Denoising image-based spatial transcriptomics data with DenoIST.

ABSTRACT: Image-based spatial transcriptomics (IST) technologies provide unprecedented resolution of gene expression in tissue sections, but suffer from contamination of cells' gene expression profiles due to imperfect cell segmentation. We present DenoIST (Denoising Image-based Spatial Transcriptomics data), a new computational tool that accurately identifies and removes contaminating transcripts from IST datasets. DenoIST models the observed transcript counts using a Poisson mixture model that explicitly accounts for local neighbourhood contamination. Applied to multiple real IST datasets of varying cell densities, DenoIST restores gene expression specificity and clarifies local biological structures by identifying and filtering transcripts spilt over from neighbouring cells. The denoised data ena

SUBMITTER: Kwok AWC 

PROVIDER: S-EPMC12642576 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature