SUMMARY: Identifying cell states associated with disease progression or experimental perturbations from single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) data is critical for unraveling disease pathogenesis. However, the high dimensionality, extreme sparsity, and nearly binary nature of scATAC-seq data pose significant challenges. Here, we present reDA, a cluster-free computational framework that performs differential abundance testing based on the random walk with restart. Through comprehensive experiments on simulated and real datasets, reDA outperforms six baseline methods, demonstrating superior accuracy, computational efficiency, and the ability to capture disease-specific molecular signatures. AVAILABILITY AND IMPLEMENTATION: The reDA along with detailed documentation is freely available at https://github.com/Jinsl-lab/reDA. It can be seamlessly integrated into existing scATAC-seq analysis workflows.

Spatial transcriptomics is a rapidly developing field of single-cell genomics that quantitatively measures gene expression while providing spatial information within tissues. A key challenge in spatial transcriptomics is identifying spatially structured domains, which involves analyzing transcriptomic data to find clusters of cells with similar expression patterns and their spatial distribution. To address these challenges, we propose a novel deep-learning method called DMGCN for domain identification. The process begins with preprocessing that constructs two types of graphs: a spatial graph based on Euclidean distance and a feature graph based on Cosine distance. These graphs represent spatial positions and gene expressions, respectively. The embeddings of both graphs are generated using a multi-view graph convolutional encoder with an attention mechanism, enabling separate and co-convolution of the graphs, as well as corrupted feature convolution for contrastive learning. Finally, a fully connected network (FCN) decoder is employed to generate domain labels and reconstruct gene expressions for downstream analysis. Experimental results demonstrate that DMGCN consistently outperforms state-of-the-art methods in various tasks, including spatial clustering, trajectory inference, and gene expression broadcasting.