# VisiumIO: Import 10X Genomics Visium Experiment Data

Marcel Ramos

#### May 22, 2026

#### Package

VisiumIO 1.9.5

# Contents

# Introduction

The `VisiumIO` package provides a set of functions to import 10X Genomics Visium experiment data into a `SpatialExperiment` object. The package makes use of the `SpatialExperiment` data structure, which provides a set of classes and methods to handle spatially resolved transcriptomics data.

# TENxIO Supported Formats

Extension | Class | Imported as
--- | --- | ---
.h5 | TENxH5 | SingleCellExperiment w/ TENxMatrix
.mtx / .mtx.gz | TENxMTX | SummarizedExperiment w/ dgCMatrix
.tar.gz | TENxFileList | SingleCellExperiment w/ dgCMatrix
peak_annotation.tsv | TENxPeaks | GRanges
fragments.tsv.gz | TENxFragments | RaggedExperiment
.tsv / .tsv.gz | TENxTSV | tibble

# VisiumIO Supported Formats

Extension | Class | Imported as
--- | --- | ---
spatial.tar.gz | TENxSpatialList | DataFrame list *
.parquet | TENxSpatialParquet | tibble *
.geojson | TENxGeoJSON | sf object *

Note. (*) Intermediate format

# Installation

    if (!require("BiocManager", quietly = TRUE))
        install.packages("BiocManager")
    BiocManager::install("VisiumIO")

# Loading package

    library(VisiumIO)

# TENxVisium

The `TENxVisium` class is used to import a single sample of 10X Visium data. The `TENxVisium` constructor function takes the following arguments:

    TENxVisium(
        resources = "path/to/10x/visium/file.tar.gz",
        spatialResource = "path/to/10x/visium/spatial/file.spatial.tar.gz",
        spacerangerOut = "path/to/10x/visium/sample/folder",
        sample_id = "sample01",
        images = c("lowres", "hires", "detected", "aligned"),
        jsonFile = "scalefactors_json.json",
        tissuePattern = "tissue_positions.*\\.csv",
        spatialCoordsNames = c("pxl_col_in_fullres", "pxl_row_in_fullres")
    )

The `resource` argument is the path to the 10X Visium file. The `spatialResource` argument is the path to the 10X Visium spatial file. It usually ends in `spatial.tar.gz`.

## Example from SpatialExperiment

Note that we use the `images = "lowres"` and `processing = "raw"` arguments based on the name of the `tissue_*_image.png` file and `*_feature_bc_matrix` folder in the `spaceranger` output. The directory structure for a single sample is shown below:

        section1
        └── outs
            ├── spatial
            │   ├── tissue_lowres_image.png
            │   └── tissue_positions_list.csv
            └── raw_feature_bc_matrix
                ├── barcodes.tsv
                ├── features.tsv
                └── matrix.mtx

### Creating a TENxVisium instance

Using the example data from `SpatialExperiment`, we can load the `section1` sample using `TENxVisium`.

    outs_folder <- system.file(
        file.path("extdata", "10xVisium", "section1", "outs"),
        package = "VisiumIO"
    )

    vis <- TENxVisium(
        spacerangerOut = outs_folder, processing = "raw", images = "lowres"
    )
    vis

    ## An object of class "TENxVisium"
    ## Slot "resources":
    ## TENxFileList of length 3
    ## names(3): barcodes.tsv features.tsv matrix.mtx
    ##
    ## Slot "spatialList":
    ## TENxSpatialList of length 3
    ## names(3): scalefactors_json.json tissue_lowres_image.png tissue_positions_list.csv
    ##
    ## Slot "coordNames":
    ## [1] "pxl_col_in_fullres" "pxl_row_in_fullres"
    ##
    ## Slot "sampleId":
    ## [1] "sample01"
    ##
    ## Slot "loadImage":
    ## [1] FALSE

Note that the `spacerangerOut` input corresponds directly to the `outs` folder from the `spaceranger` output.

The show method of the `TENxVisium` class displays the object’s metadata.

### Importing into SpatialExperiment

The `TEnxVisium` object can be imported into a `SpatialExperiment` object using the `import` function.

    import(vis)

    ## class: SpatialExperiment
    ## dim: 50 50
    ## metadata(2): resources spatialList
    ## assays(1): counts
    ## rownames(50): ENSMUSG00000051951 ENSMUSG00000089699 ...
    ##   ENSMUSG00000005886 ENSMUSG00000101476
    ## rowData names(3): ID Symbol Type
    ## colnames(50): AAACAACGAATAGTTC-1 AAACAAGTATCTCCCA-1 ...
    ##   AAAGTCGACCCTCAGT-1 AAAGTGCCATCAATTA-1
    ## colData names(4): in_tissue array_row array_col sample_id
    ## reducedDimNames(0):
    ## mainExpName: Gene Expression
    ## altExpNames(0):
    ## spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
    ## imgData names(4): sample_id image_id data scaleFactor

# TENxVisiumList

The `TENxVisiumList` class is used to import multiple samples of 10X Visium. The interface is a bit more simple in that you only need to provide the `space ranger` output folder as input to the function.

    TENxVisiumList(
        sampleFolders = "path/to/10x/visium/sample/folder",
        sample_ids = c("sample01", "sample02"),
        ...
    )

The `sampleFolders` argument is a character vector of paths to the `spaceranger` output folder. Note that each folder must contain an `outs` directory. The `sample_ids` argument is a character vector of sample ids.

## Example from SpatialExperiment

The directory structure for multiple samples (`section1` and `section2`) is shown below:

        section1
        └── outs
        |   ├── spatial
        |   └── raw_feature_bc_matrix
        section2
        └── outs
            ├── spatial
            └── raw_feature_bc_matrix

### Creating a TENxVisiumList

The main inputs to `TENxVisiumList` are the `sampleFolders` and `sample_ids`. These correspond to the `spaceranger` output sample folders and a vector of sample identifiers, respectively.

    sample_dirs <- list.dirs(
        system.file(
            file.path("extdata", "10xVisium"), package = "VisiumIO"
        ),
        recursive = FALSE, full.names = TRUE
    )

    vlist <- TENxVisiumList(
        sampleFolders = sample_dirs,
        sample_ids = basename(sample_dirs),
        processing = "raw",
        images = "lowres"
    )
    vlist

    ## An object of class "TENxVisiumList"
    ## Slot "VisiumList":
    ## List of length 2

### Importing into SpatialExperiment

The `import` method combines both `SingleCellExperiment` objects along with the spatial information into a single `SpatialExperiment` object. The number of columns in the SpatialExperiment object is equal to the number of cells across both samples (`section1` and `section2`).

    import(vlist)

    ## class: SpatialExperiment
    ## dim: 50 99
    ## metadata(4): resources spatialList resources spatialList
    ## assays(1): counts
    ## rownames(50): ENSMUSG00000051951 ENSMUSG00000089699 ...
    ##   ENSMUSG00000005886 ENSMUSG00000101476
    ## rowData names(3): ID Symbol Type
    ## colnames(99): AAACAACGAATAGTTC-1 AAACAAGTATCTCCCA-1 ...
    ##   AAAGTCGACCCTCAGT-1 AAAGTGCCATCAATTA-1
    ## colData names(4): in_tissue array_row array_col sample_id
    ## reducedDimNames(0):
    ## mainExpName: Gene Expression
    ## altExpNames(0):
    ## spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
    ## imgData names(4): sample_id image_id data scaleFactor

### Visium HD folder structure

The directory structure for a single bin size is shown below.

        Visium_HD
        └── binned_outputs
            └─── square_002um
            │   └── filtered_feature_bc_matrix
            │   │   └── barcodes.tsv.gz
            │   │   └── features.tsv.gz
            │   │   └── matrix.mtx.gz
            │   └── filtered_feature_bc_matrix.h5
            │   └── raw_feature_bc_matrix/
            │   └── raw_feature_bc_matrix.h5
            │   └── spatial
            │       └── [ ... ]
            │       └── tissue_positions.parquet
            └── square_*

### Import Visium HD into SpatialExperiment

    TENxVisiumHD(
        spacerangerOut = "./Visium_HD/",
        sample_id = "sample01",
        processing = c("filtered", "raw"),
        images = c("lowres", "hires", "detected", "aligned_fiducials"),
        bin_size = c("002", "008", "016"),
        jsonFile = .SCALE_JSON_FILE,
        tissuePattern = "tissue_positions\\.parquet",
        spatialCoordsNames = c("pxl_col_in_fullres", "pxl_row_in_fullres"),
        ...
    )

### In-package example

By default, the MatrixMarket format is read in (`format = "mtx"`).

    visfold <- system.file(
        package = "VisiumIO", "extdata", mustWork = TRUE
    )
    TENxVisiumHD(
        spacerangerOut = visfold, images = "lowres", bin_size = "002"
    ) |> import()

    ## class: SpatialExperiment
    ## dim: 10 10
    ## metadata(2): resouces spatialList
    ## assays(1): counts
    ## rownames(10): ENSMUSG00000051951 ENSMUSG00000025900 ...
    ##   ENSMUSG00000033774 ENSMUSG00000025907
    ## rowData names(3): ID Symbol Type
    ## colnames(10): s_002um_02448_01644-1 s_002um_00700_02130-1 ...
    ##   s_002um_01016_02194-1 s_002um_00775_02414-1
    ## colData names(6): barcode in_tissue ... bin_size sample_id
    ## reducedDimNames(0):
    ## mainExpName: Gene Expression
    ## altExpNames(0):
    ## spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
    ## imgData names(4): sample_id image_id data scaleFactor

H5 files are supported via the `format = "h5"` argument input.

    TENxVisiumHD(
        spacerangerOut = visfold, images = "lowres", bin_size = "002",
        format = "h5"
    ) |> import()

    ## class: SpatialExperiment
    ## dim: 10 10
    ## metadata(2): resouces spatialList
    ## assays(1): counts
    ## rownames(10): ENSMUSG00000051951 ENSMUSG00000025900 ...
    ##   ENSMUSG00000033774 ENSMUSG00000025907
    ## rowData names(3): ID Symbol Type
    ## colnames(10): s_002um_02448_01644-1 s_002um_00700_02130-1 ...
    ##   s_002um_01016_02194-1 s_002um_00775_02414-1
    ## colData names(6): barcode in_tissue ... bin_size sample_id
    ## reducedDimNames(0):
    ## mainExpName: Gene Expression
    ## altExpNames(0):
    ## spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
    ## imgData names(4): sample_id image_id data scaleFactor

# Session Info

Click to expand `sessionInfo()`

    R version 4.6.0 Patched (2026-05-01 r89994)
    Platform: aarch64-apple-darwin23
    Running under: macOS Tahoe 26.3.1

    Matrix products: default
    BLAS:   /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRblas.0.dylib
    LAPACK: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1

    locale:
    [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

    time zone: America/New_York
    tzcode source: internal

    attached base packages:
    [1] stats4    stats     graphics  grDevices utils     datasets  methods
    [8] base

    other attached packages:
     [1] VisiumIO_1.9.5              TENxIO_1.15.0
     [3] SingleCellExperiment_1.35.1 SummarizedExperiment_1.43.0
     [5] Biobase_2.73.1              GenomicRanges_1.65.0
     [7] Seqinfo_1.3.0               IRanges_2.47.1
     [9] S4Vectors_0.51.2            BiocGenerics_0.59.3
    [11] generics_0.1.4              MatrixGenerics_1.25.0
    [13] matrixStats_1.5.0           BiocStyle_2.41.0

    loaded via a namespace (and not attached):
     [1] rjson_0.2.23             xfun_0.57                bslib_0.11.0
     [4] rhdf5_2.57.0             lattice_0.22-9           tzdb_0.5.0
     [7] rhdf5filters_1.25.0      vctrs_0.7.3              tools_4.6.0
    [10] tibble_3.3.1             pkgconfig_2.0.3          BiocBaseUtils_1.15.1
    [13] R.oo_1.27.1              Matrix_1.7-5             assertthat_0.2.1
    [16] lifecycle_1.0.5          compiler_4.6.0           codetools_0.2-20
    [19] htmltools_0.5.9          sass_0.4.10              yaml_2.3.12
    [22] pillar_1.11.1            jquerylib_0.1.4          R.utils_2.13.0
    [25] DelayedArray_0.39.2      cachem_1.1.0             magick_2.9.1
    [28] abind_1.4-8              tidyselect_1.2.1         digest_0.6.39
    [31] purrr_1.2.2              bookdown_0.46            arrow_24.0.0
    [34] fastmap_1.2.0            grid_4.6.0               cli_3.6.6
    [37] SparseArray_1.13.2       magrittr_2.0.5           S4Arrays_1.13.0
    [40] h5mread_1.5.0            readr_2.2.0              withr_3.0.2
    [43] bit64_4.8.2              rmarkdown_2.31           XVector_0.53.0
    [46] bit_4.6.0                otel_0.2.0               R.methodsS3_1.8.2
    [49] hms_1.1.4                SpatialExperiment_1.23.0 HDF5Array_1.41.0
    [52] evaluate_1.0.5           knitr_1.51               BiocIO_1.23.3
    [55] rlang_1.2.0              Rcpp_1.1.1-1.1           glue_1.8.1
    [58] BiocManager_1.30.27      jsonlite_2.0.0           Rhdf5lib_2.1.0
    [61] R6_2.6.1