Evergene

Evergene performs large-scale, gene-centric analysis of primary tumors utilizing RNA-sequencing data, including principal component analysis, survival analysis, and correlation analysis with sample-level molecular and clinical annotations.

Key Features:

• Principal Component Analysis: Dimensional reduction methods enable exploration of cancer transcriptomic data.
• Survival Analysis: Relates gene expression with clinical events using sample and clinical annotation data.
• Correlation Analysis: Analyzes relationships between gene expression and other sample information such as ethnicity, histological classification, and molecular indices.
• Custom Data Upload: Users can upload custom datasets for analysis.

Scientific Applications:

• Cancer Transcriptomics: In-depth analysis of cancer transcriptomic data from large comprehensive cancer research projects such as The Cancer Genome Atlas (TCGA).
• Gene-Clinical Annotation Integration: Exploration of gene expression alongside sample-level molecular and clinical annotations including ethnicity, histological classification, and molecular indices.
• Multi-Gene Scale Analysis: Individual gene-level analysis rather than single gene or condensed gene set input.

Methodology:

Evergene is implemented in R and Shiny.