Sprod for de-noising spatially resolved transcriptomics data based on position and image information.

ABSTRACT: Spatially resolved transcriptomics (SRT) provide gene expression close to, or even superior to, single-cell resolution while retaining the physical locations of sequencing and often also providing matched pathology images. However, SRT expression data suffer from high noise levels, due to the shallow coverage in each sequencing unit and the extra experimental steps required to preserve the locations of sequencing. Fortunately, such noise can be removed by leveraging information from the physical locations of sequencing, and the tissue organization reflected in corresponding pathology images. In this work, we developed Sprod, based on latent graph learning of matched location and imaging data, to impute accurate SRT gene expression. We validated Sprod comprehensively and demonstrated its ad

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10229080 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature