GCAN: A data feature learning method for scRNA-seq data.
TL;DR
GCAN establishes a new paradigm for scRNA-seq analysis by unifying adaptive feature selection with context-aware relational modeling and implements adaptive screening of biologically relevant features and enables accurate cell typing.
Abstract
INTRODUCTION Single-cell RNA sequencing (scRNA-seq) data exhibit extreme sparsity, technical noise, and complex nonlinear structures that obstruct accurate biological interpretation. Current methods inadequately model intercellular relationships and suffer from feature selection instability.
Purpose
(1) Automatically identify biologically relevant features in noisy scRNA-seq data,(2) Model multi-scale cellular relationships for robust clustering,(3) Provide interpretable representations for mechanistic insights. METHODOLOGY Adaptive HVG selection: Improved Random Forest to mitigate dropout artifacts. Hybrid graph autoencoder: Fusion of Graph Attention Networks (local interactions) and Graph Convolutional Networks (global neighborhoods). Biologically informed optimization: MMD regularization + Spearman-correlation feature filtering.
Results
Evaluated across 17 scRNA-seq datasets: Showed better clustering performance than CellVGAE in our experiments (Silhouette Coefficient and Davies-Bouldin Index),Selection of highly variable genes mitigated technical noise while enhancing biological signal retention.
Conclusion
GCAN establishes a new paradigm for scRNA-seq analysis by unifying adaptive feature selection with context-aware relational modeling. Its architecture implements adaptive screening of biologically relevant features and enables accurate cell typing.