{"ID":2871892,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.13344","arxiv_id":"2509.13344","title":"Benchmarking Dimensionality Reduction Techniques for Spatial Transcriptomics","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 ($ρ$=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.","short_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 cl...","url_abs":"https://arxiv.org/abs/2509.13344","url_pdf":"https://arxiv.org/pdf/2509.13344v1","authors":"[\"Md Ishtyaq Mahmud\",\"Veena Kochat\",\"Suresh Satpati\",\"Jagan Mohan Reddy Dwarampudi\",\"Kunal Rai\",\"Tania Banerjee\"]","published":"2025-09-12T17:27:34Z","proceeding":"q-bio.GN","tasks":"[\"q-bio.GN\",\"cs.LG\"]","methods":"[\"Variational Autoencoder\"]","has_code":false}