Clustering by Denoising: Latent plug-and-play diffusion for single-cell data

TL;DR

This paper introduces a novel latent diffusion framework for single-cell RNA sequencing data that improves clustering accuracy by effectively denoising data while quantifying uncertainty, outperforming existing methods on synthetic and real datasets.

Contribution

The paper presents a new latent plug-and-play diffusion approach with input-space steering, enabling adaptive denoising, uncertainty quantification, and better clustering in noisy single-cell data.

Findings

Enhanced clustering accuracy on synthetic data across noise levels.

Improved biological coherence and marker alignment in real single-cell datasets.

Robustness to dataset shifts and noise through the proposed denoising framework.

Abstract

Single-cell RNA sequencing (scRNA-seq) enables the study of cellular heterogeneity. Yet, clustering accuracy, and with it downstream analyses based on cell labels, remain challenging due to measurement noise and biological variability. In standard latent spaces (e.g., obtained through PCA), data from different cell types can be projected close together, making accurate clustering difficult. We introduce a latent plug-and-play diffusion framework that separates the observation and denoising space. This separation is operationalized through a novel Gibbs sampling procedure: the learned diffusion prior is applied in a low-dimensional latent space to perform denoising, while to steer this process, noise is reintroduced into the original high-dimensional observation space. This unique "input-space steering" ensures the denoising trajectory remains faithful to the original data structure. Our approach offers three key advantages: (1) adaptive noise handling via a tunable balance between prior and observed data; (2) uncertainty quantification through principled uncertainty estimates for downstream analysis; and (3) generalizable denoising by leveraging clean reference data to denoise noisier datasets, and via averaging, improve quality beyond the training set. We evaluate robustness on both synthetic and real single-cell genomics data. Our method improves clustering accuracy on synthetic data across varied noise levels and dataset shifts. On real-world single-cell data, our method demonstrates improved biological coherence in the resulting cell clusters, with cluster boundaries that better align with known cell type markers and developmental trajectories.