Toward Convex Manifolds: A Geometric Perspective for Deep Graph Clustering of Single-cell RNA-seq Data
Toward Convex Manifolds: A Geometric Perspective for Deep Graph Clustering of Single-cell RNA-seq Data
Nairouz Mrabah, Mohamed Mahmoud Amar, Mohamed Bouguessa, Abdoulaye Banire Diallo
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
Main Track. Pages 4855-4863.
https://doi.org/10.24963/ijcai.2023/540
The deep clustering paradigm has shown great potential for discovering complex patterns that can reveal cell heterogeneity in single-cell RNA sequencing data. This paradigm involves two training phases: pretraining based on a pretext task and fine-tuning using pseudo-labels. Although current models yield promising results, they overlook the geometric distortions that regularly occur during the training process. More precisely, the transition between the two phases results in a coarse flattening of the latent structures, which can deteriorate the clustering performance. In this context, existing methods perform euclidean-based embedding clustering without ensuring the flatness and convexity of the latent manifolds. To address this problem, we incorporate two mechanisms. First, we introduce an overclustering loss to flatten the local curves. Second, we propose an adversarial mechanism to adjust the global geometric configuration. The second mechanism gradually transforms the latent structures into convex ones. Empirical results on a variety of gene expression datasets show that our model outperforms state-of-the-art methods.
Keywords:
Multidisciplinary Topics and Applications: MDA: Bioinformatics
Machine Learning: ML: Clustering
Machine Learning: ML: Unsupervised learning