TENxVisiumData 1.2.0
The TENxVisiumData package provides an R/Bioconductor resource for
Visium spatial gene expression datasets by 10X Genomics. The package currently includes 13 datasets from 23 samples across two organisms (human and mouse) and 13 tissues:
A list of currently available datasets can be obtained using the ExperimentHub interface:
library(ExperimentHub)
eh <- ExperimentHub()
(q <- query(eh, "TENxVisium"))## ExperimentHub with 26 records
## # snapshotDate(): 2021-10-18
## # $dataprovider: 10X Genomics
## # $species: Homo sapiens, Mus musculus
## # $rdataclass: SpatialExperiment
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH6695"]]'
##
## title
## EH6695 | HumanBreastCancerIDC
## EH6696 | HumanBreastCancerILC
## EH6697 | HumanCerebellum
## EH6698 | HumanColorectalCancer
## EH6699 | HumanGlioblastoma
## ... ...
## EH6739 | HumanSpinalCord_v3.13
## EH6740 | MouseBrainCoronal_v3.13
## EH6741 | MouseBrainSagittalPosterior_v3.13
## EH6742 | MouseBrainSagittalAnterior_v3.13
## EH6743 | MouseKidneyCoronal_v3.13To retrieve a dataset, we can use a dataset’s corresponding named function <id>(), where <id> should correspond to one a valid dataset identifier (see ?TENxVisiumData). E.g.:
library(TENxVisiumData)
spe <- HumanHeart()Alternatively, data can loaded directly from Bioconductor’s ExerimentHub as follows. First, we initialize a hub instance and store the complete list of records in a variable eh. Using query(), we then identify any records made available by the TENxVisiumData package, as well as their accession IDs (EH1234). Finally, we can load the data into R via eh[[id]], where id corresponds to the data entry’s identifier we’d like to load. E.g.:
library(ExperimentHub)
eh <- ExperimentHub() # initialize hub instance
q <- query(eh, "TENxVisium") # retrieve 'TENxVisiumData' records
id <- q$ah_id[1] # specify dataset ID to load
spe <- eh[[id]] # load specified datasetEach dataset is provided as a SpatialExperiment (SPE), which extends the SingleCellExperiment (SCE) class with features specific to spatially resolved data:
spe## class: SpatialExperiment
## dim: 36601 7785
## metadata(0):
## assays(1): counts
## rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
## ENSG00000277196
## rowData names(1): symbol
## colnames(7785): AAACAAGTATCTCCCA-1 AAACACCAATAACTGC-1 ...
## TTGTTTGTATTACACG-1 TTGTTTGTGTAAATTC-1
## colData names(1): sample_id
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## spatialData names(3) : in_tissue array_row array_col
## spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
## imgData names(4): sample_id image_id data scaleFactorFor details on the SPE class, we refer to the package’s vignette. Briefly, the SPE harbors the following data in addition to that stored in a SCE:
spatialCoords; a numeric matrix of spatial coordinates, stored inside the object’s int_colData:
head(spatialCoords(spe))## pxl_col_in_fullres pxl_row_in_fullres
## AAACAAGTATCTCCCA-1 15937 17428
## AAACACCAATAACTGC-1 18054 6092
## AAACAGAGCGACTCCT-1 7383 16351
## AAACAGGGTCTATATT-1 15202 5278
## AAACAGTGTTCCTGGG-1 21386 9363
## AAACATTTCCCGGATT-1 18549 16740spatialData; a DFrame of spatially-related sample metadata, stored as part of the object’s colData. This colData subset is in turn determined by the int_metadata field spatialDataNames:
head(spatialData(spe))## DataFrame with 6 rows and 3 columns
## in_tissue array_row array_col
## <logical> <integer> <integer>
## AAACAAGTATCTCCCA-1 TRUE 50 102
## AAACACCAATAACTGC-1 TRUE 59 19
## AAACAGAGCGACTCCT-1 TRUE 14 94
## AAACAGGGTCTATATT-1 TRUE 47 13
## AAACAGTGTTCCTGGG-1 TRUE 73 43
## AAACATTTCCCGGATT-1 TRUE 61 97imgData; a DFrame containing image-related data, stored inside the int_metadata:
imgData(spe)## DataFrame with 2 rows and 4 columns
## sample_id image_id data scaleFactor
## <character> <character> <list> <numeric>
## 1 HumanBreastCancerIDC1 lowres #### 0.0247525
## 2 HumanBreastCancerIDC2 lowres #### 0.0247525Datasets with multiple sections are consolidated into a single SPE with colData field sample_id indicating each spot’s sample of origin. E.g.:
spe <- MouseBrainSagittalAnterior()
table(spe$sample_id)##
## MouseBrainSagittalAnterior1 MouseBrainSagittalAnterior2
## 2695 2825Datasets of targeted analyses are provided as a nested SPE, with whole transcriptome measurements as primary data, and those obtained from targeted panels as altExps. E.g.:
spe <- HumanOvarianCancer()
altExpNames(spe)## [1] "TargetedImmunology" "TargetedPanCancer"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] TENxVisiumData_1.2.0 SpatialExperiment_1.4.0
## [3] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
## [5] Biobase_2.54.0 GenomicRanges_1.46.0
## [7] GenomeInfoDb_1.30.0 IRanges_2.28.0
## [9] S4Vectors_0.32.0 MatrixGenerics_1.6.0
## [11] matrixStats_0.61.0 ExperimentHub_2.2.0
## [13] AnnotationHub_3.2.0 BiocFileCache_2.2.0
## [15] dbplyr_2.1.1 BiocGenerics_0.40.0
## [17] 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