Kwon Yuseong, Park Sojeong, Park Soyoung, Lee Haeseung
Department of Pharmacy, College of Pharmacy and Research Institute for Drug Development, Pusan National University, Busan, Republic of Korea.
Department of Statistics, College of Natural Science, Pusan National University, Busan, Republic of Korea.
Sci Rep. 2025 Sep 1;15(1):32173. doi: 10.1038/s41598-025-12021-7.
Drug-induced transcriptomic data are crucial for understanding molecular mechanisms of action (MOAs), predicting drug efficacy, and identifying off-target effects. However, their high dimensionality presents challenges for analysis and interpretation. Dimensionality reduction (DR) methods simplify such data, enabling efficient analysis and visualization. Despite their importance, few studies have evaluated the performance of DR methods specifically for drug-induced transcriptomic data. We tested the DR methods across four distinct experimental conditions using data from the Connectivity Map (CMap) dataset, which includes different cell lines, drugs, MOA, and drug dosages. t-distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), Pairwise Controlled Manifold Approximation (PaCMAP), and TRIMAP outperformed other methods in preserving both local and global biological structures, particularly in separating distinct drug responses and grouping drugs with similar molecular targets. However, most methods struggled with detecting subtle dose-dependent transcriptomic changes, where Spectral, Potential of Heat-diffusion for Affinity-based Trajectory Embedding (PHATE), and t-SNE showed stronger performance. Standard parameter settings limited the optimal performance of DR methods, highlighting the need for further exploration of hyperparameter optimization. Our study provides valuable insights into the strengths and limitations of various DR methods for analyzing drug-induced transcriptomic data. While t-SNE, UMAP, and PaCMAP are well-suited for studying discrete drug responses, further refinement is needed for detecting subtle dose-dependent changes. This study highlights the importance of selecting the DR method to accurately analyze drug-induced transcriptomic data.
药物诱导的转录组数据对于理解分子作用机制（MOA）、预测药物疗效以及识别脱靶效应至关重要。然而，其高维度给分析和解释带来了挑战。降维（DR）方法简化了此类数据，实现了高效的分析和可视化。尽管它们很重要，但很少有研究评估DR方法对药物诱导的转录组数据的性能。我们使用来自连通性图谱（CMap）数据集的数据，在四种不同的实验条件下测试了DR方法，该数据集包括不同的细胞系、药物、MOA和药物剂量。t分布随机邻域嵌入（t-SNE）、均匀流形近似与投影（UMAP）、成对控制流形近似（PaCMAP）和TRIMAP在保留局部和全局生物学结构方面优于其他方法，特别是在分离不同的药物反应和对具有相似分子靶点的药物进行分组方面。然而，大多数方法在检测细微的剂量依赖性转录组变化方面存在困难，其中光谱法、基于亲和力轨迹嵌入的热扩散势（PHATE）和t-SNE表现出更强的性能。标准参数设置限制了DR方法的最佳性能，突出了进一步探索超参数优化的必要性。我们的研究为分析药物诱导的转录组数据的各种DR方法的优势和局限性提供了有价值的见解。虽然t-SNE、UMAP和PaCMAP非常适合研究离散的药物反应，但检测细微的剂量依赖性变化还需要进一步改进。这项研究强调了选择DR方法以准确分析药物诱导的转录组数据的重要性。
