Latest Research in Single-cell and spatial transcriptomics
45 research papers · 2026 median publication year
Top Research Topics in Single-cell and spatial transcriptomics
- Single-cell and spatial transcriptomics — 18 papers
- Computer Vision and Pattern Recognition — 8 papers
- Cell Image Analysis Techniques — 3 papers
- DNA Repair Mechanisms — 3 papers
- Genomics — 2 papers
- Machine Learning — 1 papers
- Quantitative Methods — 1 papers
- Genomics and Chromatin Dynamics — 1 papers
- Cell Behavior — 1 papers
- Arsenic contamination and mitigation — 1 papers
Highest-Cited Papers
- ReScale4DL: balancing pixel and contextual information for enhanced bioimage segmentation
- Domain Elastic Transform: Bayesian Function Registration for High-Dimensional Scientific Data
- STCGCar: Graph Contrastive Learning with Reliable Augmentation for Spatial Transcriptomics Clustering
- CrossBranch: cross-domain cell-type deconvolution with dual-branch representation learning
- Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation
- Unifying multimodal single-cell data with a mixture-of-experts β-variational autoencoder framework
- SpaMOAL is a deep learning method that enables accurate spatial domain identification from multi-omics data
- Dual-contrastive learning for spatial domain identification in spatial transcriptomics with STAMGC
- FlowLOT: Linearized Optimal Transport for Flow Cytometry Analysis
- scTransMIL bridges patient-level disease states and single-cell transcriptomics for cancer screening and heterogeneity inference
- End-to-End Cell Detection via Instance-aware Graph Modeling
- Hyperbolic Contrastive Learning with Entailment for Spatial Transcriptomics
- Recent advances in deep learning for biological microscopy image analysis beyond segmentation
- Benchmarking three simple DNA staining-based image metrics for live-cell tracking of chromatin organization
- Widespread atypical UV-induced mutations form in single-stranded DNA
- SpCAST enables scalable and interpretable integration of single-cell RNA sequencing and single-cell-resolved spatial transcriptomics
- SkNeXt enables topology-guided neuronal reconstruction from petabyte-scale microscopy data
- Spatially guided translation from histology images to transcriptomic profiles using foundation model-driven contrastive learning
- scGSI: Graph-guided self-supervised integration of paired single-cell multi-omics
- Trivalent arsenicals enhance UVA-associated genomic instability in human keratinocyte models