factDesign

factDesign implements statistical modeling for microarray factorial experiments to translate biological hypotheses into main effects, interaction effects, and contrasts for hypothesis-driven analysis of transcript abundance.

Key Features:

• Factorial model formulation: Formulates and evaluates statistical models that incorporate factorial experimental designs for microarray data.
• Hypothesis-to-parameter mapping: Translates biological hypotheses into statistical parameters corresponding to main effects, interaction effects, and contrasts.
• Contrast construction: Constructs contrasts that directly encode scientific hypotheses for targeted hypothesis testing.
• Significance quantification: Quantifies the significance of factor-specific effects and interprets interaction structures.
• Confound discrimination: Identifies expression changes attributable to specific experimental factors rather than confounded or unstructured variation.
• Experimental design guidance: Guides factorial experiment design with attention to achieving appropriate statistical power for mechanistic transcriptional questions.
• Microarray focus: Applies specifically to high-throughput microarray gene expression profiling measuring thousands of transcripts.

Scientific Applications:

• Treatment effects: Assessing treatment-induced changes in gene expression within factorial designs.
• Environmental perturbations: Evaluating transcriptional responses to environmental perturbations.
• Genotype–environment interactions: Detecting and interpreting genotype–environment interaction effects on transcript abundance.
• Time-by-treatment interactions: Analyzing temporal interactions between time and treatment factors.
• Design for power: Designing factorial microarray experiments to ensure sufficient statistical power for testing mechanistic hypotheses.
• Attribution of effects: Identifying which experimental factors are responsible for observed expression changes.

Methodology:

Formulation and evaluation of statistical models for factorial microarray experiments; translation of hypotheses into parameters corresponding to main effects, interaction effects, and contrasts; quantification of factor-specific significance, interpretation of interaction structures, and construction of contrasts.