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

Area of Science:

• Transcriptomics
• Pharmacology
• Bioinformatics

Background:

• Drug-induced transcriptomic data are vital for understanding drug mechanisms, efficacy, and side effects.
• High dimensionality of this data poses analytical challenges.
• Dimensionality reduction (DR) methods simplify complex datasets for analysis and visualization.

Purpose of the Study:

• To evaluate the performance of various DR methods on drug-induced transcriptomic data.
• To identify DR methods best suited for different aspects of transcriptomic analysis, such as distinguishing drug responses and detecting dose-dependent effects.

Main Methods:

• Tested multiple DR methods (t-SNE, UMAP, PaCMAP, TRIMAP, Spectral, PHATE, etc.) on the Connectivity Map (CMap) dataset.
• Utilized data from diverse experimental conditions including cell lines, drugs, mechanisms of action (MOAs), and dosages.
• Assessed methods based on their ability to preserve local and global biological structures and separate distinct biological patterns.

Main Results:

• t-SNE, UMAP, PaCMAP, and TRIMAP demonstrated superior performance in preserving biological structures and differentiating drug responses.
• These top methods effectively grouped drugs with similar molecular targets.
• Most DR methods struggled with subtle, dose-dependent transcriptomic changes, though Spectral, PHATE, and t-SNE showed some promise.
• Standard parameter settings were suboptimal, indicating a need for hyperparameter optimization.

Conclusions:

• The choice of DR method is critical for accurate analysis of drug-induced transcriptomic data.
• t-SNE, UMAP, and PaCMAP are effective for analyzing discrete drug responses.
• Further research and method refinement are necessary for accurately capturing subtle dose-dependent transcriptomic variations.