# Benchmarking Dimensionality Reduction Techniques for Spatial Transcriptomics

arXiv: https://arxiv.org/abs/2509.13344 · HTML: https://mlsanity.com/paper/2509.13344/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 23 references that are in the map.
- First posted on arXiv 12 Sep 2025.

## Builds on (most cited first, 1 of 1)

- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)

## Built on by (most cited first, 1 of 1)

- [hSNMF: Hybrid Spatially Regularized NMF for Image-Derived Spatial Transcriptomics](https://mlsanity.com/paper/2602.02638/) (2026)

## Abstract (arXiv)

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.