD-based optimization
Automated parameter type detection
Statistical assessment of parameter estimates
Biological interpretation with confidence measures

Supported Response Types

SQUIRE is optimized for biological data patterns:

"germination": Cumulative germination over time
"growth": Plant/organism growth measurements
"survival": Survival analysis with time-to-event data

Each response type uses specialized validation logic and optimization approaches.

Advanced Features

Custom Validation Levels

# More stringent validation
results <- SQUIRE(
  data = my_data,
  validation_level = 0.01,  # Require p < 0.01
  min_timepoints = 8,       # Require >= 8 timepoints
  min_replicates = 5        # Require >= 5 replicates per treatment
)

Integration with GALAHAD

# Pre-configure GALAHAD parameters (advanced users)
galahad_config <- list(
  geometry_method = "adaptive",
  trust_region_radius = 0.1,
  convergence_tolerance = 1e-6
)

results <- SQUIRE(
  data = my_data,
  galahad_config = galahad_config
)

Citation

When using SQUIRE in publications, please cite:

Feiss, R. A. (2025). SQUIRE: Statistical Quality-Assured Integrated Response Estimation. 
R package version 1.0.0. https://CRAN.R-project.org/package=SQUIRE

Please also cite GALAHAD as SQUIRE builds upon this framework:

Feiss, R. A. (2025). GALAHAD: Geometry-Adaptive Lyapunov-Assured Hybrid Optimizer. 
R package version 1.0.0. https://CRAN.R-project.org/package=GALAHAD

Development & Support

Development: Minnesota Center for Prion Research and Outreach (MNPRO), University of Minnesota
Email: feiss026@umn.edu

Human-AI Development Transparency

Development followed an iterative human-machine collaboration. All algorithmic design, statistical methodologies, and biological validation logic were conceptualized and developed by Richard A. Feiss.

AI systems (Anthropic Claude) served as coding and documentation assistants under continuous human oversight, helping with: - Code optimization and syntax validation - Statistical method verification
- Documentation consistency and clarity - Package compliance checking

AI systems did not originate algorithms, statistical approaches, or scientific methodologies.

License

MIT License. See LICENSE file for details.