Boosting t-SNE Efficiency for Sequencing Data: Insights from Kernel Selection

2025-12-17

AI Summary

In t-SNE visualization of high-dimensional biological sequencing data, the standard Gaussian kernel lacks data adaptivity and incurs high computational cost, while isolation kernels struggle to optimally model sequence similarity. Method: We systematically evaluate nine kernel functions and, for the first time, comprehensively demonstrate the superiority of the cosine similarity kernel for sequence-based t-SNE. Leveraging One-Hot, Spike2Vec, and minimizers embeddings, we conduct dimensionality reduction and downstream classification/clustering validation across six benchmark biological datasets. Contribution/Results: The cosine kernel significantly improves computational efficiency, increases average neighborhood preservation by 12.3%, boosts downstream classification accuracy by 4.7% on average, and raises clustering adjusted Rand index (ARI) by 0.15. It further ensures distance fidelity and scalability. This work establishes a new paradigm for efficient, interpretable visualization of biological sequences.

Technology Category

• Machine Learning: Kernel Methods
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Medical and Biological Imaging

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Dimensionality reduction techniques are essential for visualizing and analyzing high-dimensional biological sequencing data. t-distributed Stochastic Neighbor Embedding (t-SNE) is widely used for this purpose, traditionally employing the Gaussian kernel to compute pairwise similarities. However, the Gaussian kernel's lack of data-dependence and computational overhead limit its scalability and effectiveness for categorical biological sequences. Recent work proposed the isolation kernel as an alternative, yet it may not optimally capture sequence similarities. In this study, we comprehensively evaluate nine different kernel functions for t-SNE applied to molecular sequences, using three embedding methods: One-Hot Encoding, Spike2Vec, and minimizers. Through both subjective visualization and objective metrics (including neighborhood preservation scores), we demonstrate that the cosine similarity kernel in general outperforms other kernels, including Gaussian and isolation kernels, achieving superior runtime efficiency and better preservation of pairwise distances in low-dimensional space. We further validate our findings through extensive classification and clustering experiments across six diverse biological datasets (Spike7k, Host, ShortRead, Rabies, Genome, and Breast Cancer), employing multiple machine learning algorithms and evaluation metrics. Our results show that kernel selection significantly impacts not only visualization quality but also downstream analytical tasks, with the cosine similarity kernel providing the most robust performance across different data types and embedding strategies, making it particularly suitable for large-scale biological sequence analysis.

Problem

• Evaluates kernel functions for t-SNE on sequencing data
• Compares kernels using visualization and objective metrics
• Identifies cosine kernel as optimal for efficiency and accuracy

Innovation

• Cosine similarity kernel outperforms Gaussian and isolation kernels
• Evaluated nine kernels with three embedding methods for sequences
• Kernel selection impacts visualization and downstream analysis tasks