GSECA

GSECA identifies altered biological processes in heterogeneous RNA-sequencing (RNA-seq) data by leveraging bimodal gene expression patterns to detect altered gene sets and pathways.

Key Features:

• Bimodal Expression Analysis: Exploits inherent bimodal distributions in RNA-seq gene expression to distinguish altered gene sets.
• Discretization of Expression Profiles: Discretizes expression profiles to reveal bimodal behavior indicative of altered biological processes.
• High Performance: Demonstrated superior performance versus other algorithms in detecting truly altered biological pathways within large datasets.
• Large-scale Application: Applied to a dataset of 5,941 samples across 14 cancer types to assess robustness on heterogeneous cohorts.
• Pathway Identification (PI3K/AKT): Accurately identified alterations in the PI3K/AKT signaling pathway associated with somatic loss of PTEN.
• Clinical Associations: Revealed PTEN-associated modulation of immune-related processes and associations with patient outcomes such as disease-free survival in prostate cancer.

Scientific Applications:

• Oncology Research: Identification of key signaling pathways and genetic alterations that drive cancer progression from RNA-seq cohorts.
• Large-scale Transcriptomic Analysis: Analysis of heterogeneous, large-scale RNA-seq datasets to uncover pathway-level alterations across diverse patient cohorts.
• Clinical and Biomarker Studies: Linking somatic alterations (e.g., PTEN loss) to immune-related processes and clinical outcomes such as disease-free survival.

Methodology:

GSECA discretizes expression profiles to detect bimodal gene expression distributions and uses those bimodality patterns to identify altered gene sets and pathways.