Journal: Bioinformatics Advances

Article Title: Revealing tissue architecture through the hypercomplex Fourier analysis of spatial transcriptomics data

doi: 10.1093/bioadv/vbaf191

Figure Lengend Snippet: Visualization of 10× Visium HD spatial transcriptomics data for coronal mouse brain slice. The H&E stained image is displayed behind a visualization of the Visium spots colored according to the number of UMI counts.

Article Snippet: To illustrate the proposed quaternion model and the ST visualization, rotation and hypercomplex Fourier analysis applications, we analysed a 10× Visium HD ST dataset generated on a mouse coronal brain slice (results for a mouse brain sagittal Visium dataset, a mouse kidney Visium dataset, and human ovarian cancer Vizgen MERSCOPE dataset are included as vingettes with the QSC R package).

Techniques: Slice Preparation, Staining

Journal: Bioinformatics Advances

Article Title: Revealing tissue architecture through the hypercomplex Fourier analysis of spatial transcriptomics data

doi: 10.1093/bioadv/vbaf191

Figure Lengend Snippet: Visualization of quaternion mapping for coronal mouse brain slice Visium HD data. The left panel shows the SVD model and the right panel shows a mapping based on the rank 20 reconstructed expression of the GFAP, Reln, and Neurod6 genes, which are markers for astrocytes, interneurons and excitatory neurons and are mapped to red, green, and blue respectively.

Article Snippet: To illustrate the proposed quaternion model and the ST visualization, rotation and hypercomplex Fourier analysis applications, we analysed a 10× Visium HD ST dataset generated on a mouse coronal brain slice (results for a mouse brain sagittal Visium dataset, a mouse kidney Visium dataset, and human ovarian cancer Vizgen MERSCOPE dataset are included as vingettes with the QSC R package).

Techniques: Slice Preparation, Expressing

Journal: Nature Communications

Article Title: Reconstructing single-cell resolution from spatial transcriptomics with CellRefiner

doi: 10.1038/s41467-026-70090-2

Figure Lengend Snippet: a Visium ST data from a slice of the mouse cortex. b The distribution of annotated cell types from the scRNA-seq data on the CellRefiner output. c Segmentation of CellRefiner reconstruction of mouse cortex Visium data, using SpaceFlow, with colors corresponding to clusters. d Ripley’s L for spatial organization on Visium and CellRefiner. e Neighborhood enrichment score on spatial proximity of clusters for CellRefiner output. f ST data from murine lymph node using Visium. Source data are provided as a Source Data file.

Article Snippet: The mouse kidney Visium data is available at the 10X Genomics website ( https://www.10xgenomics.com/datasets/mouse-kidney-section-coronal-1-standard-1-1-0 ).

Techniques:

Journal: Nature Communications

Article Title: Reconstructing single-cell resolution from spatial transcriptomics with CellRefiner

doi: 10.1038/s41467-026-70090-2

Figure Lengend Snippet: a The single-cell resolution spatial map of cells reconstructed by CellRefiner using a paired Visium data and scRNA-seq data. The analysis rediscovered several contact-based signaling activities confirmed by prior knowledge, including EPHB, NOTCH, ICAM, and CDH. b CellRefiner also identified several highly active junction-related signaling including EPHA, MPZ, CD39, CD46, DESMOSOME, and JAM. Source data are provided as a Source Data file.

Article Snippet: The mouse kidney Visium data is available at the 10X Genomics website ( https://www.10xgenomics.com/datasets/mouse-kidney-section-coronal-1-standard-1-1-0 ).

Techniques: Single Cell

Journal: iMeta

Article Title: A comprehensive benchmarking for spatially resolved transcriptomics clustering methods across variable technologies, organs, and replicates

doi: 10.1002/imt2.70084

Figure Lengend Snippet: Overview of datasets and methods for benchmarking. (A) Workflow of the benchmarking study. Real datasets collected from public databases, together with simulated datasets generated with SRTsim and scCube, were used to evaluate spatial clustering methods across multiple application scenarios. Fourteen spatial clustering methods, spanning both probabilistic statistics and neural network‐based methods, were compared for accuracy across technologies, organs, biological replicates, and simulated spatial patterns. (B) Summary of real datasets used in the study. Datasets obtained from ST, 10× Visium, Slide‐seq, Stereo‐seq, Visium HD, seqFISH+, STARmap, MERFISH, CosMx, and Xenium technologies are shown, including the number of slices, spatial resolution, number of spots, genes, and sparsity per slice. Bar lengths represent the mean number of spots, and error bars indicate standard deviation.

Article Snippet: The adult mouse kidney (FFPE) 10× Visium data set was downloaded from https://www.10xgenomics.com/resources/datasets/adult-mouse-kidney-ffpe-1-standard-1-3-0 , containing 1 tissue slice.

Techniques: Generated, Standard Deviation

Journal: iMeta

Article Title: A comprehensive benchmarking for spatially resolved transcriptomics clustering methods across variable technologies, organs, and replicates

doi: 10.1002/imt2.70084

Figure Lengend Snippet: Performance comparison with SRT datasets across organs. (A) Ground‐truth annotations for representative slices from each organ in the 10× Visium datasets. (B) Ground‐truth annotations and clustering results from all methods on slice 151676 of the DLPFC 10× Visium data set. (C) Box plots compare methods on all 10× Visium datasets of variable organs with Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Centerline: median; box limits: upper and lower quartiles; whiskers: 1.5× interquartile range. Results of kidney and skin datasets are presented in Figure S16 due to lower confidence in the ground truth. (D) Ground‐truth annotations for representative slices from each organ in the Slide‐seq datasets. (E) Ground truth and clustering result from each method on a representative Hippocampus Slide‐seq slice. (F) Comparison of methods across all Slide‐seq datasets from different organs, with clustering accuracy measured by ARI and NMI. Centerline: median; box limits: upper and lower quartiles; whiskers: 1.5× interquartile range. SRT, spatially resolved transcriptomics.

Article Snippet: The adult mouse kidney (FFPE) 10× Visium data set was downloaded from https://www.10xgenomics.com/resources/datasets/adult-mouse-kidney-ffpe-1-standard-1-3-0 , containing 1 tissue slice.

Techniques: Comparison

Journal: iMeta

Article Title: A comprehensive benchmarking for spatially resolved transcriptomics clustering methods across variable technologies, organs, and replicates

doi: 10.1002/imt2.70084

Figure Lengend Snippet: Performance comparison with SRT datasets across biological replicates. (A) Simulation of variable replicates based on DLPFC 10× Visium datasets. Box plot shows Normalized Mutual Information (NMI) scores on simulated datasets. (B) Simulation of variable replicates based on the hypothalamus MERFISH data set. Box plot shows NMI scores on simulated datasets. (C) Design of simulation for neighborhood‐changing replicates by merging adjacent clusters. Box plot shows NMI scores on simulated datasets. (D) Design of simulation for neighborhood‐changing replicates by adding new clusters. Box plot shows NMI scores on simulated datasets. The box represents the interquartile range, the horizontal line inside the box indicates the median, and the whiskers extend to 1.5× interquartile range. SRT, spatially resolved transcriptomics.

Article Snippet: The adult mouse kidney (FFPE) 10× Visium data set was downloaded from https://www.10xgenomics.com/resources/datasets/adult-mouse-kidney-ffpe-1-standard-1-3-0 , containing 1 tissue slice.

Techniques: Comparison

Journal: Communications Biology

Article Title: SPADE: spatial deconvolution for domain specific cell-type estimation

doi: 10.1038/s42003-024-06172-y

Figure Lengend Snippet: a Original image of Adult Mouse Brain (Coronal) downloaded from 10x Visium. b Detected spatial domain. Colors represent different domains c SPADE inferred the dominant cell type at each location. d Estimated cell type in the mouse visual cortex. Each location is indicated by a composition of several cell types. e 4 subtypes of the excitatory neurons at the mouse visual cortex. f Genes displayed differences in expression within each excitatory neuronal cell subtype at the mouse visual cortex. Source data can be found in Supplementary Data .

Article Snippet: The spatial MOB, mouse kidney, and mouse brain datasets were obtained from the 10x Visium dataset, which can be accessed at https://www.10xgenomics.com/resources/datasets .

Techniques: Expressing

Journal: Nucleic Acids Research

Article Title: CellMap: precision mapping of cellular landscape in spatial transcriptomics

doi: 10.1093/nar/gkaf1484

Figure Lengend Snippet: Benchmark CellMap on the Visium HD data from human CRC. ( A )The UMAP layout depicting the clustering space of human CRC scRNA-seq data (B cells, Endothelial, Fibroblast, Intestinal Epithelial,Myeloid, Neuronal, Smooth Muscle, T cells, and Tumor). The cell types are color-coded, with each dot representing an individual cell. ( B ) Spatial structure of human CRC reconstructed using CellMap. ( C ) Spatial heat maps showing the spatial distribution of nine cell types predicted by CellMap in the Visium HD ST data, with each cell type highlighted in a different color. ( D ) Spatial heat maps showing cell type signature genes score calculated using AddModuleScore in Seurat. The colors from blue to red indicate the scores from low to high. ( E ) Benchmark of CellMap’s performance with different methods. The box plot reflects the overall distribution of Pearson’s correlation calculated for each spot by various method.

Article Snippet: Dataset3 (mouse kidney): 10X Visium, https://www.10xgenomics.com/resources/datasets?query=&page=1&configure%5BhitsPerPage%5D=50&configure%5BmaxValuesPerFacet%5D=1000 ; 10X Visium, GSE171406 in the GEO database [ ]; 10X Chromium, GSE129798 in the GEO database [ ].

Techniques:

Journal: bioRxiv

Article Title: A Comprehensive Benchmarking of Spatial Deconvolution and Domain Detection Methods across Diverse Tissues and Spatial Transcriptomic Technologies

doi: 10.64898/2026.05.11.724248

Figure Lengend Snippet: (a) Schematic representation of the benchmarking workflow for evaluating the performance of 21 cell-type deconvolution methods (i.e. Autogenes, CARD, Cell2location, CellDART, CelloScope, GraphST, Polaris, RCTD, Redeconve, Seurat, SONAR, SpatialDecon, SpatialDWLS, Spotlight, STDeconvolve, Stereoscope, STRIDE, Tangram, DestVI, SD, SpiceMix), categorized by their data requirements and computational strategies, using paired spatial transcriptomics and scRNA-seq datasets. (b) The benchmarking was performed on 37 datasets, spanning cancer, brain, and organ tissues, encompassing different spatial technologies including 10x Visium, Slide-seq V2, 10x Visium HD, and image-based sequencing techniques. c) Overview of simulation strategy 1 and simulation strategy 2 for generation of simulated spatial gene expression and cell type proportion datasets. (d) The benchmarking results were evaluated using a set of five bi-variate spatial and four non-spatial evaluation metrics, five shape characterization metrics and three rare cell-type metrics. Created in BioRender .

Article Snippet: The spatial and scRNA dataset for DLPFC are available at ref ( ) and ( ) respectively, Mouse Brain at ref , Hippocampus at ref , Cerebellum at ref , Visium HD Mouse brain at ref ( ) ( https://www.10xgenomics.com/datasets , scRNA data at ref ( )), Kidney Cancer at ref , Breast Cancer at ref , Liver Cancer at ref ( ) and ( ) respectively, Prostate Cancer at ref , Visium HD Lung Cancer at ref ( ) ( https://www.10xgenomics.com/datasets )and , Developmental Lung at ref , Human Breast Atlas at ref , Intestine at ref , Mouse Liver Atlas at ref , Human Liver Atlas at ref , Kidney Atlas at ref ( ) and Chicken heart at ref ( ).

Techniques: Spatial Transcriptomics, Sequencing, Gene Expression

Journal: bioRxiv

Article Title: A Comprehensive Benchmarking of Spatial Deconvolution and Domain Detection Methods across Diverse Tissues and Spatial Transcriptomic Technologies

doi: 10.64898/2026.05.11.724248

Figure Lengend Snippet: a) Quantitative comparison of deconvolution methods in terms of Composite scores for 10x Visium datasets (red), Large datasets including Slide-seq V2 and Visium HD (green), and datasets generated using binning strategy on imaging-based datasets (purple) b) Quantitative comparison of deconvolution methods in terms of Composite scores for deconvolution tasks involving < = 12 cell types (blue), > 12 < = 21 cell types (yellow) and > 21 cell types (green).

Article Snippet: The spatial and scRNA dataset for DLPFC are available at ref ( ) and ( ) respectively, Mouse Brain at ref , Hippocampus at ref , Cerebellum at ref , Visium HD Mouse brain at ref ( ) ( https://www.10xgenomics.com/datasets , scRNA data at ref ( )), Kidney Cancer at ref , Breast Cancer at ref , Liver Cancer at ref ( ) and ( ) respectively, Prostate Cancer at ref , Visium HD Lung Cancer at ref ( ) ( https://www.10xgenomics.com/datasets )and , Developmental Lung at ref , Human Breast Atlas at ref , Intestine at ref , Mouse Liver Atlas at ref , Human Liver Atlas at ref , Kidney Atlas at ref ( ) and Chicken heart at ref ( ).

Techniques: Comparison, Generated, Imaging

Journal: Nature Communications

Article Title: STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics

doi: 10.1038/s41467-024-51728-5

Figure Lengend Snippet: a Computational resolution enhancement cannot achieve single-cell level. Illustration of the spot layout on the mouse brain hippocampus 10X Visium FFPE spatial transcriptome (left), the original gene expression summary at the spot level (middle), the BayesSpace-imputed gene expression summary at the subspot level along with the enlarged subspots on the H&E image (right). In the enlarged area, the white circle represents the original spot, and the red circle represents the enhanced subspot. b–e Systematic evaluation of spatial relationship between single cells and spots via simulation of high-resolution spots from real 10X Visium spatial transcriptomics FFPE data. b Nuclear morphological feature distribution of mouse kidney, mouse brain and human breast cancer. c Examples of simulated high-resolution spots on the real nuclear segmentation. The brown circle represents the high-resolution spot with 5 μm in diameter, and the black circle represents the nucleus in the real tissue. d The frequency of spots covering different numbers of cells, where the x-axis is the cell count covered by one single spot, and the y-axis represents the spot frequency. Only the spot that covers cells was considered. e The distribution of the cell area fraction covered by the spots in different diameters. In the box plots ( b , e ), center line represents median, lower and upper hinges represent first and third quartiles, whiskers extend from hinge to ±1.5 × IQR. The above distributions are drawn from 172,835 nuclei in mouse kidney, 31,546 nuclei in mouse brain, and 44,218 nuclei in human breast cancer, respectively. Source data are provided as a Source Data file.

Article Snippet: The raw reads and images of 10X ST Visium FFPE for mouse brain, mouse kidney and human breast cancer and the processed gene-level read count matrix and image of 10X CytAssist Spatial Gene Expression (V2 Chemistry) for mouse brain section 1&2 were downloaded from www.10xgenomics.com/resources .

Techniques: Gene Expression, Cell Counting

Journal: Nature Communications

Article Title: STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics

doi: 10.1038/s41467-024-51728-5

Figure Lengend Snippet: a–j Mouse brain hippocampus 10X Visium FFPE spatial transcriptome. a Spot-level cell-type deconvolution using SPOTlight. In the enlarged area, each pie chart represents the proportion of cell types for the corresponding spot. b Subspot-level cell type deconvolution using BayesSpace followed by SPOTlight. In the enlarged area, each pie chart represents the proportion of cell types for the corresponding subspot. c Single-cell level deconvolution by STIE (left panel), which is the aggregation of cells captured by spots (middle panel) and cells missed by spots but recovered by STIE (right panel). In the enlarged area, the circle is the cell contour, with the color representing its cell type. Spot-level cell-type deconvolution using DWLS ( d ), Stereoscope ( e ), RCTD ( f ), Tangram ( g ), and BayesPrism ( h ). i Ground truth of mouse brain hippocampus cell types by In Situ Hybridization (ISH): CA1 ( Mpped1 ), CA2 ( Map3k15 ), CA3 ( Cdh24 ) and DG ( Prox1 ). The arrowhead indicates high expression. The figure is reproduced from Fig. in ref. . j Nuclear morphological feature distributions of cell types learned by STIE (422 CA1, 39 CA2, 115 CA3, 818 DG, and 872 Glia). k Single-cell level deconvolution by STIE on 10X Visium V2 Chemistry CytAssist FFPE spatial transcriptomics of two consecutive mouse brain hippocampus sections: section 1 (up panel) and section 2 (bottom panel). l–n Human breast cancer 10X Visium FFPE spatial transcriptome. l Single-cell level deconvolution by STIE. m Nuclear morphological feature distributions for cell types (2910 Bcells, 12,269 CAFs, 11,957 CancerEpithelial, 3120 Myeloid, 6997 Plasmablasts, 1920 PVL, and 4584 Tcells). Center line represents median, lower and upper hinges represent first and third quartiles, whiskers extend from hinge to ±1.5× IQR. n Manually annotated human breast cancer pathological regions. Single-cell convolution for the simulated high-resolution spatial transcriptomics data of the mouse brain hippocampus ( o ) and human breast cancer ( p ) using spots with 5 μm in diameter. Source data are provided as a Source Data file.

Article Snippet: The raw reads and images of 10X ST Visium FFPE for mouse brain, mouse kidney and human breast cancer and the processed gene-level read count matrix and image of 10X CytAssist Spatial Gene Expression (V2 Chemistry) for mouse brain section 1&2 were downloaded from www.10xgenomics.com/resources .

Techniques: In Situ Hybridization, Expressing

Journal: Nature Communications

Article Title: STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics

doi: 10.1038/s41467-024-51728-5

Figure Lengend Snippet: Cell type specific transcriptomic signature learning from 10X Visium mouse brain hippocampus FFPE ( a ) and 10X Visium human breast cancer FFPE ( b ). Spot-level clustering by K-means, SpaGCN, MUSE, subspot-level clustering by BayesSpace and single-cell-level clustering by STIE on 10X Visium FFPE mouse brain hippocampus ( c ), mouse brain cortex ( e ) and human breast cancer ( g ). Cell type deconvolution of spot-, subspot-, and single-cell-level clustering-derived CAGE in the mouse brain hippocampus ( d ), mouse brain cortex ( f ), and human breast cancer ( h ). For the mouse brain cortex, the cell types in the transcriptomic signature, which are not cortex layers and have small proportions, are not shown in the barplot. The box plot ( h ) represents the deconvoluted proportion of 9 cell types, where center line represents median, lower and upper hinges represent first and third quartiles, and whiskers extend from hinge to ±1.5 × IQR. The p-value was calculated based on one-sided Wilcoxon signed-rank test without adjustment for multiple comparisons. i The UMAP plot of human breast cancer scRNA-seq data from 26 primary tumors . The top panel is the original cell typing of 10,060 single cells, and the bottom panel is the subset of cells that are mapped to the six STIE clusters. Spot-level clustering by K-means (left), SpaGCN (middle), and single-cell-level clustering by STIE (right) on the simulated high-resolution spot spatial transcriptome data of the mouse brain hippocampus ( j ) and human breast cancer ( m ). Cell type deconvolution of spot- and single-cell-level clustering-derived CAGE in the mouse brain hippocampus ( k ) and human breast cancer ( n ). l , o The consistency table of single-cell clusters between the simulated high-resolution spot-based STIE clustering and the original low-resolution spot-based STIE clustering as ground truth of the mouse brain hippocampus ( c ) and human breast cancer ( g ). Source data are provided as a Source Data file.

Article Snippet: The raw reads and images of 10X ST Visium FFPE for mouse brain, mouse kidney and human breast cancer and the processed gene-level read count matrix and image of 10X CytAssist Spatial Gene Expression (V2 Chemistry) for mouse brain section 1&2 were downloaded from www.10xgenomics.com/resources .

Techniques: Derivative Assay

Journal: Nature Communications

Article Title: STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics

doi: 10.1038/s41467-024-51728-5

Figure Lengend Snippet: Identification of the bona fide area captured by spots in the mouse brain hippocampus ( a ) and human breast cancer ( b ). The x-axis represents the putative size of the bona fide area measured by the spot in the unit of a regular 10X Visium spot size (55 μm). The top panel represents the chart of bona fide spot size in the real tissue. The middle panel represents the cell count (y-axis) in the spot area (x-axis); the bottom panel represents the RMSE by fitting the STIE model using the cells within the corresponding area indicated by the x-axis. The distributions are drawn from 80 to 151 spots in mouse brain hippocampus and 1568–2508 spots in human breast cancer, respectively. c Identification of the bona fide area captured by spots in the 10X Visium V2 Chemistry CytAssist mouse brain hippocampus. The distributions are drawn from 120 to 230 spots in section 1 and 121–245 spots in section 2, respectively. The evaluation of image contribution to the cell type deconvolution in human breast cancer ( d ) and mouse brain hippocampus ( e ). The top panel represents the difference between cell-type proportions estimated from the spot gene expression and the nuclear morphological features; the bottom panel represents the RMSE of gene expression fitting. The x-axis represents the value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda$$\end{document} λ in Formula (7). The distributions are drawn from 151 spots in mouse brain hippocampus and 2,451 spots in human breast cancer, respectively, which are presented as mean values ±SEM. f Heatmap of the correlation between the cell type proportion within spots by SPOTlight, DWLS, Stereoscope, RCTD, Tangram, BayesPrism, and STIE. g The association between transcriptomic signature similarity and cell-type colocalization by SPOTlight, DWLS, Stereoscope, RCTD, Tangram, BayesPrism, and STIE. The two-sided p values are calculated for the Pearson’s correlation coefficients ( n = 36) without adjustment for multiple comparisons. h–i High-resolution spots along with STIE holds the premise to distinguish nuanced cell types. h Random assignments of Memory Bcell or Naïve Bcell to the Bcell; CD8+ Tcell, CD4+ Tcell, NK cells, Cycling Tcell, or NKT cell, to the Tcell; and Macrophage, Monocyte, Cycling Myeloid, or DCs to the Myeloid. i The barplot represents the concordance of STIE deconvoluted/convoluted single cells with the simulation ground truth ( h ). The x-axis represents the simulated spot diameter, and the y-axis represents the concordance. The color refers to the cell type in the legend. Source data are provided as a Source Data file.

Article Snippet: The raw reads and images of 10X ST Visium FFPE for mouse brain, mouse kidney and human breast cancer and the processed gene-level read count matrix and image of 10X CytAssist Spatial Gene Expression (V2 Chemistry) for mouse brain section 1&2 were downloaded from www.10xgenomics.com/resources .

Techniques: Cell Counting, Gene Expression