SIM

SIM identifies associations between DNA copy number and RNA expression across human genomic datasets to detect gene-dosage effects relevant to tumorigenesis.

Key Features:

• Joint copy number–expression analysis: Performs joint analysis of DNA copy number and RNA expression rather than separate analyses.
• Gene-set modeling: Models associations using gene sets instead of individual genes to improve robustness and sensitivity.
• Detection of long-spanning, low-amplitude aberrations: Detects long-spanning, low-amplitude copy number aberrations by aggregating signal across multiple resident genes within chromosomal regions.
• Probe mapping independence: Operates without requiring mapping between copy number and expression probes.
• Incorporation of explanatory variables: Allows inclusion of additional explanatory variables in the analysis.
• Cross-platform integration: Integrates multiple microarray datasets and supports analyses across different platforms.

Scientific Applications:

• Gene-dosage association discovery: Identifies associations between gene dosage (copy number) and gene expression levels.
• Prioritization of targets for validation: Prioritizes putative targets for functional validation by highlighting genes whose expression correlates with copy number aberrations.
• Tumorigenesis studies: Facilitates analysis of genes involved in tumorigenesis by linking copy number changes to expression alterations.
• Cross-dataset comparison: Reveals consistent association patterns across independent datasets, as demonstrated in re-analyses of two breast cancer microarray datasets from different platforms.

Methodology:

Performs joint analysis of copy number and expression using a gene-set based model that aggregates expression of multiple resident genes per chromosomal region, operates without probe-level mapping between platforms, and supports inclusion of additional explanatory variables.