# 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 \* |
**Note**. (\*) Intermediate format
# Installation
``` r
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("VisiumIO")
```
# Loading package
``` r
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:
``` r
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 in `SpatialExperiment`, we can load the
`section1` sample using `TENxVisium`.
``` r
sample_dir <- system.file(
file.path("extdata", "10xVisium", "section1"),
package = "SpatialExperiment"
)
vis <- TENxVisium(
spacerangerOut = sample_dir, 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"
```
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.
``` r
import(vis)
#> class: SpatialExperiment
#> dim: 50 50
#> metadata(0):
#> assays(1): counts
#> rownames: NULL
#> rowData names(1): Symbol
#> colnames(50): AAACAACGAATAGTTC-1 AAACAAGTATCTCCCA-1 ...
#> AAAGTCGACCCTCAGT-1 AAAGTGCCATCAATTA-1
#> colData names(4): in_tissue array_row array_col sample_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> 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.
``` r
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.
``` r
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`).
``` r
import(vlist)
#> class: SpatialExperiment
#> dim: 50 99
#> metadata(0):
#> assays(1): counts
#> rownames: NULL
#> rowData names(1): Symbol
#> colnames(99): AAACAACGAATAGTTC-1 AAACAAGTATCTCCCA-1 ...
#> AAAGTCGACCCTCAGT-1 AAAGTGCCATCAATTA-1
#> colData names(4): in_tissue array_row array_col sample_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> 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
``` r
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"`).
``` r
visfold <- system.file(
package = "VisiumIO", "extdata", mustWork = TRUE
)
TENxVisiumHD(
spacerangerOut = visfold, images = "lowres", bin_size = "002"
) |> import()
#> class: SpatialExperiment
#> dim: 10 10
#> metadata(2): resources 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.
``` r
TENxVisiumHD(
spacerangerOut = visfold, images = "lowres", bin_size = "002",
format = "h5"
) |> import()
#> class: SpatialExperiment
#> dim: 10 10
#> metadata(2): resources 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
```
<details>
<summary>
Click to expand <code>sessionInfo()</code>
</summary>
# Session Info
``` r
sessionInfo()
#> R version 4.5.0 Patched (2025-04-15 r88148)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.2 LTS
#>
#> Matrix products: default
#> BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] VisiumIO_1.5.1 TENxIO_1.11.1 SingleCellExperiment_1.31.0 SummarizedExperiment_1.39.0
#> [5] Biobase_2.69.0 GenomicRanges_1.61.0 GenomeInfoDb_1.45.3 IRanges_2.43.0
#> [9] S4Vectors_0.47.0 BiocGenerics_0.55.0 generics_0.1.3 MatrixGenerics_1.21.0
#> [13] matrixStats_1.5.0 colorout_1.3-2
#>
#> loaded via a namespace (and not attached):
#> [1] rjson_0.2.23 xfun_0.52 rhdf5_2.53.0 lattice_0.22-7 tzdb_0.5.0
#> [6] rhdf5filters_1.21.0 vctrs_0.6.5 tools_4.5.0 parallel_4.5.0 tibble_3.2.1
#> [11] pkgconfig_2.0.3 BiocBaseUtils_1.11.0 R.oo_1.27.0 Matrix_1.7-3 assertthat_0.2.1
#> [16] lifecycle_1.0.4 compiler_4.5.0 codetools_0.2-20 htmltools_0.5.8.1 yaml_2.3.10
#> [21] pillar_1.10.2 crayon_1.5.3 R.utils_2.13.0 rsconnect_1.3.4 DelayedArray_0.35.1
#> [26] magick_2.8.6 abind_1.4-8 tidyselect_1.2.1 digest_0.6.37 purrr_1.0.4
#> [31] arrow_19.0.1.1 fastmap_1.2.0 grid_4.5.0 archive_1.1.12 cli_3.6.5
#> [36] SparseArray_1.9.0 magrittr_2.0.3 S4Arrays_1.9.0 h5mread_1.1.0 readr_2.1.5
#> [41] UCSC.utils_1.5.0 bit64_4.6.0-1 rmarkdown_2.29 XVector_0.49.0 httr_1.4.7
#> [46] bit_4.6.0 R.methodsS3_1.8.2 hms_1.1.3 SpatialExperiment_1.19.0 HDF5Array_1.37.0
#> [51] evaluate_1.0.3 knitr_1.50 BiocIO_1.19.0 rlang_1.1.6 Rcpp_1.0.14
#> [56] glue_1.8.0 rstudioapi_0.17.1 vroom_1.6.5 jsonlite_2.0.0 R6_2.6.1
#> [61] Rhdf5lib_1.31.0
```
</details>
