LLOT: application of Laplacian Linear Optimal Transport in spatial transcriptome reconstruction

TL;DR

LLOT is a novel interpretable computational method that integrates single-cell RNA sequencing with spatial transcriptomics to accurately reconstruct spatial gene expression at single-cell resolution, outperforming existing methods.

Contribution

This paper introduces LLOT, a new Laplacian Linear Optimal Transport-based approach for spatial transcriptome reconstruction that corrects platform effects and decomposes spatial data into single-cell components.

Findings

LLOT outperforms existing methods in reconstructing spatial gene expression.

LLOT effectively corrects platform effects in spatial transcriptomics data.

LLOT demonstrates robust performance across multiple datasets.

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 benchmarked LLOT against several other methods on datasets of Drosophila embryo, mouse cerebellum and synthetic datasets generated by scDesign3 in the paper, and another three datasets in the supplementary. The results showed that LLOT consistently outperformed others in reconstructing spatial expressions.