Denoising spatially resolved transcriptomics with consistency of heterogeneous spatial coordinates, transcription, and morphology.

ABSTRACT: Spatially resolved transcriptomics (SRT) simultaneously captures spatial coordinates, pathological features, and transcriptional profiles of cells within intact tissues, offering unprecedented opportunities to explore tissue architecture. However, SRT data often suffer from substantial technical noise introduced by experimental procedures, posing challenges for downstream analyses. To overcome these challenges, we introduce a Multiview Denoising framework for Spatial Transcriptomics (MvDST), which integrates a deep autoencoder and self-supervised learning to jointly reconstruct expression profiles, denoise features, and enforce cross-view consistency, effectively reducing technical noise, and heterogeneity. As a result, MvDST reliably and accurately delineates tissue subgroups across simul

SUBMITTER: Wang H 

PROVIDER: S-EPMC12496014 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature