STexampleData 1.2.0
The STexampleData package provides access to a collection of spatially resolved transcriptomics datasets, which have been formatted into the SpatialExperiment Bioconductor object class.
These datasets have been collected from various publicly available sources, and cover several technological platforms. We provide them in the form of SpatialExperiment objects to make them easier to access, so that we and others can use them for examples, demonstrations, tutorials, and other purposes.
The SpatialExperiment class is an extension of SingleCellExperiment, adapted for the properties of spatially resolved transcriptomics data. For more details, see the SpatialExperiment documentation.
To install the STexampleData package from Bioconductor:
install.packages("BiocManager")
BiocManager::install("STexampleData")Alternatively, the latest version can also be installed from GitHub:
remotes::install_github("lmweber/STexampleData")The package contains the following datasets:
Visium_humanDLPFC (10x Genomics Visium): A single sample (sample 151673) of human brain dorsolateral prefrontal cortex (DLPFC) in the human brain, measured using the 10x Genomics Visium platform. This is a subset of the full dataset containing 12 samples from 3 neurotypical donors, published by Maynard and Collado-Torres et al. (2021). The full dataset is available from the spatialLIBD Bioconductor package.
Visium_mouseCoronal (10x Genomics Visium): A single coronal section from the mouse brain, spanning one hemisphere. This dataset was previously released by 10x Genomics on their website.
seqFISH_mouseEmbryo (seqFISH): A subset of cells (embryo 1, z-slice 2) from a previously published dataset investigating mouse embryogenesis by Lohoff and Ghazanfar et al. (2020), generated using the seqFISH platform. The full dataset is available online.
The following examples show how to load the example datasets as SpatialExperiment objects in an R session.
library(ExperimentHub)# create ExperimentHub instance
eh <- ExperimentHub()## snapshotDate(): 2021-10-18# query STexampleData datasets
myfiles <- query(eh, "STexampleData")
myfiles## ExperimentHub with 6 records
## # snapshotDate(): 2021-10-18
## # $dataprovider: NA
## # $species: Mus musculus, Homo sapiens
## # $rdataclass: SpatialExperiment
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH5443"]]'
##
## title
## EH5443 | Visium_humanDLPFC_3_13
## EH5444 | Visium_mouseCoronal_3_13
## EH5445 | seqFISH_mouseEmbryo_3_13
## EH6708 | Visium_humanDLPFC
## EH6709 | Visium_mouseCoronal
## EH6710 | seqFISH_mouseEmbryo# metadata
md <- as.data.frame(mcols(myfiles))library(STexampleData)
library(SpatialExperiment)# load object
spe <- Visium_humanDLPFC()## see ?STexampleData and browseVignettes('STexampleData') for documentation## loading from cache# alternatively: using ExperimentHub query
# spe <- myfiles[[1]]
# alternatively: using ExperimentHub ID
# spe <- myfiles[["EH6708"]]
# check object
spe## class: SpatialExperiment
## dim: 33538 4992
## metadata(0):
## assays(1): counts
## rownames(33538): ENSG00000243485 ENSG00000237613 ... ENSG00000277475
## ENSG00000268674
## rowData names(3): gene_id gene_name feature_type
## colnames(4992): AAACAACGAATAGTTC-1 AAACAAGTATCTCCCA-1 ...
## TTGTTTGTATTACACG-1 TTGTTTGTGTAAATTC-1
## colData names(3): cell_count ground_truth sample_id
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## spatialData names(6) : barcode_id in_tissue ... pxl_col_in_fullres
## pxl_row_in_fullres
## spatialCoords names(2) : x y
## imgData names(4): sample_id image_id data scaleFactordim(spe)## [1] 33538 4992assayNames(spe)## [1] "counts"rowData(spe)## DataFrame with 33538 rows and 3 columns
## gene_id gene_name feature_type
## <character> <character> <character>
## ENSG00000243485 ENSG00000243485 MIR1302-2HG Gene Expression
## ENSG00000237613 ENSG00000237613 FAM138A Gene Expression
## ENSG00000186092 ENSG00000186092 OR4F5 Gene Expression
## ENSG00000238009 ENSG00000238009 AL627309.1 Gene Expression
## ENSG00000239945 ENSG00000239945 AL627309.3 Gene Expression
## ... ... ... ...
## ENSG00000277856 ENSG00000277856 AC233755.2 Gene Expression
## ENSG00000275063 ENSG00000275063 AC233755.1 Gene Expression
## ENSG00000271254 ENSG00000271254 AC240274.1 Gene Expression
## ENSG00000277475 ENSG00000277475 AC213203.1 Gene Expression
## ENSG00000268674 ENSG00000268674 FAM231C Gene ExpressioncolData(spe)## DataFrame with 4992 rows and 3 columns
## cell_count ground_truth sample_id
## <integer> <factor> <character>
## AAACAACGAATAGTTC-1 NA NA sample_151673
## AAACAAGTATCTCCCA-1 6 Layer3 sample_151673
## AAACAATCTACTAGCA-1 16 Layer1 sample_151673
## AAACACCAATAACTGC-1 5 WM sample_151673
## AAACAGAGCGACTCCT-1 2 Layer3 sample_151673
## ... ... ... ...
## TTGTTTCACATCCAGG-1 3 WM sample_151673
## TTGTTTCATTAGTCTA-1 4 WM sample_151673
## TTGTTTCCATACAACT-1 3 Layer6 sample_151673
## TTGTTTGTATTACACG-1 16 WM sample_151673
## TTGTTTGTGTAAATTC-1 5 Layer2 sample_151673spatialData(spe)## DataFrame with 4992 rows and 6 columns
## barcode_id in_tissue array_row array_col
## <character> <integer> <integer> <integer>
## AAACAACGAATAGTTC-1 AAACAACGAATAGTTC-1 0 0 16
## AAACAAGTATCTCCCA-1 AAACAAGTATCTCCCA-1 1 50 102
## AAACAATCTACTAGCA-1 AAACAATCTACTAGCA-1 1 3 43
## AAACACCAATAACTGC-1 AAACACCAATAACTGC-1 1 59 19
## AAACAGAGCGACTCCT-1 AAACAGAGCGACTCCT-1 1 14 94
## ... ... ... ... ...
## TTGTTTCACATCCAGG-1 TTGTTTCACATCCAGG-1 1 58 42
## TTGTTTCATTAGTCTA-1 TTGTTTCATTAGTCTA-1 1 60 30
## TTGTTTCCATACAACT-1 TTGTTTCCATACAACT-1 1 45 27
## TTGTTTGTATTACACG-1 TTGTTTGTATTACACG-1 1 73 41
## TTGTTTGTGTAAATTC-1 TTGTTTGTGTAAATTC-1 1 7 51
## pxl_col_in_fullres pxl_row_in_fullres
## <integer> <integer>
## AAACAACGAATAGTTC-1 2435 3913
## AAACAAGTATCTCCCA-1 8468 9791
## AAACAATCTACTAGCA-1 2807 5769
## AAACACCAATAACTGC-1 9505 4068
## AAACAGAGCGACTCCT-1 4151 9271
## ... ... ...
## TTGTTTCACATCCAGG-1 9396 5653
## TTGTTTCATTAGTCTA-1 9630 4825
## TTGTTTCCATACAACT-1 7831 4631
## TTGTTTGTATTACACG-1 11193 5571
## TTGTTTGTGTAAATTC-1 3291 6317head(spatialCoords(spe))## x y
## AAACAACGAATAGTTC-1 3913 2435
## AAACAAGTATCTCCCA-1 9791 8468
## AAACAATCTACTAGCA-1 5769 2807
## AAACACCAATAACTGC-1 4068 9505
## AAACAGAGCGACTCCT-1 9271 4151
## AAACAGCTTTCAGAAG-1 3393 7583imgData(spe)## DataFrame with 2 rows and 4 columns
## sample_id image_id data scaleFactor
## <character> <character> <list> <numeric>
## 1 sample_151673 lowres #### 0.0450045
## 2 sample_151673 hires #### 0.1500150# load object
spe <- Visium_mouseCoronal()
# alternatively: using ExperimentHub query
# spe <- myfiles[[2]]
# alternatively: using ExperimentHub ID
# spe <- myfiles[["EH6709"]]
# check object
spe## class: SpatialExperiment
## dim: 32285 4992
## metadata(0):
## assays(1): counts
## rownames(32285): ENSMUSG00000051951 ENSMUSG00000089699 ...
## ENSMUSG00000095019 ENSMUSG00000095041
## rowData names(3): gene_id gene_name feature_type
## colnames(4992): AAACAACGAATAGTTC-1 AAACAAGTATCTCCCA-1 ...
## TTGTTTGTATTACACG-1 TTGTTTGTGTAAATTC-1
## colData names(1): sample_id
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## spatialData names(6) : barcode_id in_tissue ... pxl_col_in_fullres
## pxl_row_in_fullres
## spatialCoords names(2) : x y
## imgData names(4): sample_id image_id data scaleFactor# load object
spe <- seqFISH_mouseEmbryo()
# alternatively: using ExperimentHub query
# spe <- myfiles[[3]]
# alternatively: using ExperimentHub ID
# spe <- myfiles[["EH6710"]]
# check object
spe## class: SpatialExperiment
## dim: 351 11026
## metadata(0):
## assays(2): counts molecules
## rownames(351): Abcc4 Acp5 ... Zfp57 Zic3
## rowData names(1): gene_name
## colnames(11026): embryo1_Pos0_cell10_z2 embryo1_Pos0_cell100_z2 ...
## embryo1_Pos28_cell97_z2 embryo1_Pos28_cell98_z2
## colData names(14): uniqueID embryo ... segmentation_vertices sample_id
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## spatialData names(3) : cell_id x_global_affine y_global_affine
## spatialCoords names(2) : x y
## imgData names(0):For reference, we include code scripts to generate the SpatialExperiment objects from the raw data files.
These scripts are saved in /inst/scripts/ in the source code of the STexampleData package. The scripts include references and links to the raw data files from the original sources for each dataset.
sessionInfo()## R version 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.14-bioc/R/lib/libRlapack.so
##
## 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
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] BumpyMatrix_1.2.0 STexampleData_1.2.0
## [3] SpatialExperiment_1.4.0 SingleCellExperiment_1.16.0
## [5] SummarizedExperiment_1.24.0 Biobase_2.54.0
## [7] GenomicRanges_1.46.0 GenomeInfoDb_1.30.0
## [9] IRanges_2.28.0 S4Vectors_0.32.0
## [11] MatrixGenerics_1.6.0 matrixStats_0.61.0
## [13] ExperimentHub_2.2.0 AnnotationHub_3.2.0
## [15] BiocFileCache_2.2.0 dbplyr_2.1.1
## [17] BiocGenerics_0.40.0 BiocStyle_2.22.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 bit64_4.0.5
## [3] filelock_1.0.2 httr_1.4.2
## [5] tools_4.1.1 bslib_0.3.1
## [7] utf8_1.2.2 R6_2.5.1
## [9] HDF5Array_1.22.0 DBI_1.1.1
## [11] rhdf5filters_1.6.0 withr_2.4.2
## [13] tidyselect_1.1.1 bit_4.0.4
## [15] curl_4.3.2 compiler_4.1.1
## [17] DelayedArray_0.20.0 bookdown_0.24
## [19] sass_0.4.0 rappdirs_0.3.3
## [21] stringr_1.4.0 digest_0.6.28
## [23] rmarkdown_2.11 R.utils_2.11.0
## [25] XVector_0.34.0 pkgconfig_2.0.3
## [27] htmltools_0.5.2 sparseMatrixStats_1.6.0
## [29] limma_3.50.0 fastmap_1.1.0
## [31] rlang_0.4.12 RSQLite_2.2.8
## [33] shiny_1.7.1 DelayedMatrixStats_1.16.0
## [35] jquerylib_0.1.4 generics_0.1.1
## [37] jsonlite_1.7.2 BiocParallel_1.28.0
## [39] dplyr_1.0.7 R.oo_1.24.0
## [41] RCurl_1.98-1.5 magrittr_2.0.1
## [43] scuttle_1.4.0 GenomeInfoDbData_1.2.7
## [45] Matrix_1.3-4 Rcpp_1.0.7
## [47] Rhdf5lib_1.16.0 fansi_0.5.0
## [49] lifecycle_1.0.1 R.methodsS3_1.8.1
## [51] edgeR_3.36.0 stringi_1.7.5
## [53] yaml_2.2.1 zlibbioc_1.40.0
## [55] rhdf5_2.38.0 grid_4.1.1
## [57] blob_1.2.2 parallel_4.1.1
## [59] promises_1.2.0.1 dqrng_0.3.0
## [61] crayon_1.4.1 lattice_0.20-45
## [63] Biostrings_2.62.0 beachmat_2.10.0
## [65] KEGGREST_1.34.0 magick_2.7.3
## [67] locfit_1.5-9.4 knitr_1.36
## [69] pillar_1.6.4 rjson_0.2.20
## [71] glue_1.4.2 BiocVersion_3.14.0
## [73] evaluate_0.14 BiocManager_1.30.16
## [75] png_0.1-7 vctrs_0.3.8
## [77] httpuv_1.6.3 purrr_0.3.4
## [79] assertthat_0.2.1 cachem_1.0.6
## [81] xfun_0.27 DropletUtils_1.14.0
## [83] mime_0.12 xtable_1.8-4
## [85] later_1.3.0 tibble_3.1.5
## [87] AnnotationDbi_1.56.1 memoise_2.0.0
## [89] ellipsis_0.3.2 interactiveDisplayBase_1.32.0