10x Genomics Dataset Repository
Overview
10x Genomics maintains a repository of publicly available datasets generated through three main platforms: Chromium, Visium, and Xenium. The repository includes Chromium single-cell molecular profiles, Visium spatial transcriptomics (ST) data, and Xenium single-cell spatial gene expression measurements. Datasets span a wide range of tissue types, species, and disease states, with each platform offering multiple dataset formats and analysis outputs depending on the assay chosen. As an example, the Human Glioblastoma: Whole Transcriptome Analysis dataset is a Visium dataset containing ST data on fresh frozen human glioblastoma multiforme tissue, and will be referenced throughout this overview to illustrate typical dataset structure and content.
Methodology and Generation
Datasets in the 10x repository are generated using standardized wet-lab and computational protocols specific to each platform. For Visium datasets, tissue is typically obtained from a specialized tissue bank (e.g., BioIVT Asterand, the source for the glioblastoma dataset), encased in a supportive medium, rapidly frozen, and sliced into 10 µm-thick layers. A slice of tissue is placed onto a Visium Gene Expression slide, which is embedded with thousands of microscopic capture spots acting as biological barcodes. The tissue is fixed (typically with methanol), stained with Hematoxylin and Eosin (H&E), and imaged using a microscope such as the Nikon Eclipse Ti2-E. To capture genetic data, a sequencing instrument such as the Illumina NovaSeq 6000 is used to read the RNA in the tissue and build a genomic library accordingly. The resulting data are run through the software Space Ranger to match the genetic code back to the tissue image. In the glioblastoma dataset specifically, this process yielded 3,468 distinct spots occupied by tissue on the slide, with a median of 4,326 genes and 11,596 unique molecular identifier (UMI) counts detected per spot. Figures like these vary by dataset, tissue type, and sequencing depth.
Access and Analysis
To access a dataset, create a free account through 10x Genomics. Navigate to the Datasets page and use the sidebar to filter by platform, software, species, sample/tissue type, and more. Choose a dataset and look at the "Dataset overview" tab for metadata such as the source, preparation, imaging, sequencing, and key metrics. Look at the "Output and supplemental files" tab for the files to download. For most ST analyses, a minimum of four files are needed: the gene expression matrix, available in either HDF5 (.h5) or Matrix Market (.mtx.gz) format, with .h5 preferred because it is a single file and typically smaller; the spatial coordinates, which map each spot to its location on the slide, available in .csv format; the histology image, required for overlaying expression onto the tissue, available in .png format; and the scale factors, required for aligning spot coordinates with the image, available in .json format. As an example, in the glioblastoma dataset, the display text for the .h5 file is "Feature / barcode matrix HDF5 (filtered)," which downloads a file named "filtered_feature_bc_matrix.h5." For the other three files, the display text "Spatial imaging data" downloads a folder named "spatial" that contains "tissue_positions_list.csv," "tissue_hires_image.png," and "scalefactors_json.json."
In terms of analysis, the software toolkit Seurat is designed to help beginners analyze ST data.[1] Seurat contains user-friendly workflows that streamline complex computational steps to visualize where specific genes are active and identify distinct cell types. Other built-in functions of Seurat include clustering, quality control, and data normalization. While Seurat provides a focused framework for ST analysis, there is also a rapidly expanding ecosystem of ST software. To help navigate these options, Gillespie et al. conducted a meta-review synthesizing benchmarking studies across tissue architecture identification, spatially variable gene detection, cell-cell communication analysis, and deconvolution.[2]This paper can be referenced to help with software selection for ST analysis.
Standardized Healthcare Vocabularies
In 10x Genomics gene expression matrices, genes are typically identified using HUGO Gene Nomenclature Committee (HGNC) gene symbols, which are unique abbreviations assigned to each human gene. For example, in the glioblastoma .h5 file, the first three genes are MIR1302-2HG, FAM138A, and OR4F5.
Data Dictionary
Spatial Coordinates
Row Names (barcodes)
character
unique 16-base pair spatial barcode to identify the physical Visium spot
AAACAGTGTTCCTGGG-1
x
numeric
horizontal coordinate on the 2D plane of the image
2361 to 10354 pixels
y
numeric
vertical coordinate on the 2D plane of the image
1442 to 10673 pixels
cell
character
duplicate of spatial barcodes maintained by Seurat
AAACAGTGTTCCTGGG-1
Gene Expression Matrix
Matrix Object
dgCMatrix
compressed sparse matrix structure that contains all counts
36,601 genes x 3,468 spots
Row Names (features)
character
biological features in HGNC Gene Symbols
OR4F5
Column Names (cells/spots)
character
unique 16-base pair spatial barcode to identify the physical Visium spot
AAACCGTTCGTCCAGG-1
Matrix Values
numeric
raw UMI transcript counts per gene, per spot
0 to 1,118 UMIs
References
Hao Y, Stuart T, Kowalski MH, Choudhary S, Hoffman P, Hartman A, Srivastava A, Molla G, Madad S, Fernandez-Granda C, Satija R. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat Biotechnol. 2024 Feb;42(2):293-304.
Gillespie J, Pietrzak M, Song MA, Chung D. A Meta-Review of Spatial Transcriptomics Analysis Software. Cells. 2025 Jul 10;14(14):1060.
Resources
Articles
Jaume G, Doucet P, Song AH, Lu MY, Almagro-Pérez C, Wagner SJ, Vaidya AJ, Chen RJ, Williamson DF, Kim A, Mahmood F. HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis. Adv Neural Inf Process Syst. 2024 Dec 10;37:53798-833.
Zhang L, Sagan A, Qin B, Wang H, Kim E, Hu B, Osmanbeyoglu HU. STAN, a computational framework for inferring spatially informed transcription factor activity. Nucleic Acids Res. 2026 Jan 5;54(1):gkaf1473.
Links
Human Glioblastoma: Whole Transcriptome Analysis Visium-Generated Spatial Transcriptomics Dataset
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