MetaQC

MetaQC provides quantitative quality-control metrics for genomic studies to guide objective inclusion and exclusion decisions in meta-analysis.

Key Features:

• Six QC indices: Six quantitative indices that evaluate multiple aspects of study quality for genomic meta-analysis.
• Internal homogeneity of coexpression structure: Evaluates consistency of gene coexpression patterns among studies.
• External consistency with pathway databases: Assesses concordance of coexpression patterns with established pathway databases.
• Detection accuracy for differential expression and pathway enrichment: Measures accuracy and consistency in detection of differentially expressed genes and enriched pathways.
• Statistical derivation from minus log-transformed P-values: Each QC index is derived from minus log-transformed P-values obtained via formal hypothesis testing.
• Visualization using PCA biplots and standardized mean ranks: Represents study quality metrics with PCA biplots and standardized mean ranks.

Scientific Applications:

• Genomic meta-analysis: Guides selection of homogeneous and high-quality studies to reduce bias and improve reliability of combined analyses.
• Disease-specific meta-analyses: Applied in large-scale meta-analyses for brain cancer, prostate cancer, idiopathic pulmonary fibrosis, and major depressive disorder to identify and exclude problematic studies.

Methodology:

MetaQC derives six QC indices via formal hypothesis testing using minus log-transformed P-values and visualizes results with PCA biplots and standardized mean ranks.