CiberAMP

CiberAMP analyzes somatic copy number variations (SCNVs) and correlates them with gene expression changes using Cancer Genome Atlas (TCGA) genomic and transcriptomic data to functionally annotate SCNVs in cancer.

Key Features:

• R package implementation: Implemented as an R package for computational analysis of SCNVs and expression data.
• Differential expression analyses: Leverages differential expression analyses to establish correlations between specific SCNVs and resultant gene expression alterations.
• TCGA data integration: Utilizes TCGA genomic and transcriptomic data to identify and catalog SCNV-associated expression changes across cancer types.
• Contextual genomic information: Incorporates contextual genomic information surrounding SCNVs to assess their potential functional relevance.
• Large-scale data processing: Integrates and processes large-scale TCGA datasets to systematically detect SCNV–expression correlations.
• Demonstration dataset: Demonstrated using glioblastoma data from TCGA to showcase identification of SCNV-associated expression changes.

Scientific Applications:

• Functional annotation of SCNVs: Assigns functional relevance to SCNVs by correlating copy-number changes with gene expression alterations.
• Pan-cancer SCNV cataloging: Identifies and catalogs SCNV-associated expression events across multiple cancer types using TCGA data.
• Investigation of cancer progression: Investigates how specific SCNVs may influence cancer progression through associated expression changes.
• Genomic context analysis: Explores the genomic context surrounding SCNVs to infer potential biological implications.
• Glioblastoma analysis: Applies the approach to TCGA glioblastoma data to detect and annotate SCNV-linked expression changes.

Methodology:

Integrates and processes TCGA genomic and transcriptomic data and applies differential expression analyses to identify correlations between SCNVs and gene expression while incorporating contextual genomic information surrounding SCNVs.