B1922
Title: SPROD: De-noising spatial transcriptomics data based on position and image information
Authors: Yunguan Wang - UT Southwestern Medical Center (United States)
Bing Song - University of Texas Southwestern Medical Center (United States) [presenting]
Shidan Wang - University of Texas Southwestern Medical Center (United States)
mingyi Chen - University of Texas Southwestern Medical Center (United States)
Yang Xie - University of Texas Southwestern Medical Center (United States)
Guanghua Xiao - University of Texas Southwestern Medical Center (United States)
Li Wang - University of Texas at Arlington (United States)
Tao Wang - UT Southwestern Medical Center (United States)
Abstract: Spatially resolved transcriptomics (SRT) provides 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. We develop Sprod, based on latent graph learning of matched location and imaging data, to impute accurate SRT gene expression. We validate Sprod comprehensively and demonstrate its advantages over previous methods for removing drop-outs in single-cell RNA-sequencing data. We show that, after imputation by Sprod, differential expression analyses, pathway enrichment, and cell-to-cell interaction inferences are more accurate. Overall, we envision de-noising by Sprod to become a key first step towards empowering SRT technologies for biomedical discoveries.
