MergeMaid

MergeMaid merges gene expression datasets from multiple studies to enable cross-study validation and comparative analysis of gene expression–phenotype associations.

Key Features:

• Implementation: Implemented as an R package for cross-study integration of gene expression datasets.
• Data merging: Merges multiple gene expression datasets using arbitrary character IDs to align common genes across studies.
• Integrative correlation plots: Generates integrative correlation plots that compare pairwise correlations between studies for individual genes and for all genes combined.
• Gene-specific visualization: Provides gene-specific plots to identify genes with consistently measured changes across datasets to support biomarker identification.
• Statistical visualization: Visualizes gene-specific statistics and produces scatterplots derived from linear, logistic, and Cox regression analyses to quantify expression–phenotype associations.
• Cross-platform analysis: Enables joint analyses and cross-study validation without requiring platform-specific normalization.

Scientific Applications:

• Cross-study validation: Validates reproducibility of gene expression signals across independent studies.
• Biomarker discovery: Identifies genes with consistent measurement patterns across datasets for potential biomarker selection.
• Survival and clinical outcome analysis: Assesses associations between gene expression and survival outcomes using Cox regression, applicable to oncology studies.
• Comparative expression analysis: Facilitates comparative analyses of gene expression patterns across diverse platforms and cohorts.

Methodology:

Merges gene expression data using arbitrary character IDs and generates integrative correlation plots and gene-specific scatterplots derived from linear, logistic, and Cox regression analyses to assess cross-study correlations and expression–phenotype associations.