Contrastive dimension reduction: when and how?

Area of Science:

• Data Science
• Biomedical Data Analysis
• Machine Learning

Background:

• Traditional dimension reduction (DR) methods are unsuitable for datasets with distinct foreground (case) and background (control) groups.
• Biomedical studies often feature such contrastive data structures, requiring specialized techniques.
• Existing contrastive dimension reduction (CDR) methods lack clear guidelines for application and quantification of unique information.

Purpose of the Study:

• To develop a hypothesis test for detecting contrastive information in datasets.
• To introduce a contrastive dimension estimator (CDE) for quantifying unique foreground group components.
• To address the underexplored questions of when to apply CDR and how to quantify unique foreground information.

Main Methods:

• Proposed a novel hypothesis testing framework to ascertain the presence of contrastive information.
• Developed a contrastive dimension estimator (CDE) to measure unique components within the foreground group.
• Provided theoretical underpinnings and extensive validation through simulations and real-world data.

Main Results:

• The proposed hypothesis test effectively determines the existence of contrastive information.
• The CDE accurately quantifies unique components enriched in the foreground group.
• Methods demonstrated robust performance across diverse data types including images, gene expression, protein expression, and medical sensor data.

Conclusions:

• The developed methods provide crucial guidance on the application of CDR.
• Novel approaches enable effective quantification of unique information in foreground groups.
• These advancements enhance the analysis of contrastive biomedical datasets, improving data-driven insights.