GuidedClustering

GuidedClustering integrates clinical microarray profiles with per-gene experimental measurements using guided clustering to identify gene sets that are prominent in experimental data and coherent across clinical datasets.

Key Features:

• Joint Analysis Capability: Integrates transcriptomic, proteomic, or metabolomic profiles from patient samples with genomic data derived from cell lines or animal models in a single joint analysis.
• Guided clustering strategy: Merges experimental high-throughput per-gene measurements with clinical datasets in a unified analysis rather than through sequential methods.
• Identification of Coherent Gene Sets: Identifies gene sets that are prominent in experimental data and exhibit coherent expression patterns in clinical microarray data.
• Assay integration: Facilitates integration of clinical microarray data with genome-wide chromatin immunoprecipitation (ChIP) assays and with cell perturbation assays.
• Performance evaluation: Shows favorable performance compared to traditional sequential analysis approaches based on simulation studies and multiple biological applications.
• Implementation: Provided as an R package.
• Data availability: Newly generated data are deposited in the Gene Expression Omnibus (GEO) under accession GSE29700.

Scientific Applications:

• Integrative experimental–clinical analysis: Linking cell line or animal model experiments and cell perturbation assays to patient microarray profiles to prioritize clinically relevant genes.
• Regulatory target prioritization: Combining genome-wide ChIP assay data with clinical expression to prioritize regulatory targets.
• Biomarker and mechanism discovery: Supporting discovery of biomarkers, investigation of disease mechanisms, and identification of therapeutic targets from integrated datasets.

Methodology:

Applies a "guided clustering" joint-analysis approach that merges experimental high-throughput per-gene measurements with clinical microarray datasets and was evaluated using simulation studies and biological applications.