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arXiv · 2509.13344

Benchmarking Dimensionality Reduction Techniques for Spatial Transcriptomics

Md Ishtyaq Mahmud·Veena Kochat·Suresh Satpati·Jagan Mohan Reddy Dwarampudi·Kunal Rai·Tania Banerjee
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Abstract

We introduce a unified framework for evaluating dimensionality reduction techniques in spatial transcriptomics beyond standard PCA approaches. We benchmark six methods PCA, NMF, autoencoder, VAE, and two hybrid embeddings on a cholangiocarcinoma Xenium dataset, systematically varying latent dimensions ($k$=5-40) and clustering resolutions ($\rho$=0.1-1.2). Each configuration is evaluated using complementary metrics including reconstruction error, explained variance, cluster cohesion, and two novel biologically-motivated measures: Cluster Marker Coherence (CMC) and Marker Exclusion Rate (MER). Our results demonstrate distinct performance profiles: PCA provides a fast baseline, NMF maximizes marker enrichment, VAE balances reconstruction and interpretability, while autoencoders occupy a middle ground. We provide systematic hyperparameter selection using Pareto optimal analysis and demonstrate how MER-guided reassignment improves biological fidelity across all methods, with CMC scores improving by up to 12\% on average. This framework enables principled selection of dimensionality reduction methods tailored to specific spatial transcriptomics analyses.

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Md Ishtyaq Mahmud, Veena Kochat, Suresh Satpati, Jagan Mohan Reddy Dwarampudi, Kunal Rai, Tania Banerjee. 2025-09-12. Benchmarking Dimensionality Reduction Techniques for Spatial Transcriptomics. https://arxiv.org/abs/2509.13344

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