Deconfounded Dimension Reduction via Partial Embeddings.

Area of Science:

• Computational Biology
• Data Science
• Bioinformatics

Background:

• High-dimensional biological data, such as single-cell sequencing and neuroimaging, often contain complex patterns obscured by confounding factors.
• Existing dimension reduction techniques, including t-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP), excel at preserving data structure but do not inherently account for unwanted sources of variation.

Approach:

• We introduce the partial embedding (PARE) framework, a novel method designed to remove confounding effects from any distance-based dimension reduction technique.
• PARE is integrated with popular methods to create partial t-SNE and partial UMAP, enabling the isolation of biological signals from technical or clinical variability.
• The framework is applied to diverse high-dimensional datasets, including genomic and neuroimaging data, to demonstrate its versatility and effectiveness.

Key Points:

• The PARE framework successfully removes batch effects in single-cell RNA sequencing data, a common challenge in genomics.
• In neuroimaging, PARE effectively separates clinical variability from technical noise, improving the interpretability of brain data.
• Partial dimension reduction methods developed using PARE highlight biologically relevant patterns while mitigating the influence of confounding variables.

Conclusions:

• The PARE framework provides a robust and generalizable approach to enhance existing dimension reduction tools by removing unwanted effects.
• This methodology allows for a clearer visualization and analysis of biological patterns of interest in high-dimensional data.
• PARE represents a significant advancement in computational biology and data analysis, enabling more accurate insights from complex biological datasets.