The SpatialExperiment package provides classes and methods for single cell spatial data handling.
The VisiumExperiment class exteds the SpatialExperiment class by providing more specific attributes and methods for the 10x-Genomics Visium experiments data.
This vignettes shows how to create a VisiumExperiment class by loading a Mouse Coronal 10x-Genomics Visium spatial dataset.
Dataset downloaded from 10x-Genomics website .
if(!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("SpatialExperiment")library("SpatialExperiment")
library("Matrix")
library("rjson")Examples data files have been downloaded from the 10x site, Mouse adult brain
Loading features and count matrix.
barcodesFile <- system.file(file.path("extdata", "10x_visium",
"barcodes.tsv"), package="SpatialExperiment")
barcodesEx <- read.csv(barcodesFile, sep="\t",
header=FALSE, col.names=c("Cell_ID"))
featuresFile <- system.file(file.path("extdata", "10x_visium",
"features.tsv"), package="SpatialExperiment")
featuresEx <- read.csv(featuresFile, sep="\t",
header=FALSE, col.names=c("Feature_ID", "Feature_name",
"Feature_type"))
countsFile <- system.file(file.path("extdata", "10x_visium",
"matrix.mtx"), package="SpatialExperiment")
countsEx <- readMM(file=countsFile)Loading Spatial coordinates of the tissue section.
posFile <- system.file(file.path("extdata", "10x_visium",
"tissue_positions_list.tsv"),
package="SpatialExperiment")
tissPosEx <- read.csv(posFile,
sep="\t", header=FALSE,
col.names=c("Cell_ID", "in_tissue",
"array_row", "array_col",
"pxl_col_in_fullres", "pxl_row_in_fullres"))10x-Genomics Visium experiments comes out with images in different resolutions for tissue spots locations.
imageFilePath <- list.files(system.file(file.path("extdata", "10x_visium",
"images"), package="SpatialExperiment"),
full.names=TRUE)Scale factors for the given three 10x Visium image formats transformations.
scaleFile <- system.file(file.path("extdata", "10x_visium",
"scalefactors_json.json"),
package="SpatialExperiment")
scalefactors <- fromJSON(file=scaleFile)Creating VisiumExperiment class within 10x Visium data.
ve <- VisiumExperiment(rowData=featuresEx, colData=barcodesEx,
assays=c(counts=countsEx),
spatialCoords=tissPosEx,
scaleFactors=scalefactors)
ve## class: VisiumExperiment
## dim: 50 50
## metadata(0):
## assays(1): counts
## rownames: NULL
## rowData names(3): Feature_ID Feature_name Feature_type
## colnames: NULL
## colData names(1): Cell_ID
## reducedDimNames(0):
## altExpNames(0):
## spatialCoordinates(6): Cell_ID in_tissue ... pxl_col_in_fullres
## pxl_row_in_fullres
## inTissue(1): 22
## imagePaths(0):The spatialCoords methods can be used to retrieve the spatial coordinates., It can be used also to update the values.
spatialCoords(ve)## DataFrame with 50 rows and 6 columns
## Cell_ID in_tissue array_row array_col pxl_col_in_fullres
## <character> <integer> <integer> <integer> <integer>
## 1 AAACAACGAATAGTTC-1 0 0 16 1252
## 2 AAACAAGTATCTCCCA-1 1 50 102 7237
## 3 AAACAATCTACTAGCA-1 1 3 43 1611
## 4 AAACACCAATAACTGC-1 1 59 19 8315
## 5 AAACAGAGCGACTCCT-1 1 14 94 2927
## ... ... ... ... ... ...
## 46 AAAGGTCAACGACATG-1 0 0 112 1251
## 47 AAAGTAGCATTGCTCA-1 1 51 27 7357
## 48 AAAGTCACTGATGTAA-1 1 10 52 2449
## 49 AAAGTCGACCCTCAGT-1 1 37 15 5681
## 50 AAAGTGCCATCAATTA-1 0 63 125 8793
## pxl_row_in_fullres
## <integer>
## 1 2312
## 2 8230
## 3 4170
## 4 2519
## 5 7679
## ... ...
## 46 8917
## 47 3069
## 48 4789
## 49 2244
## 50 9812Image paths can be handled with the VisiumExperiment class by aid of the
imagePaths methods.
imagePaths(ve) <- imageFilePath
imagePaths(ve)## [1] "/tmp/RtmpzHSW63/Rinst2d1f10c5d97a/SpatialExperiment/extdata/10x_visium/images/tissue_lowres_image.png"Scale Factors stored during VisiumExperiment class creation can be get/set
with scaleFactors method.
scaleFactors(ve)## $spot_diameter_fullres
## [1] 89.44476
##
## $tissue_hires_scalef
## [1] 0.1701114
##
## $fiducial_diameter_fullres
## [1] 144.4877
##
## $tissue_lowres_scalef
## [1] 0.05103343The isInTissue method can be used to know which and how many spots are covered by the tissue.
sum(isInTissue(ve))## [1] 22ve[isInTissue(ve)]## class: VisiumExperiment
## dim: 22 50
## metadata(0):
## assays(1): counts
## rownames: NULL
## rowData names(3): Feature_ID Feature_name Feature_type
## colnames: NULL
## colData names(1): Cell_ID
## reducedDimNames(0):
## altExpNames(0):
## spatialCoordinates(6): Cell_ID in_tissue ... pxl_col_in_fullres
## pxl_row_in_fullres
## inTissue(1): 22
## imagePaths(1):
## /tmp/RtmpzHSW63/Rinst2d1f10c5d97a/SpatialExperiment/extdata/10x_visium/images/tissue_lowres_image.pngsessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 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
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] rjson_0.2.20 Matrix_1.2-18
## [3] SpatialExperiment_1.0.0 SingleCellExperiment_1.12.0
## [5] SummarizedExperiment_1.20.0 Biobase_2.50.0
## [7] GenomicRanges_1.42.0 GenomeInfoDb_1.26.0
## [9] IRanges_2.24.0 S4Vectors_0.28.0
## [11] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
## [13] matrixStats_0.57.0 BiocStyle_2.18.0
##
## loaded via a namespace (and not attached):
## [1] knitr_1.30 XVector_0.30.0 magrittr_1.5
## [4] zlibbioc_1.36.0 lattice_0.20-41 rlang_0.4.8
## [7] stringr_1.4.0 tools_4.0.3 grid_4.0.3
## [10] xfun_0.18 htmltools_0.5.0 yaml_2.2.1
## [13] digest_0.6.27 bookdown_0.21 GenomeInfoDbData_1.2.4
## [16] BiocManager_1.30.10 bitops_1.0-6 RCurl_1.98-1.2
## [19] evaluate_0.14 rmarkdown_2.5 DelayedArray_0.16.0
## [22] stringi_1.5.3 compiler_4.0.3