ArticleNature communications2024
Detecting anomalous anatomic regions in spatial transcriptomics with STANDS.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- A universal deep learning framework for empowering nanopore identification by reinforcing temporal signals.Nature communications · 2026Article
- STNMAE: Identifying Spatial Domains from Spatial Transcriptomics Data with Neighbor-Aware Multi-view Masked Graph Autoencoder.Interdisciplinary sciences, computational life sciences · 2026Article
- AI-driven multiscale virtual plant cell modeling: from molecular mechanisms to tissue functions.Planta · 2026Review
- Precise excision of expanded GGC repeats in NOTCH2NLC via CRISPR/Cas9 for treating neuronal intranuclear inclusion disease.Nature communications · 2026Article
- Accurate imputation of pathway-specific gene expression in spatial transcriptomics with PASTA.Nature communications · 2025Article
- ST-GCP: a graph convolutional network model with contrastive consistency and permutation for spatial transcriptomics.Briefings in bioinformatics · 2025Article
- Anomaly detection in spatial transcriptomics via spatially localized density comparison.Bioinformatics (Oxford, England) · 2025Article
- GTAT-GRN: a graph topology-aware attention method with multi-source feature fusion for gene regulatory network inference.Frontiers in genetics · 2025Article
- PRTS: Predicting Single-Cell Spatial Transcriptomic Maps from Histological Images.Research (Washington, D.C.) · 2025Article
- Uncovering biomarkers and molecular pathways linking NAFLD and AIS: Insights from bioinformatic analysis and experiment.PloS one · 2025Article
- KSRV: a Kernel PCA-Based framework for inferring spatial RNA velocity at single-cell resolution.Frontiers in genetics · 2025Article
- Integrative Genomic and Single-Cell Insights Into Efferocytosis-Mediated Immune Regulation in Clear Cell Renal Cell Carcinoma.Mediators of inflammation · 2025Article
- Hypergraph-based analysis of weighted gene co-expression hypernetwork.Frontiers in genetics · 2025Article
- A semi-supervised weighted SPCA- and convolution KAN-based model for drug response prediction.Frontiers in genetics · 2025Article
- stGuide advances label transfer in spatial transcriptomics through attention-based supervised graph representation learning.Frontiers in genetics · 2025Article
- Reference-free deconvolution of complex samples based on cross-cell-type differential analysis: Systematic evaluations with various feature selection options.Frontiers in genetics · 2025Article
- MOLUNGN: a multi-omics graph neural network for biomarker discovery and accurate lung cancer classification.Frontiers in genetics · 2025Article
- Detecting anomalous anatomic regions in spatial transcriptomics with STANDS.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detection and Dissection of Anomalous Tissue Domains (DDATD) from multi-sample spatial transcriptomics (ST) data provides unprecedented opportunities to characterize anomalous tissue domains (ATDs), revealing both population-level and individual-specific pathogenic factors for understanding pathogenic heterogeneities behind diseases. However, no current methods can perform de novo DDATD from ST data, especially in the multi-sample context. Here, we introduce STANDS, an innovative framework based on Generative Adversarial Networks which integrates three core tasks in multi-sample DDATD: detecting, aligning, and subtyping ATDs. STANDS incorporates multimodal-learning, transfer-learning, and style-transfer techniques to effectively address major challenges in multi-sample DDATD, including complications caused by unalignable ATDs, under-utilization of multimodal information, and scarcity of normal ST datasets necessary for comparative analysis. Extensive benchmarks from diverse datasets demonstrate STAND's superiority in identifying both common and individual-specific ATDs and further dissecting them into biologically distinct subdomains. STANDS also provides clues to developing ATDs visually indistinguishable from surrounding normal tissues.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.