Spatial mutual nearest neighbors for spatial transcriptomics data.

ABSTRACT:

Motivation

Mutual nearest neighbors (MNN) is a widely used computational tool to perform batch correction for single-cell RNA-sequencing data. However, in applications such as spatial transcriptomics, it fails to take into account the 2D spatial information.

Results

Here, we present spatialMNN, an algorithm that integrates multiple spatial transcriptomic samples and identifies spatial domains. Our approach begins by building a k-nearest neighbors (kNN) graph based on the spatial coordinates, prunes noisy edges, and identifies niches to act as anchor points for each sample. Next, we construct a MNN graph across the samples to identify similar niches. Finally, the spatialMNN graph can be partitioned using existing algorithms, such as the Louvain algorithm to predict spatial do

SUBMITTER: Zhou H 

PROVIDER: S-EPMC12317746 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature