kmcut

kmcut identifies prognostic biomarkers and an optimal numeric cutoff for each biomarker to stratify samples into two sub-groups with significantly different survival outcomes.

Key Features:

• Kaplan-Meier Analysis Optimization: The package implements optimized Kaplan-Meier analysis for survival stratification.
• Biomarker Validation: The package supports identification and validation of prognostic biomarkers.
• Flexible Input Data: The package accepts any quantitative variable with a sufficiently large proportion of unique values, including gene expression data from RNA-seq.

Scientific Applications:

• Prognostic Biomarker Discovery: The package identifies biomarkers that can separate samples into groups with better versus worse survival outcomes.
• Survival Stratification: The package determines optimal numeric cutoffs to stratify test subjects into two sub-groups with significantly different survival.
• Gene Expression Analysis: The package was developed for analysis of RNA-seq and other gene expression data types.

Methodology:

The package determines an optimal numeric cutoff for each biomarker to stratify samples into two sub-groups with significantly different survival.