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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

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SurvMarker: an R package for identifying survival-associated molecular features using PCA-based weighted scores.

Dona Hasini Gammune1, Tongjun Gu2,3,4

  • 1Versiti Blood Research Institute, 8727 W Watertown Plank Rd, Milwaukee, WI, 53226, USA.

BMC Bioinformatics
|May 8, 2026
PubMed
Summary

SurvMarker, a new R package, identifies prognostic molecular features from high-dimensional data using a novel PCA-based scoring framework. It improves biomarker discovery by enhancing false discovery control and predictive performance in survival studies.

Keywords:
Feature Selection; Weighted Score; Survival Analysis; Principal Components; Dimension Reduction

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Identifying prognostic molecular features is crucial for biomarker discovery in complex diseases.
  • Principal Component Analysis (PCA) is a common method for dimensionality reduction in survival studies, but feature selection from PCs is challenging.
  • Existing methods often rely on arbitrary thresholds for feature selection from principal components.

Purpose of the Study:

  • To develop a robust and interpretable framework for prioritizing survival-associated molecular features from high-dimensional data.
  • To address the limitations of arbitrary thresholds in selecting features from principal components in survival studies.
  • To introduce SurvMarker, an R package designed for PCA-based survival feature selection.

Main Methods:

  • SurvMarker applies PCA to normalized molecular data and jointly evaluates PCs using multivariable Cox proportional hazards models.
  • Features are ranked by aggregating absolute loadings across survival-associated PCs.
  • Feature significance is assessed using an empirical null framework with false discovery rate control.

Main Results:

  • SurvMarker demonstrated superior false positive control compared to LASSO Cox, Elastic Net Cox, and Partial Least Squares Cox in simulations, especially in small-n, large-p settings.
  • In the TCGA-LAML cohort, SurvMarker achieved the best predictive performance for gene expression data (C-index=0.78, AUC=0.882) and miRNA expression data.
  • SurvMarker outperformed sparse PCA and fixed per-PC threshold approaches in predictive performance, yielding more compact and stable feature sets.

Conclusions:

  • SurvMarker offers a robust, interpretable, and reproducible framework for identifying survival-associated molecular features.
  • The package improves false discovery control, stability, and biological relevance through survival-guided PC selection and empirical null-based inference.
  • SurvMarker serves as a practical tool for biomarker discovery across multiple omics data types.