Detecting spatially co-expressed gene clusters with functional coherence by graph-regularized convolutional neural network

Tianci Song, Kathleen Markham, Zhuliu Li, Kristen E. Muller, Kathleen Greenham, Rui Kuang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


MOTIVATION: Clustering spatial-resolved gene expressions is an essential analysis to reveal gene activities in the underlying morphological context by their functional roles. However, conventional clustering analysis does not consider gene expression co-localizations in tissue for detecting spatial expression patterns or functional relationships among the genes for biological interpretation in the spatial context. In this paper, we present a Convolutional Neural Network (CNN) regularized by the graph of Protein-Protein Interaction (PPI) network to cluster spatially-resolved gene expressions. This method improves the coherence of spatial patterns and provides biological interpretation of the gene clusters in the spatial context by exploiting the spatial localization by convolution and gene functional relationships by graph-Laplacian regularization.

RESULTS: In the experiments, we tested clustering the spatially variable genes or all expressed genes in the transcriptome in 22 Visium spatial transcriptomics datasets of different tissue sections publicly available from 10x Genomics and spatialLIBD. The results demonstrate that the PPI-regularized CNN constantly detects gene clusters with coherent spatial patterns and significantly enriched by gene functions with the-state-of-the-art performance. Additional case studies on mouse kidney tissue and human breast cancer tissue suggest that the PPI-regularized CNN also detects spatially co-expressed genes to define the corresponding morphological context in the tissue with valuable insights.

AVAILABILITY: Source code is available at

Original languageEnglish (US)
Pages (from-to)1344-1352
Number of pages9
Issue number5
StatePublished - Mar 1 2022

Bibliographical note

Publisher Copyright:
© The Author(s) 2021. Published by Oxford University Press. All rights reserved.

PubMed: MeSH publication types

  • Journal Article
  • Research Support, U.S. Gov't, Non-P.H.S.


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