scE2EGAE: enhancing single-cell RNA-Seq data analysis through an end-to-end cell-graph-learnable graph autoencoder with differentiable edge sampling.
Journal:
Biology direct
Published Date:
May 27, 2025
Abstract
BACKGROUND: Single-cell RNA sequencing (scRNA-Seq) technology reveals biological processes and molecular-level genomic information among individual cells. Numerous computational methods, including methods based on graph neural networks (GNNs), have been developed to enhance scRNA-Seq data analysis. However, existing GNNs-based methods usually construct fixed graphs by applying the k-nearest neighbors algorithm, which may result in information loss.
