Identification of Spatial Domains, Spatially Variable Genes, and Genetic Association Studies of Alzheimer Disease with an Autoencoder-based Fuzzy Clustering Algorithm

Introduction: Transcriptional gene expressions and their corresponding spatial information are critical for understanding the biological function, mutual regulation, and identification of various cell types. Materials and Methods: Recently, several computational methods have been proposed for clustering using spatial transcriptional expression. Although these algorithms have certain practicability, they cannot utilize spatial information effectively and are highly sensitive to noise and outliers. In this study, we propose ACSpot, an autoencoder-based fuzzy clustering algorithm, as a solution to tackle these problems. Specifically, we employed a self-supervised autoencoder to reduce feature dimensionality, mitigate nonlinear noise, and learn high-quality representations. Additionally, a commonly used clustering method, Fuzzy c-means, is used to achieve improved clustering results. In particular, we utilize spatial neighbor information to optimize the clustering process and to fine-tune each spot to its associated cluster category using probabilistic and statistical methods. Result and Discussion: The comparative analysis on the 10x Visium human dorsolateral prefrontal cortex (DLPFC) dataset demonstrates that ACSpot outperforms other clustering algorithms. Subsequently, spatially variable genes were identified based on the clustering outcomes, revealing a striking similarity between their spatial distribution and the subcluster spatial distribution from the clustering results. Notably, these spatially variable genes include APP, PSEN1, APOE, SORL1, BIN1, and PICALM, all of which are well-known Alzheimer's disease-associated genes. Conclusion: In addition, we applied our model to explore some potential Alzheimer's disease correlated genes within the dataset and performed Gene Ontology (GO) enrichment and gene-pathway analyses for validation, illustrating the capability of our model to pinpoint genes linked to Alzheimer's disease.