空间转录组学数据的空间相互最近邻算法

Spatial mutual nearest neighbors for spatial transcriptomics data.

作者信息

Zhou Haowen, Panwar Pratibha, Guo Boyi, Hallinan Caleb, Ghazanfar Shila, Hicks Stephanie C

机构信息

Bioinformatics and Systems Biology Graduate Program, University of California San Diego, Gilman, CA 92093, United States.

School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia.

出版信息

Bioinformatics. 2025 Aug 2;41(8). doi: 10.1093/bioinformatics/btaf403.

DOI:10.1093/bioinformatics/btaf403

PMID:40713795

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12317746/

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 domains across the tissue samples. We demonstrate the performance of spatialMNN using large datasets, including one with N = 31 10x Genomics Visium samples. We also evaluate the computing performance of spatialMNN to other popular spatial clustering methods.

AVAILABILITY AND IMPLEMENTATION

Our software package is available on GitHub (https://github.com/Pixel-Dream/spatialMNN). The code is available on Zenodo (https://doi.org/10.5281/zenodo.15073963).

摘要

动机

相互最近邻（MNN）是一种广泛用于对单细胞RNA测序数据进行批次校正的计算工具。然而，在空间转录组学等应用中，它未能考虑二维空间信息。

结果

在此，我们提出了spatialMNN，一种整合多个空间转录组样本并识别空间域的算法。我们的方法首先基于空间坐标构建k近邻（kNN）图，修剪噪声边，并识别小生境作为每个样本的锚点。接下来，我们跨样本构建MNN图以识别相似的小生境。最后，可以使用现有算法（如Louvain算法）对spatialMNN图进行划分，以预测组织样本中的空间域。我们使用大型数据集展示了spatialMNN的性能，包括一个包含N = 31个10x Genomics Visium样本的数据集。我们还评估了spatialMNN与其他流行的空间聚类方法的计算性能。