TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses.

ABSTRACT: Whole-transcriptome spatial profiling of genes at single-cell resolution remains a challenge. To address this limitation, spatial gene expression prediction methods have been developed to infer the spatial expression of unmeasured transcripts, but the quality of these predictions can vary greatly. Here we present TISSUE (Transcript Imputation with Spatial Single-cell Uncertainty Estimation) as a general framework for estimating uncertainty for spatial gene expression predictions and providing uncertainty-aware methods for downstream inference. Across eleven benchmark datasets, TISSUE provides well-calibrated prediction intervals for predicted expression values. Moreover it consistently reduces false discovery rates for differential gene expression analysis, improves clustering and visualiz

SUBMITTER: Sun ED 

PROVIDER: S-EPMC10168375 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature