QTModel

QTModel: Microarray Differential Expression and Mixed Model Analysis

QTModel performs microarray data analysis to identify differentially expressed genes (DEGs) under one- and two-treatment factor designs using diallel design and mixed model analysis.

Key Features:

• Differentially Expressed Gene Identification: Detects DEGs from gene expression data with support for one- and two-treatment factors and accommodates missing observations.
• Statistical Testing Framework: Applies an F statistic based on Henderson's method III to evaluate treatment effects and adjusts cutoff P values to control the experimental-wise false discovery rate.
• Comparative Analytical Performance: Achieves performance comparable to SAM (Significance Analysis of Microarrays) and MAANOVA (Microarray Analysis of Variance), with improved robustness to missing data and detection of region-specific expression patterns.

Scientific Applications:

• Human Acute Leukemia Gene Expression Analysis: Reanalyzed microarray data from 38 leukemia patients to identify DEGs with efficacy comparable to MAANOVA while efficiently handling missing data.
• Mouse Brain Regional Expression Profiling: Analyzed gene expression across six brain regions in two inbred mouse strains, identifying increased region-specific expression patterns in multifactorial designs.

Methodology:

QTModel integrates diallel design modules and mixed model analysis, employing Henderson's method III-based F statistics and false discovery rate-controlled P value thresholds to assess differential gene expression in complex microarray experimental designs.