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

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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.

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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"
  }
}

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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

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Installation

# From CRAN
install.packages("SQUIRE")

# Development version

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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"

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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

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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.

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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

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Development & Support

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

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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.

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License

MIT License. See LICENSE file for details.