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<p>Single-cell multi-omics technologies have brought a groundbreaking transformation to the field of cell biology by uncovering the intricate connections between an organism’s genetic blueprint and its observable traits. While all cells in an organism share the same genetic makeup, variations in gene expression shape their physiological characteristics. Single-cell RNA sequencing (scRNA-seq) has become a pivotal method, offering detailed insights into transcriptomic landscapes with exceptional resolution. Analyzing scRNA-seq data requires effective dimensionality reduction methods to simplify the high-dimensional datasets while preserving critical biological information. Traditional techniques like principal component analysis, though fundamental, often struggle to capture the full complexity of diverse cellular populations. Nonlinear dimensionality reduction methods have emerged as powerful alternatives, offering a more detailed and accurate representation of cellular relationships. Among these, pairwise controlled manifold approximation projection (PaCMAP) has gained recognition for its ability to preserve both local and global data structures effectively. This paper introduces compactness preservation pairwise controlled manifold approximation projection (CP-PaCMAP), an enhanced dimensionality reduction method tailored for scRNA-seq data visualization. CP-PaCMAP improves upon its predecessor by focusing on maintaining data compactness, which is critical for accurate classification and clustering. Benchmark datasets from significant human organs are used to illustrate the effectiveness of this approach, highlighting its potential to provide clearer insights into complex biological data. To assess the performance of CP-PaCMAP, a variety of evaluation metrics are employed, including reliability, stability, Matthew correlation coefficient, and the Mantel test. These metrics collectively demonstrate CP-PaCMAP’s superior ability to retain meaningful biological patterns compared to other state-of-the-art dimensionality reduction techniques, making it an invaluable tool for advancing single-cell transcriptomic analysis.</p>

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Advancing dimensionality reduction for enhanced visualization and clustering in single-cell transcriptomics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月5日

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  • P. Sanju

摘要

Single-cell multi-omics technologies have brought a groundbreaking transformation to the field of cell biology by uncovering the intricate connections between an organism’s genetic blueprint and its observable traits. While all cells in an organism share the same genetic makeup, variations in gene expression shape their physiological characteristics. Single-cell RNA sequencing (scRNA-seq) has become a pivotal method, offering detailed insights into transcriptomic landscapes with exceptional resolution. Analyzing scRNA-seq data requires effective dimensionality reduction methods to simplify the high-dimensional datasets while preserving critical biological information. Traditional techniques like principal component analysis, though fundamental, often struggle to capture the full complexity of diverse cellular populations. Nonlinear dimensionality reduction methods have emerged as powerful alternatives, offering a more detailed and accurate representation of cellular relationships. Among these, pairwise controlled manifold approximation projection (PaCMAP) has gained recognition for its ability to preserve both local and global data structures effectively. This paper introduces compactness preservation pairwise controlled manifold approximation projection (CP-PaCMAP), an enhanced dimensionality reduction method tailored for scRNA-seq data visualization. CP-PaCMAP improves upon its predecessor by focusing on maintaining data compactness, which is critical for accurate classification and clustering. Benchmark datasets from significant human organs are used to illustrate the effectiveness of this approach, highlighting its potential to provide clearer insights into complex biological data. To assess the performance of CP-PaCMAP, a variety of evaluation metrics are employed, including reliability, stability, Matthew correlation coefficient, and the Mantel test. These metrics collectively demonstrate CP-PaCMAP’s superior ability to retain meaningful biological patterns compared to other state-of-the-art dimensionality reduction techniques, making it an invaluable tool for advancing single-cell transcriptomic analysis.