ArticleiScience2023
ADEPT: Autoencoder with differentially expressed genes and imputation for robust spatial transcriptomics clustering.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Benchmarking cell type annotation in spatial transcriptomics: resolving cellular hierarchies, biological fidelity, and dynamic cell states.Research square · 2026Article
- Benchmarking cell type annotation in spatial transcriptomics: resolving cellular hierarchies, biological fidelity, and dynamic cell states.bioRxiv : the preprint server for biology · 2026Article
- JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- SpaLSTF: Diffusion-based generative model with BiLSTM and XCA-Transformer for spatial transcriptomics imputation.PLoS computational biology · 2026Article
- Spatial information matters: are traditional imputation methods effective for spatial transcriptomics data?Briefings in bioinformatics · 2026Article
- From features to slice: parameter-cloud modeling of spatial transcriptomics for simulation and 3D interpolatory augmentation.bioRxiv : the preprint server for biology · 2025Article
- BioLACE: unifying spatial geometry and marker priors for cohesive cell-type clustering in spatial transcriptomics.bioRxiv : the preprint server for biology · 2025Article
- JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.Advances in neural information processing systems · 2025Article
- MaskGraphene: an advanced framework for interpretable joint representation for multi-slice, multi-condition spatial transcriptomics.Genome biology · 2025Article
- Review
- SCGclust: Single Cell Graph clustering using graph autoencoders integrating SNVs and CNAs.bioRxiv : the preprint server for biology · 2025Article
- A composite scaling network of EfficientNet for improving spatial domain identification performance.Communications biology · 2024Article
- Benchmarking clustering, alignment, and integration methods for spatial transcriptomics.Genome biology · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
Advancements in spatial transcriptomics (ST) have enabled an in-depth understanding of complex tissues by quantifying gene expression at spatially localized spots. Several notable clustering methods have been introduced to utilize both spatial and transcriptional information in the analysis of ST datasets. However, data quality across different ST sequencing techniques and types of datasets influence the performance of different methods and benchmarks. To harness spatial context and transcriptional profile in ST data, we developed a graph-based, multi-stage framework for robust clustering, called ADEPT. To control and stabilize data quality, ADEPT relies on a graph autoencoder backbone and performs an iterative clustering on imputed, differentially expressed genes-based matrices to minimize the variance of clustering results. ADEPT outperformed other popular methods on ST data generated by different platforms across analyses such as spatial domain identification, visualization, spatial trajectory inference, and data denoising.
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