PESSA

PESSA performs survival analysis in cancer using gene set activation levels derived from ssGSEA.

Key Features:

• Kaplan-Meier Analysis: PESSA performs median and optimal cut-off dichotomous grouping of ssGSEA scores for Kaplan-Meier analyses with accompanying visualization plots.
• Cox Regression Analysis: PESSA performs Cox regression analyses of dichotomous and continuous variables.
• Pre-grouped Datasets: PESSA includes 238 datasets from GEO, TCGA, EGA, and supplementary tables covering 51 cancer types, 13 survival outcome types, and 13,434 tumor-related gene sets from MSigDB.

Scientific Applications:

• Tumor Prognostic Analysis: PESSA enables visualization of tumor prognostic analyses using predefined gene set activation levels as molecular markers.
• Gene Set Marker Evaluation: Users can select gene set markers of interest from MSigDB to evaluate their prognostic value across multiple cancer datasets.

Methodology:

Gene set activation levels are assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from GEO, TCGA, EGA, and supplementary tables. All data analyses were implemented via R. Median and optimal cut-off dichotomous grouping of ssGSEA scores rely on the survival and survminer packages.