Journal: Frontiers in Genetics

Article Title: Deciphering the role of CNIH4 in pan-cancer landscapes and its significance in breast cancer progression

doi: 10.3389/fgene.2025.1536620

Figure Lengend Snippet: (A) Expression of CNIH4 gene in each microdomain in BRCA spatial transcriptome sections; (B–E) The cell type with the largest proportion in each microdomain at BRCA idle resolution and the spatial transcriptome localization of the CNIH4 gene. Each dot is a spot for spatial transcriptome sequencing, and different colors represent different cell types. The darker the color (red) in the same spot, the higher the expression of the CNIH4 gene in the spot; (F–I) Spearman correlation of CNIH4 gene expression with each cell type in microdomains at idle resolution. The red line indicates a positive correlation, the green line denotes a negative correlation, the gray line signifies no statistical significance, and the thickness of the line reflects the absolute value of the correlation coefficient.

Article Snippet: Spatial transcriptome datasets were obtained from the 10x Genomics server ( https://www.10xgenomics.com/ ) and previous studies ( ; ). provide details of the spatial transcriptome dataset and detailed abbreviations for the 33 tumors.

Techniques: Expressing, Sequencing, Gene Expression

Journal: Nucleic Acids Research

Article Title: SPathDB: a comprehensive database of spatial pathway activity atlas

doi: 10.1093/nar/gkae1041

Figure Lengend Snippet: Overview of data content and functions of SPathDB. The left panel contains the database content, which includes the spatial transcriptome dataset and pathway data content, and construction of spatial pathway activity profiles. The right panel contains the tools of SPathDB to retrieve, analyze and visualize spatial pathway activity.

Article Snippet: Spatial transcriptome datasets of human and mouse were collected from the Gene Expression Omnibus (GEO) database , 10× Genomics ( https://www.10xgenomics.com/ ), Mendeley Data ( https://data.mendeley.com/ ) and CROST ( https://ngdc.cncb.ac.cn/crost/ ).

Techniques: Activity Assay

Journal: Computational and Structural Biotechnology Journal

Article Title: Spatial domains identification in spatial transcriptomics using modality-aware and subspace-enhanced graph contrastive learning

doi: 10.1016/j.csbj.2024.10.029

Figure Lengend Snippet: GRAS4T improved the accuracy of identifying layer structures within the DLPFC dataset compared to other methods. (a) Boxplot of ARI values across all sections of the DLPFC dataset for six methods. (b) The H&E image and manual annotation of slice 151672. (c) The spatial domains in six methods for slice 151672. (d) UMAP visualizations and PAGA graphs in six methods for slice 151672.

Article Snippet: The ST datasets supporting the findings of this study are all publicly available. (1) The DLPFC dataset is available at http://research.libd.org/spatialLIBD/ . (2) The HER2+ dataset generated by spatial transcriptomics platform is accessed at https://github.com/almaan/her2st . (3) The mouse visual cortex dataset generated by STARmap is available at https://www.dropbox.com/sh/f7ebheru1lbz91s/AADm6D54GSEFXB1feRy6OSASa/visual_1020/20180505_BY3_1kgenes?dl=0&subfolder_nav_tracking=1 . (4) The adult mouse brain dataset is accessed at https://www.10xgenomics.com/resources/datasets . (5) The Stereo-seq mouse olfactory bulb dataset is available at https://github.com/JinmiaoChenLab/SEDR_analyses/ . (6) The MERFISH dataset is accessed at https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248 . (7) The human breast cancer dataset is available at https://www.10xgenomics.com/resources/datasets . (8) The anterior and posterior sections of the mouse brain are accessed at https://www.10xgenomics.com/resources/datasets and the Allen Brain Atlas reference is available at https://mouse.brain-map.org/static/atlas .

Techniques: