Graph Neural Operators for Classification of Spatial Transcriptomics Data

TL;DR

This paper introduces graph neural operators for classifying tissue types in spatial transcriptomics data, demonstrating improved accuracy and robustness over traditional graph neural networks.

Contribution

It is the first to apply neural operators within graph neural networks for spatial transcriptomics classification tasks.

Findings

Achieved nearly 72% F1 score with graph neural operators.

Outperformed all baseline and other graph network approaches.

Validated the approach on mouse brain tissue samples.

Abstract

The inception of spatial transcriptomics has allowed improved comprehension of tissue architectures and the disentanglement of complex underlying biological, physiological, and pathological processes through their positional contexts. Recently, these contexts, and by extension the field, have seen much promise and elucidation with the application of graph learning approaches. In particular, neural operators have risen in regards to learning the mapping between infinite-dimensional function spaces. With basic to deep neural network architectures being data-driven, i.e. dependent on quality data for prediction, neural operators provide robustness by offering generalization among different resolutions despite low quality data. Graph neural operators are a variant that utilize graph networks to learn this mapping between function spaces. The aim of this research is to identify robust machine learning architectures that integrate spatial information to predict tissue types. Under this notion, we propose a study incorporating various graph neural network approaches to validate the efficacy of applying neural operators towards prediction of brain regions in mouse brain tissue samples as a proof of concept towards our purpose. We were able to achieve an F1 score of nearly 72% for the graph neural operator approach which outperformed all baseline and other graph network approaches.