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Identification of spatial expression trends in single-cell gene expression data

https://doi.org/10.1038/nmeth.4634
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Modelling and Simulation of Stationary Point Processes
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Single-Cell Transcriptomics Reveals that Differentiation and Spatial Signatures Shape Epidermal and Hair Follicle Heterogeneity
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In Situ Transcription Profiling of Single Cells Reveals Spatial Organization of Cells in the Mouse Hippocampus
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Resolving early mesoderm diversification through single-cell expression profiling
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The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
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Revealing the vectors of cellular identity with single-cell genomics
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Accounting for technical noise in single-cell RNA-seq experiments
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Single-cell in situ RNA profiling by sequential hybridization
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Bayesian approach to single-cell differential expression analysis
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Diffusion pseudotime robustly reconstructs lineage branching
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Detecting Dependence Between Marks and Locations of Marked Point Processes
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On estimators for the reduced second moment measure of point processes
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STATISTICS FOR SPATIAL DATA
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Spatially resolved, highly multiplexed RNA profiling in single cells
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Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
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KLF6-SV1 Drives Breast Cancer Metastasis and Is Associated with Poor Survival
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Transforming Growth Factor-β (TGF-β)–Inducible Gene TMEPAI Converts TGF-β from a Tumor Suppressor to a Tumor Promoter in Breast Cancer
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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data
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Identifying multicellular spatiotemporal organization of cells with SpaceFlow
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Identification of spatially variable genes with graph cuts
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Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease
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nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes
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Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry
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Three-dimensional molecular architecture of mouse organogenesis
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Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues
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Dimension-agnostic and granularity-based spatially variable gene identification using BSP
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PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics
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Simulating multiple variability in spatially resolved transcriptomics with scCube
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A unified model-based framework for doublet or multiplet detection in single-cell multiomics data
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HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics
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Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data
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Prioritizing perturbation-responsive gene patterns using interpretable deep learning
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Finding spatially variable ligand-receptor interactions with functional support from downstream genes
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Challenges in unsupervised clustering of single-cell RNA-seq data
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Integrative single-cell analysis
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RNA sequencing: the teenage years
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Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics
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Bioinformatics in urology — molecular characterization of pathophysiology and response to treatment
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Robust decomposition of cell type mixtures in spatial transcriptomics
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Spatial components of molecular tissue biology
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DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data
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High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE
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Mapping isoforms and regulatory mechanisms from spatial transcriptomics data with SPLISOSM
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Gene expression atlas of a developing tissue by single cell expression correlation analysis
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Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies
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SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network
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Cell type-specific inference of differential expression in spatial transcriptomics
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Mapping the topography of spatial gene expression with interpretable deep learning
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Modeling Spatial Correlation of Transcripts with Application to Developing Pancreas
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Probing Notch1-Dll4 signaling in regulating osteogenic differentiation of human mesenchymal stem cells using single cell nanobiosensor
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A universal tool for predicting differentially active features in single-cell and spatial genomics data
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Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models
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Detecting significant expression patterns in single-cell and spatial transcriptomics with a flexible computational approach
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Multiplex bioimaging of single-cell spatial profiles for precision cancer diagnostics and therapeutics
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Spatially visualized single-cell pathology of highly multiplexed protein profiles in health and disease
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Deciphering tissue structure and function using spatial transcriptomics
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Single-cell and spatial transcriptomic profiling reveals distinct tumor microenvironment dynamics in cervical adenocarcinoma and squamous cell carcinoma
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SEPAR enables spatial metagene discovery and associated molecular pattern characterization in spatial transcriptomics and multi-omics datasets
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HarveST uses a heterogeneous graph learning framework to reveal spatial transcriptomics patterns
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Image-activated cell sorting
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Long non-coding RNA (lncRNA) MALAT1 in regulating osteogenic and adipogenic differentiation using a double-stranded gapmer locked nucleic acid nanobiosensor
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Space: the final frontier — achieving single-cell, spatially resolved transcriptomics in plants
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Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing
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Model-free prediction test with application to genomics data
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Spatial Patterning Analysis of Cellular Ensembles (SPACE) finds complex spatial organization at the cell and tissue levels
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Single-cell sequencing: expansion, integration and translation
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Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies
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Critical downstream analysis steps for single-cell RNA sequencing data
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Deep learning in spatially resolved transcriptomics: a comprehensive technical view
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A robust statistical approach for finding informative spatially associated pathways
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GAADE: identification spatially variable genes based on adaptive graph attention network
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Probabilistic-graph-based spatial context-aware framework for interpretable spatial omics denoising and augmentation
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Spatial-ZEDNet : a unified spatial transcriptomics framework for detecting differential gene activation and expression
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GALA: a unified landmark-free framework for coarse-to-fine spatial alignment across resolutions and modalities in spatial transcriptomics
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MLN2SVG: domain-aware spatially variable gene detection using contrastive variational autoencoder and multi-level neighbor search
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Biased multi-view contrastive learning with attentive masking for spatial transcriptomic analysis
inferred← citeshttps://doi.org/10.1093/bib/bbag302openalex-snapshot
geneSCOPE: gene spatial co-occurrence of pairwise expression
inferred← citeshttps://doi.org/10.1093/bib/bbag371openalex-snapshot
Variational sparse Gaussian-process method for detecting spatially variable genes and cellular interactions in spatial transcriptomics
inferred← citeshttps://doi.org/10.1093/bib/bbag417openalex-snapshot
Computational analysis in spatial transcriptomics: methods and perspectives
inferred← citeshttps://doi.org/10.1093/bioadv/vbaf285openalex-snapshot
Adding highly variable genes to spatially variable genes can improve cell type clustering performance in spatial transcriptomics data
inferred← citeshttps://doi.org/10.1093/bioinformatics/btaa198openalex-snapshot
Automatic identification of relevant genes from low-dimensional embeddings of single-cell RNA-seq data
inferred← citeshttps://doi.org/10.1093/bioinformatics/btaa769openalex-snapshot
Identifying signaling genes in spatial single-cell expression data
inferred← citeshttps://doi.org/10.1093/bioinformatics/btaa965openalex-snapshot
Quantification of spatial tumor heterogeneity in immunohistochemistry staining images
inferred← citeshttps://doi.org/10.1093/bioinformatics/btab164openalex-snapshot
sepal: identifying transcript profiles with spatial patterns by diffusion-based modeling
inferred← citeshttps://doi.org/10.1093/bioinformatics/btab447openalex-snapshot
A convolutional neural network for common coordinate registration of high-resolution histology images
inferred← citeshttps://doi.org/10.1093/bioinformatics/btab455openalex-snapshot
Bayesian modeling of spatial molecular profiling data via Gaussian process
inferred← citeshttps://doi.org/10.1093/bioinformatics/btab471openalex-snapshot
SOMDE: a scalable method for identifying spatially variable genes with self-organizing map
inferred← citeshttps://doi.org/10.1093/bioinformatics/btab486openalex-snapshot
Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments
inferred← citeshttps://doi.org/10.1093/bioinformatics/btab490openalex-snapshot
Tysserand—fast and accurate reconstruction of spatial networks from bioimages
inferred← citeshttps://doi.org/10.1093/bioinformatics/btab812openalex-snapshot
Detecting spatially co-expressed gene clusters with functional coherence by graph-regularized convolutional neural network
inferred← citeshttps://doi.org/10.1093/bioinformatics/btac457openalex-snapshot
Identification of cell-type-specific spatially variable genes accounting for excess zeros
inferred← citeshttps://doi.org/10.1093/bioinformatics/btad366openalex-snapshot
JUMP: replicability analysis of high-throughput experiments with applications to spatial transcriptomic studies
inferred← citeshttps://doi.org/10.1093/bioinformatics/btae027openalex-snapshot
DESpace : spatially variable gene detection via differential expression testing of spatial clusters
inferred← citeshttps://doi.org/10.1093/bioinformatics/btae356openalex-snapshot
GammaGateR: semi-automated marker gating for single-cell multiplexed imaging
inferred← citeshttps://doi.org/10.1093/bioinformatics/btae611openalex-snapshot
Statistical batch-aware embedded integration, dimension reduction, and alignment for spatial transcriptomics
inferred← citeshttps://doi.org/10.1093/bioinformatics/btaf131openalex-snapshot
Benchmarking algorithms for spatially variable gene identification in spatial transcriptomics
inferred← citeshttps://doi.org/10.1093/bioinformatics/btaf495openalex-snapshot
BISON: bi-clustering of spatial omics data with feature selection
inferred← citeshttps://doi.org/10.1093/bioinformatics/btaf562openalex-snapshot
Spider: a flexible and unified framework for simulating spatial transcriptomics data
inferred← citeshttps://doi.org/10.1093/bioinformatics/btag644openalex-snapshot
STcompare: comparative spatial transcriptomics data analysis of structurally matched tissues to characterize differentially spatially patterned genes
inferred← citeshttps://doi.org/10.1093/biostatistics/kxaf012openalex-snapshot
Addressing the mean–variance relationship in spatially resolved transcriptomics data with spoon
inferred← citeshttps://doi.org/10.1093/genetics/iyac095openalex-snapshot
Statistical analysis of spatially resolved transcriptomic data by incorporating multiomics auxiliary information
inferred← citeshttps://doi.org/10.1093/gigascience/giag006openalex-snapshot
SpaceBF: spatial coexpression analysis using Bayesian fused approaches in spatial omics datasets
inferred← citeshttps://doi.org/10.1093/gpbjnl/qzae057openalex-snapshot
Computational Strategies and Algorithms for Inferring Cellular Composition of Spatial Transcriptomics Data
inferred← citeshttps://doi.org/10.1093/jnci/djz208openalex-snapshot
Unraveling Triple-Negative Breast Cancer Tumor Microenvironment Heterogeneity: Towards an Optimized Treatment Approach
inferred← citeshttps://doi.org/10.1093/nar/gkaa312openalex-snapshot
dotdotdot: an automated approach to quantify multiplex single molecule fluorescent in situ hybridization (smFISH) images in complex tissues
inferred← citeshttps://doi.org/10.1093/nar/gkab095openalex-snapshot
Discovery of molecular features underlying the morphological landscape by integrating spatial transcriptomic data with deep features of tissue images
inferred← citeshttps://doi.org/10.1093/nar/gkac889openalex-snapshot
SPASCER: spatial transcriptomics annotation at single-cell resolution
inferred← citeshttps://doi.org/10.1093/nar/gkad1023openalex-snapshot
Probabilistic cell/domain-type assignment of spatial transcriptomics data with SpatialAnno
inferred← citeshttps://doi.org/10.1093/nar/gkad801openalex-snapshot
STAMarker: determining spatial domain-specific variable genes with saliency maps in deep learning
inferred← citeshttps://doi.org/10.1093/nar/gkae962openalex-snapshot
Spatial pattern and differential expression analysis with spatial transcriptomic data
inferred← citeshttps://doi.org/10.1093/nar/gkz934openalex-snapshot
SpatialDB: a database for spatially resolved transcriptomes
inferred← citeshttps://doi.org/10.1097/bs9.0000000000000141openalex-snapshot
Spatially resolved transcriptomics: advances and applications
inferred← citeshttps://doi.org/10.1101/180273openalex-snapshot
cytoNet: Spatiotemporal Network Analysis of Cell Communities
inferred← citeshttps://doi.org/10.1101/2020.03.04.977140openalex-snapshot
Polar Gini Curve: a Technique to Discover Single-cell Biomarker Using 2D Visual Information
inferred← citeshttps://doi.org/10.1101/2020.08.11.245472openalex-snapshot
Mapping multicellular programs from single-cell profiles
inferred← citeshttps://doi.org/10.1101/2020.11.16.385377openalex-snapshot
Tysserand - Fast and accurate reconstruction of spatial networks from bioimages
inferred← citeshttps://doi.org/10.1101/2020.11.30.405118openalex-snapshot
Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network
inferred← citeshttps://doi.org/10.1101/2021.06.23.449615openalex-snapshot
A Bayesian Multivariate Mixture Model for Spatial Transcriptomics Data
inferred← citeshttps://doi.org/10.1101/2021.08.27.457741openalex-snapshot
Benchmarking Computational Integration Methods for Spatial Transcriptomics Data
inferred← citeshttps://doi.org/10.1101/2022.02.04.479191openalex-snapshot
SpatialCorr: Identifying Gene Sets with Spatially Varying Correlation Structure
inferred← citeshttps://doi.org/10.1101/2022.02.09.479510openalex-snapshot
SPRI: Spatial Pattern Recognition using Information based method for spatial gene expression data
inferred← citeshttps://doi.org/10.1101/2022.05.07.491043openalex-snapshot
Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace
inferred← citeshttps://doi.org/10.1101/2022.05.16.492124openalex-snapshot
nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes
inferred← citeshttps://doi.org/10.1101/2022.05.24.493189openalex-snapshot
SPIRAL: Significant Process InfeRence ALgorithm for single cell RNA-sequencing and spatial transcriptomics
inferred← citeshttps://doi.org/10.1101/2022.12.07.519417openalex-snapshot
Spateo: multidimensional spatiotemporal modeling of single-cell spatial transcriptomics
inferred← citeshttps://doi.org/10.1101/2023.03.10.532127openalex-snapshot
Spatial Transcriptomics Arena (STAr): an Integrated Platform for Spatial Transcriptomics Methodology Research
inferred← citeshttps://doi.org/10.1101/2023.03.17.533200openalex-snapshot
Statistical modeling and analysis of multiplexed imaging data
inferred← citeshttps://doi.org/10.1101/2023.03.21.533713openalex-snapshot
Dimension-agnostic and granularity-based spatially variable gene identification
inferred← citeshttps://doi.org/10.1101/2023.05.30.542607openalex-snapshot
STAREG: an empirical Bayesian approach to detect replicable spatially variable genes in spatial transcriptomic studies
inferred← citeshttps://doi.org/10.1101/2023.12.02.569717openalex-snapshot
Benchmarking computational methods to identify spatially variable genes and peaks
inferred← citeshttps://doi.org/10.1101/2023.12.08.570837openalex-snapshot
Spatial Patterning Analysis of Cellular Ensembles (SPACE) discovers complex spatial organization at the cell and tissue levels
inferred← citeshttps://doi.org/10.1101/2024.03.04.583438openalex-snapshot
DenoiseST: A dual-channel unsupervised deep learning-based denoising method to identify spatial domains and functionally variable genes in spatial transcriptomics
inferred← citeshttps://doi.org/10.1101/2024.04.08.588586openalex-snapshot
Spatial motifs reveal patterns in cellular architecture of complex tissues
inferred← citeshttps://doi.org/10.1101/2024.05.06.592851openalex-snapshot
scBSP: A fast and accurate tool for identifying spatially variable genes from spatial transcriptomic data
inferred← citeshttps://doi.org/10.1101/2024.06.13.598641openalex-snapshot
Identification of perturbation-responsive regions and genes in comparative spatial transcriptomics atlases
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