Journal: BMC Bioinformatics

Article Title: SCNT: an R package for data analysis and visualization of single-cell and spatial transcriptomics

doi: 10.1186/s12859-025-06209-x

Figure Lengend Snippet: SCNT testing on human kidney 10X visium HD dataset. ( A ) Spatial clustering plot based on stPlot showing the distribution of 12 cell clusters across the entire tissue space. ( B ) Spatial distribution of the 12 cell clusters within a sub-region. ( C ) scFeature plot displaying the spatial expression feature of LRP2 within the sub-region

Article Snippet: To demonstrate the functionality of SCNT , we applied it to a 10X Genomics Visium HD dataset of human kidney tissue, following the official tutorial, including dimensionality reduction and clustering.

Techniques: Expressing

Journal: Genome Biology

Article Title: DeepGFT: identifying spatial domains in spatial transcriptomics of complex and 3D tissue using deep learning and graph Fourier transform

doi: 10.1186/s13059-025-03631-5

Figure Lengend Snippet: DeepGFT effectively characterized the subtle tissue architectures of human lymph node Visium data. a H&E image and ground truth segmentation of germinal center. b Domain assignments (evaluated by Jaccard index) and results generated by DeepGFT, BayesSpace, SpaGCN, SpaceFlow, Scanpy, GraphST, STAGATE, stLearn, SpatialPCA, and spaVAE in human lymph node section. c Evaluation results of four metrics (Jaccard index, precision, recall, and F1 score) of ten tools. d The GCs and their adjacent regions obtained from DeepGFT, as well as genes supported corresponding spatial domains. e Cell type compositions of each spatial domain, obtained from cell2location. f Biological processes involved in DEGs in domain 1

Article Snippet: The human lymph node dataset (10x Visium) is available at ( https://www.10xgenomics.com/cn/resources/datasets/human-lymph-node-1-standard-1-1-0 ) [ ].

Techniques: Generated

Journal: Communications Biology

Article Title: Flexible integration of spatial and expression information for precise spot embedding via ZINB-based graph-enhanced autoencoder

doi: 10.1038/s42003-025-07965-5

Figure Lengend Snippet: a Domain annotation of the human breast cancer 10X Visium dataset. b Spatial clustering results of five methods. c From top to bottom: reference histological images; domains 3 and 10 predicted by Spot2vector superimposed on histology; high-contrast enhancement of histological features within the predicted domains. d, e : ( d ) The raw and ( e ) Spot2vector-denoised gene expression differences of marker genes between subdomains 3 (n = 110) and 10 (n = 349). A two-sided Wilcoxon Rank Sum test is used to test the difference. f Tumor and tumor edge annotations of the dataset. g, h : ( g) The raw and ( h ) Spot2vector-denoised gene expression differences of marker genes between the tumor (n = 2490) and tumor edge (n = 823). A two-sided Wilcoxon Rank Sum test is used to test the difference. All box plots range from the first and third quartiles with the median as the horizontal line, while whiskers represent 1.5 times the interquartile range from the lower and upper bounds of the box. i Spatial plots of marker genes, using their raw expression (upper panels) and Spot2vector-denoised expression (lower panels), respectively. j Survival analyses of four genes. A two-sided Log-rank test is used to compare the differences in survival curves between groups. HR stands for Hazard Ratio.

Article Snippet: The raw 10X Visium Human Breast cancer dataset can be downloaded from https://www.10xgenomics.com/datasets/human-breast-cancer-block-a-section-1-1-standard-1-1-0 , and its annotation was downloaded from https://huggingface.co/datasets/han-shu/st_datasets/tree/main .

Techniques: Gene Expression, Marker, Expressing

Journal: bioRxiv

Article Title: Towards a Better Understanding of Batch Effects in Spatial Transcriptomics: Definition and Method Evaluation

doi: 10.1101/2025.03.12.642755

Figure Lengend Snippet: Visualization of the batch effect correction results from six ST methods using the Coronal mouse brain dataset (Sample 7). The dataset includes tissue slices of three protocols: “10X_FFPE,” “10X_Normal,” and “10X_DAPI”. Panels A-C show UMAP plots of uncorrected (RAW) data and six ST methods ( PRECAST, DeepST, STAligner, GraphST, SPIRAL , and spatiAlign ). Spots are colored by A: tissue slice indexes, B: manual annotations, and C: clustering outcomes. Panel D comparatively visualizes spatial domain identification performance during cross-protocol integration.

Article Snippet: We used 10X Genomics Visium datasets of the DLPFC and human breast cancer (HBC) to evaluate the effectiveness of various methods in correcting batch effects during the integration of tissue slices from the same sample.

Techniques:

Journal: bioRxiv

Article Title: Towards a Better Understanding of Batch Effects in Spatial Transcriptomics: Definition and Method Evaluation

doi: 10.1101/2025.03.12.642755

Figure Lengend Snippet: Visualization of the batch effect correction results from four ST methods using the Mouse olfactory bulbs (OB) dataset (Sample 8). The dataset contains tissue slices from three platforms: “10x Visium”, “Stereo-seq”, and “Slide-seq V2”. Panels A-B display UMAP plots of uncorrected data (RAW) and four ST methods ( STAligner, GraphST, SPIRAL , and spatiAlign ). Spots are colored by tissue slice indexes and clustering results, respectively. Panel C comparatively visualizes spatial domain identification performance during cross-platform integration.

Article Snippet: We used 10X Genomics Visium datasets of the DLPFC and human breast cancer (HBC) to evaluate the effectiveness of various methods in correcting batch effects during the integration of tissue slices from the same sample.

Techniques: