Differential Analysis and Visualization of Single-Cell Based on Multiple Dimensionality Reduction Algorithms

随着生物基因测序技术的发展，单细胞RNA测序(scRNA-seq)技术在细胞异质性研究中发挥了重要作用，单核细胞的转录组分析提供了丰富的基因表达数据。为了分析这些高维数据的潜在特征，本研究使用多种降维算法，包括主成分分析(PCA)、统一流形逼近与投影(UMAP)和t-分布随机邻域嵌入(t-SNE)，对单细胞基因表达的结构特征进行揭示。这些降维方法能够有效将高维数据映射至低维空间，从而帮助我们发现单细胞的亚群分布、动态变化和差异性表达基因的聚类特征。通过可视化展示，本文探索了基因表达的内在规律，进一步为单细胞在疾病诊断、治疗以及精准医疗中的应用提供了新的见解。本文强调了统计方法在生物数据分析中的核心作用，特别是与可视化技术的结合上，为后续研究提供了有力支持。With the advancement of biological gene sequencing technologies, single-cell RNA sequencing (scRNA-seq) has played a significant role in studying cell heterogeneity. The transcriptomic analysis of mononuclear cells provides rich gene expression data. To analyze the underlying patterns of these high-dimensional data, this study applies multiple dimensionality reduction algorithms, including Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), and t-Distributed Stochastic Neighbor Embedding (t-SNE), to reveal the structural features of single-cell gene expression. These dimensionality reduction methods map high-dimensional data to lower-dimensional spaces, helping to uncover the distribution of subpopulations, dynamic changes, and clustering features of differentially expressed genes. Through visualization, this study explores the inherent patterns of gene expression and provides new insights into the applications of single-cell data in disease diagnosis, treatment, and precision medicine. This paper emphasizes the central role of statistical methods in biological data analysis, particularly in the integration of dimensionality reduction and visualization techniques, offering strong support for future research.