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.
| 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 |
| Extension | Class | Imported as |
|---|---|---|
| spatial.tar.gz | TENxSpatialList | inter. DataFrame list |
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("VisiumIO")library(VisiumIO)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.
Note that we use the image = "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.mtxUsing the example data in SpatialExperiment, we can load the section1
sample using TENxVisium.
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.
The TEnxVisium object can be imported into a SpatialExperiment object using
the import function.
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 scaleFactorThe 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.
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_matrixThe 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 = "SpatialExperiment"
),
recursive = FALSE, full.names = TRUE
)
vlist <- TENxVisiumList(
sampleFolders = sample_dirs,
sample_ids = basename(sample_dirs),
processing = "raw",
image = "lowres"
)
vlist## An object of class "TENxVisiumList"
## Slot "VisiumList":
## List of length 2The 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(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 scaleFactorsessionInfo()## R version 4.4.0 RC (2024-04-16 r86468)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] 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
## [8] base
##
## other attached packages:
## [1] VisiumIO_1.1.0 TENxIO_1.7.0
## [3] SingleCellExperiment_1.27.0 SummarizedExperiment_1.35.0
## [5] Biobase_2.65.0 GenomicRanges_1.57.0
## [7] GenomeInfoDb_1.41.0 IRanges_2.39.0
## [9] S4Vectors_0.43.0 BiocGenerics_0.51.0
## [11] MatrixGenerics_1.17.0 matrixStats_1.3.0
## [13] BiocStyle_2.33.0
##
## loaded via a namespace (and not attached):
## [1] rjson_0.2.21 xfun_0.43 bslib_0.7.0
## [4] lattice_0.22-6 tzdb_0.4.0 vctrs_0.6.5
## [7] tools_4.4.0 parallel_4.4.0 tibble_3.2.1
## [10] fansi_1.0.6 pkgconfig_2.0.3 BiocBaseUtils_1.7.0
## [13] Matrix_1.7-0 lifecycle_1.0.4 GenomeInfoDbData_1.2.12
## [16] compiler_4.4.0 codetools_0.2-20 htmltools_0.5.8.1
## [19] sass_0.4.9 yaml_2.3.8 pillar_1.9.0
## [22] crayon_1.5.2 jquerylib_0.1.4 DelayedArray_0.31.0
## [25] cachem_1.0.8 magick_2.8.3 abind_1.4-5
## [28] tidyselect_1.2.1 digest_0.6.35 bookdown_0.39
## [31] fastmap_1.1.1 grid_4.4.0 archive_1.1.8
## [34] cli_3.6.2 SparseArray_1.5.0 magrittr_2.0.3
## [37] S4Arrays_1.5.0 utf8_1.2.4 readr_2.1.5
## [40] UCSC.utils_1.1.0 bit64_4.0.5 rmarkdown_2.26
## [43] XVector_0.45.0 httr_1.4.7 bit_4.0.5
## [46] hms_1.1.3 SpatialExperiment_1.15.0 evaluate_0.23
## [49] knitr_1.46 BiocIO_1.15.0 rlang_1.1.3
## [52] Rcpp_1.0.12 glue_1.7.0 BiocManager_1.30.22
## [55] vroom_1.6.5 jsonlite_1.8.8 R6_2.5.1
## [58] zlibbioc_1.51.0