Benchmarking of dimensionality reduction methods to capture drug response in transcriptome data.

ABSTRACT: 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 Con

SUBMITTER: Kwon Y 

PROVIDER: S-EPMC12402208 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature