STexampleData 1.11.1
The STexampleData package contains a collection of spatially resolved transcriptomics (SRT) datasets, which have been formatted into the SpatialExperiment Bioconductor class, for use in examples, demonstrations, and tutorials. The datasets are from several different SRT platforms and have been sourced from various publicly available sources. Some of the datasets include images and/or ground truth annotation labels.
To install the STexampleData package from Bioconductor:
install.packages("BiocManager")
BiocManager::install("STexampleData")Alternatively, the latest version can also be installed from GitHub:
install.packages("remotes")
remotes::install_github("lmweber/STexampleData", build_vignettes = TRUE)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.
ST_mouseOB (Spatial Transcriptomics): A single sample from the mouse brain olfactory bulb (OB), measured with the Spatial Transcriptomics platform (Stahl et al. 2016). This dataset contains annotations for five cell layers from the original authors.
SlideSeqV2_mouseHPC (Slide-seqV2): A single sample of mouse brain from the hippocampus (HPC) and surrounding regions, measured with the Slide-seqV2 platform (Stickels et al. 2020). This dataset contains cell type annotations generated by Cable et al. (2021).
The following examples show how to load the example datasets as SpatialExperiment objects in an R session.
There are two options for loading the datasets: either using named accessor functions or by querying the ExperimentHub database.
library(SpatialExperiment)
library(STexampleData)# load object
spe <- Visium_humanDLPFC()
# 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(7): barcode_id sample_id ... ground_truth cell_count
## 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 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 7 columns
## barcode_id sample_id in_tissue array_row
## <character> <character> <integer> <integer>
## AAACAACGAATAGTTC-1 AAACAACGAATAGTTC-1 sample_151673 0 0
## AAACAAGTATCTCCCA-1 AAACAAGTATCTCCCA-1 sample_151673 1 50
## AAACAATCTACTAGCA-1 AAACAATCTACTAGCA-1 sample_151673 1 3
## AAACACCAATAACTGC-1 AAACACCAATAACTGC-1 sample_151673 1 59
## AAACAGAGCGACTCCT-1 AAACAGAGCGACTCCT-1 sample_151673 1 14
## ... ... ... ... ...
## TTGTTTCACATCCAGG-1 TTGTTTCACATCCAGG-1 sample_151673 1 58
## TTGTTTCATTAGTCTA-1 TTGTTTCATTAGTCTA-1 sample_151673 1 60
## TTGTTTCCATACAACT-1 TTGTTTCCATACAACT-1 sample_151673 1 45
## TTGTTTGTATTACACG-1 TTGTTTGTATTACACG-1 sample_151673 1 73
## TTGTTTGTGTAAATTC-1 TTGTTTGTGTAAATTC-1 sample_151673 1 7
## array_col ground_truth cell_count
## <integer> <character> <integer>
## AAACAACGAATAGTTC-1 16 NA NA
## AAACAAGTATCTCCCA-1 102 Layer3 6
## AAACAATCTACTAGCA-1 43 Layer1 16
## AAACACCAATAACTGC-1 19 WM 5
## AAACAGAGCGACTCCT-1 94 Layer3 2
## ... ... ... ...
## TTGTTTCACATCCAGG-1 42 WM 3
## TTGTTTCATTAGTCTA-1 30 WM 4
## TTGTTTCCATACAACT-1 27 Layer6 3
## TTGTTTGTATTACACG-1 41 WM 16
## TTGTTTGTGTAAATTC-1 51 Layer2 5head(spatialCoords(spe))## pxl_col_in_fullres pxl_row_in_fullres
## 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()
# 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(5): barcode_id sample_id in_tissue array_row array_col
## 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# load object
spe <- seqFISH_mouseEmbryo()
# 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): cell_id embryo ... segmentation_vertices sample_id
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## spatialCoords names(2) : x y
## imgData names(0):# load object
spe <- ST_mouseOB()
# check object
spe## class: SpatialExperiment
## dim: 15928 262
## metadata(0):
## assays(1): counts
## rownames(15928): 0610007N19Rik 0610007P14Rik ... Zzef1 Zzz3
## rowData names(1): gene_name
## colnames(262): ACAACTATGGGTTGGCGG ACACAGATCCTGTTCTGA ...
## TTTCAACCCGAGGAAGTC TTTCTAACTCATAAGGAT
## colData names(3): barcode_id sample_id layer
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## spatialCoords names(2) : x y
## imgData names(0):# load object
spe <- SlideSeqV2_mouseHPC()
# check object
spe## class: SpatialExperiment
## dim: 23264 53208
## metadata(0):
## assays(1): counts
## rownames(23264): 0610005C13Rik 0610007P14Rik ... n-R5s40 n-R5s95
## rowData names(1): gene_name
## colnames(53208): AACGTCATAATCGT TACTTTAGCGCAGT ... GACTTTTCTTAAAG
## GTCAATAAAGGGCG
## colData names(3): barcode_id sample_id celltype
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## spatialCoords names(2) : xcoord ycoord
## imgData names(0):library(ExperimentHub)# create ExperimentHub instance
eh <- ExperimentHub()
# query STexampleData datasets
myfiles <- query(eh, "STexampleData")
myfiles## ExperimentHub with 5 records
## # snapshotDate(): 2024-03-11
## # $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[["EH7538"]]'
##
## title
## EH7538 | Visium_humanDLPFC
## EH7539 | Visium_mouseCoronal
## EH7540 | seqFISH_mouseEmbryo
## EH7541 | ST_mouseOB
## EH7542 | SlideSeqV2_mouseHPC# metadata
md <- as.data.frame(mcols(myfiles))## Warning in as.data.frame.integer(col, optional = optional): Direct call of
## 'as.data.frame.integer()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead
## Warning in as.data.frame.integer(col, optional = optional): Direct call of
## 'as.data.frame.integer()' is deprecated. Use 'as.data.frame.vector()' or
## 'as.data.frame()' instead# load Visium_humanDLPFC dataset using ExperimentHub query
spe <- myfiles[[1]]
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(7): barcode_id sample_id ... ground_truth cell_count
## 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# load Visium_humanDLPFC dataset using ExperimentHub ID
spe <- myfiles[["EH7538"]]
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(7): barcode_id sample_id ... ground_truth cell_count
## 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 scaleFactorFor 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 data files from the original sources for each dataset.
sessionInfo()## R Under development (unstable) (2024-03-06 r86056)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-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] BumpyMatrix_1.11.0 STexampleData_1.11.1
## [3] ExperimentHub_2.11.1 AnnotationHub_3.11.2
## [5] BiocFileCache_2.11.1 dbplyr_2.4.0
## [7] SpatialExperiment_1.13.0 SingleCellExperiment_1.25.0
## [9] SummarizedExperiment_1.33.3 Biobase_2.63.0
## [11] GenomicRanges_1.55.4 GenomeInfoDb_1.39.9
## [13] IRanges_2.37.1 S4Vectors_0.41.4
## [15] BiocGenerics_0.49.1 MatrixGenerics_1.15.0
## [17] matrixStats_1.2.0 BiocStyle_2.31.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.43.0 rjson_0.2.21 xfun_0.42
## [4] bslib_0.6.1 lattice_0.22-5 vctrs_0.6.5
## [7] tools_4.4.0 generics_0.1.3 curl_5.2.1
## [10] AnnotationDbi_1.65.2 tibble_3.2.1 fansi_1.0.6
## [13] RSQLite_2.3.5 blob_1.2.4 pkgconfig_2.0.3
## [16] Matrix_1.6-5 lifecycle_1.0.4 GenomeInfoDbData_1.2.11
## [19] compiler_4.4.0 Biostrings_2.71.4 htmltools_0.5.7
## [22] sass_0.4.9 yaml_2.3.8 pillar_1.9.0
## [25] crayon_1.5.2 jquerylib_0.1.4 DelayedArray_0.29.9
## [28] cachem_1.0.8 magick_2.8.3 abind_1.4-5
## [31] mime_0.12 tidyselect_1.2.1 digest_0.6.35
## [34] purrr_1.0.2 BiocVersion_3.19.1 dplyr_1.1.4
## [37] bookdown_0.38 fastmap_1.1.1 grid_4.4.0
## [40] cli_3.6.2 SparseArray_1.3.4 magrittr_2.0.3
## [43] S4Arrays_1.3.6 utf8_1.2.4 withr_3.0.0
## [46] rappdirs_0.3.3 filelock_1.0.3 bit64_4.0.5
## [49] rmarkdown_2.26 XVector_0.43.1 httr_1.4.7
## [52] bit_4.0.5 png_0.1-8 memoise_2.0.1
## [55] evaluate_0.23 knitr_1.45 rlang_1.1.3
## [58] Rcpp_1.0.12 glue_1.7.0 DBI_1.2.2
## [61] BiocManager_1.30.22 jsonlite_1.8.8 R6_2.5.1
## [64] zlibbioc_1.49.3