--- title: "TENxVisiumData" author: - name: Helena L. Crowell affiliation: Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland output: BiocStyle::html_document: toc_float: true package: TENxVisiumData abstract: | The TENxVisiumData ExperimentHub package provides a collection of Visium spatial gene expression datasets by 10X Genomics. Data cover various organisms and tissues, and are formatted into objects of class SpatialExperiment. vignette: | %\VignetteIndexEntry{TENxVisiumData} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r include = FALSE} knitr::opts_chunk$set(message = FALSE, warning = FALSE, error = FALSE) ``` # Available datasets The `TENxVisiumData` package provides an R/Bioconductor resource for [Visium spatial gene expression datasets by 10X Genomics](https://support.10xgenomics.com/spatial-gene-expression/datasets). The package currently includes 13 datasets from 23 samples across two organisms (human and mouse) and 13 tissues: * HumanBreastCancerIDC * [Human Breast Cancer (Block A Section 1)](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Breast_Cancer_Block_A_Section_1) * [Human Breast Cancer (Block A Section 2)](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Breast_Cancer_Block_A_Section_2) * HumanBreastCancerILC * [Human Breast Cancer: Whole Transcriptome Analysis](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.2.0/Parent_Visium_Human_BreastCancer) * [Human Breast Cancer: Targeted, Immunology Panel](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.2.0/Targeted_Visium_Human_BreastCancer_Immunology) * HumanCerebellum * [Human Cerebellum: Whole Transcriptome Analysis](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.2.0/Parent_Visium_Human_Cerebellum) * [Human Cerebellum: Targeted, Neuroscience Panel](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.2.0/Targeted_Visium_Human_Cerebellum_Neuroscience) * HumanColorectalCancer * [Human Colorectal Cancer: Whole Transcriptome Analysis](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.2.0/Parent_Visium_Human_ColorectalCancer) * [Human Colorectal Cancer: Targeted, Gene Signature Panel](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.2.0/Targeted_Visium_Human_ColorectalCancer_GeneSignature) * HumanGlioblastoma * [Human Glioblastoma: Whole Transcriptome Analysis](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.2.0/Parent_Visium_Human_Glioblastoma) * [Human Glioblastoma: Targeted, Pan-Cancer Panel](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.2.0/Targeted_Visium_Human_Glioblastoma_Pan_Cancer) * HumanHeart * [Human Heart](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Human_Heart) * HumanLymphNode * [Human Lymph Node](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Human_Lymph_Node) * HumanOvarianCancer * [Human Ovarian Cancer: Whole Transcriptome Analysis](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.2.0/Parent_Visium_Human_OvarianCancer) * [Human Ovarian Cancer: Targeted, Immunology Panel](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.2.0/Targeted_Visium_Human_OvarianCancer_Immunology) * [Human Ovarian Cancer: Targeted, Pan-Cancer Panel](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.2.0/Targeted_Visium_Human_OvarianCancer_Pan_Cancer) * HumanSpinalCord * [Human Spinal Cord: Whole Transcriptome Analysis](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.2.0/Parent_Visium_Human_SpinalCord) * [Human Spinal Cord: Targeted, Neuroscience Panel](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.2.0/Targeted_Visium_Human_SpinalCord_Neuroscience) * MouseBrainCoronal * [Mouse Brain Section (Coronal)](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Adult_Mouse_Brain) * MouseBrainSagittalAnterior * [Mouse Brain Serial Section 1 (Sagittal-Anterior)](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Mouse_Brain_Sagittal_Anterior) * [Mouse Brain Serial Section 2 (Sagittal-Anterior)](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Mouse_Brain_Sagittal_Anterior_Section_2) * MouseBrainSagittalPosterior * [Mouse Brain Serial Section 1 (Sagittal-Posterior)](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Mouse_Brain_Sagittal_Posterior) * [Mouse Brain Serial Section 2 (Sagittal-Posterior)](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Mouse_Brain_Sagittal_Posterior_Section_2) * MouseKidneyCoronal * [Mouse Kidney Section (Coronal)](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Mouse_Kidney) A list of currently available datasets can be obtained using the `ExperimentHub` interface: ```{r} library(ExperimentHub) eh <- ExperimentHub() (q <- query(eh, "TENxVisium")) ``` # Loading the data To retrieve a dataset, we can use a dataset's corresponding named function `()`, where `` should correspond to one a valid dataset identifier (see `?TENxVisiumData`). E.g.: ```{r} library(TENxVisiumData) spe <- HumanHeart() ``` Alternatively, data can loaded directly from Bioconductor's `r Biocpkg("ExerimentHub")` as follows. First, we initialize a hub instance and store the complete list of records in a variable `eh`. Using `query()`, we then identify any records made available by the `TENxVisiumData` package, as well as their accession IDs (EH1234). Finally, we can load the data into R via `eh[[id]]`, where `id` corresponds to the data entry's identifier we'd like to load. E.g.: ```{r} library(ExperimentHub) eh <- ExperimentHub() # initialize hub instance q <- query(eh, "TENxVisium") # retrieve 'TENxVisiumData' records id <- q$ah_id[1] # specify dataset ID to load spe <- eh[[id]] # load specified dataset ``` # Data representation Each dataset is provided as a `r Biocpkg("SpatialExperiment")` (SPE), which extends the `r Biocpkg("SingleCellExperiment")` (SCE) class with features specific to spatially resolved data: ```{r} spe ``` For details on the SPE class, we refer to the package's vignette. Briefly, the SPE harbors the following data in addition to that stored in a SCE: `spatialCoords`; a numeric matrix of spatial coordinates, stored inside the object's `int_colData`: ```{r} head(spatialCoords(spe)) ``` `spatialData`; a `DFrame` of spatially-related sample metadata, stored as part of the object's `colData`. This `colData` subset is in turn determined by the `int_metadata` field `spatialDataNames`: ```{r} head(spatialData(spe)) ``` `imgData`; a `DFrame` containing image-related data, stored inside the `int_metadata`: ```{r} imgData(spe) ``` Datasets with multiple sections are consolidated into a single SPE with `colData` field `sample_id` indicating each spot's sample of origin. E.g.: ```{r} spe <- MouseBrainSagittalAnterior() table(spe$sample_id) ``` Datasets of targeted analyses are provided as a *nested* SPE, with whole transcriptome measurements as primary data, and those obtained from targeted panels as `altExp`s. E.g.: ```{r} spe <- HumanOvarianCancer() altExpNames(spe) ``` # Session information {- .smaller} ```{r} sessionInfo() ```