> For the complete documentation index, see [llms.txt](https://docs.bcbi.brown.edu/codiac-for-health/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.bcbi.brown.edu/codiac-for-health/ursa-ri/datasets/10x-genomics-dataset-repository.md).

# 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.[\[1\]](#references) 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.[\[2\]](#references)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

| Variable / Component | R Class / Data Type | Description                                                              | Example / Range      |
| -------------------- | ------------------- | ------------------------------------------------------------------------ | -------------------- |
| 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

| Variable / Component       | R Class / Data Type | Description                                                              | Example / Range            |
| -------------------------- | ------------------- | ------------------------------------------------------------------------ | -------------------------- |
| 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

1. 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.](https://www.nature.com/articles/s41587-023-01767-y) Nat Biotechnol. 2024 Feb;42(2):293-304.
2. Gillespie J, Pietrzak M, Song MA, Chung D. [A Meta-Review of Spatial Transcriptomics Analysis Software.](https://www.mdpi.com/2073-4409/14/14/1060) 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.](https://arxiv.org/abs/2406.16192) 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.](https://academic.oup.com/nar/article/54/1/gkaf1473/8418223?login=true) Nucleic Acids Res. 2026 Jan 5;54(1):gkaf1473.

### Links

* [Human Glioblastoma: Whole Transcriptome Analysis](https://www.10xgenomics.com/datasets/human-glioblastoma-whole-transcriptome-analysis-1-standard-1-2-0) Visium-Generated Spatial Transcriptomics Dataset
* [Analysis, visualization, and integration of spatial datasets with Seurat](https://satijalab.org/seurat/articles/spatial_vignette)
* [Spatial Transcriptomic Data Analysis: A Beginner’s Guide](https://nexco.ch/blog/Spatial-Transcriptomic-Data-Analysis-A-Beginner%C3%A2%C2%80%C2%99s-Guide)
* [HGNC Gene Search Tool](https://www.genenames.org/)


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.bcbi.brown.edu/codiac-for-health/ursa-ri/datasets/10x-genomics-dataset-repository.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.