Biometrics. 2026 Jan 6;82(1):ujag046. doi: 10.1093/biomtc/ujag046.
ABSTRACT
Single-cell RNA sequencing (scRNA-seq) allows transcriptional profiling, and cell-type annotation of individual cells. However, sample preparation in typical scRNA-seq experiments often homogenizes the samples, thus spatial locations of individual cells are often lost. Although spatial transcriptomic techniques, such as in situ hybridization (ISH) or Slide-seq, can be used to measure gene expression in specific locations in samples, it remains a challenge to measure or infer expression level for every gene at a single-cell resolution in every location in tissues. Existing computational methods show promise in reconstructing these missing data by integrating scRNA-seq data with spatial expression data such as those obtained from spatial transcriptomics. Here we describe Laplacian Linear Optimal Transport (LLOT), an interpretable method to integrate single-cell and spatial transcriptomics data to reconstruct missing information at a whole-genome and single-cell resolution. LLOT iteratively corrects platform effects and employs Laplacian Optimal Transport to decompose each spot in spatial transcriptomics data into a spatially-smooth probabilistic mixture of single cells. We benchmark LLOT against several existing methods on multiple datasets from different measurement technologies, including in situ hybridization, Slide-seq, 10x Visium, and Visium HD. The results demonstrate that LLOT provides an interpretable and versatile framework for reconstructing spatial gene expression and inferring cell locations.
PMID:41885893 | DOI:10.1093/biomtc/ujag046
