TENxVisiumData 1.4.0
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(): 2022-04-19
## # $dataprovider: 10X Genomics
## # $species: Homo sapiens, Mus musculus
## # $rdataclass: SpatialExperiment
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH6695"]]'
##
## title
## EH6695 | HumanBreastCancerIDC
## EH6696 | HumanBreastCancerILC
## EH6697 | HumanCerebellum
## EH6698 | HumanColorectalCancer
## EH6699 | HumanGlioblastoma
## ... ...
## EH6739 | HumanSpinalCord_v3.13
## EH6740 | MouseBrainCoronal_v3.13
## EH6741 | MouseBrainSagittalPosterior_v3.13
## EH6742 | MouseBrainSagittalAnterior_v3.13
## EH6743 | MouseKidneyCoronal_v3.13
To retrieve a dataset, we can use a dataset’s corresponding named function <id>()
, where <id>
should correspond to one a valid dataset identifier (see ?TENxVisiumData
). E.g.:
library(TENxVisiumData)
spe <- HumanHeart()
Alternatively, data can loaded directly from Bioconductor’s ExerimentHub as follows. First, we initialize a hub instance and store the complete list of records in a variable eh
. Using query()
, we then identify any records made available by the TENxVisiumData
package, as well as their accession IDs (EH1234). Finally, we can load the data into R via eh[[id]]
, where id
corresponds to the data entry’s identifier we’d like to load. E.g.:
library(ExperimentHub)
eh <- ExperimentHub() # initialize hub instance
q <- query(eh, "TENxVisium") # retrieve 'TENxVisiumData' records
id <- q$ah_id[1] # specify dataset ID to load
spe <- eh[[id]] # load specified dataset
Each 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):
## spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
## imgData names(4): sample_id image_id data scaleFactor
For 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 16740
spatialData
; 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 0 columns
imgData
; 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 #### 0.0247525
Datasets 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 2825
Datasets of targeted analyses are provided as a nested SPE, with whole transcriptome measurements as primary data, and those obtained from targeted panels as altExp
s. E.g.:
spe <- HumanOvarianCancer()
altExpNames(spe)
## [1] "TargetedImmunology" "TargetedPanCancer"
sessionInfo()
## R version 4.2.0 RC (2022-04-19 r82224)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.15-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_1.4.0 SpatialExperiment_1.6.0
## [3] SingleCellExperiment_1.18.0 SummarizedExperiment_1.26.0
## [5] Biobase_2.56.0 GenomicRanges_1.48.0
## [7] GenomeInfoDb_1.32.0 IRanges_2.30.0
## [9] S4Vectors_0.34.0 MatrixGenerics_1.8.0
## [11] matrixStats_0.62.0 ExperimentHub_2.4.0
## [13] AnnotationHub_3.4.0 BiocFileCache_2.4.0
## [15] dbplyr_2.1.1 BiocGenerics_0.42.0
## [17] BiocStyle_2.24.0
##
## 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_4.2.0 bslib_0.3.1
## [7] utf8_1.2.2 R6_2.5.1
## [9] HDF5Array_1.24.0 DBI_1.1.2
## [11] rhdf5filters_1.8.0 withr_2.5.0
## [13] tidyselect_1.1.2 bit_4.0.4
## [15] curl_4.3.2 compiler_4.2.0
## [17] cli_3.3.0 DelayedArray_0.22.0
## [19] bookdown_0.26 sass_0.4.1
## [21] rappdirs_0.3.3 stringr_1.4.0
## [23] digest_0.6.29 rmarkdown_2.14
## [25] R.utils_2.11.0 XVector_0.36.0
## [27] pkgconfig_2.0.3 htmltools_0.5.2
## [29] sparseMatrixStats_1.8.0 limma_3.52.0
## [31] fastmap_1.1.0 rlang_1.0.2
## [33] RSQLite_2.2.12 shiny_1.7.1
## [35] DelayedMatrixStats_1.18.0 jquerylib_0.1.4
## [37] generics_0.1.2 jsonlite_1.8.0
## [39] BiocParallel_1.30.0 dplyr_1.0.8
## [41] R.oo_1.24.0 RCurl_1.98-1.6
## [43] magrittr_2.0.3 scuttle_1.6.0
## [45] GenomeInfoDbData_1.2.8 Matrix_1.4-1
## [47] Rcpp_1.0.8.3 Rhdf5lib_1.18.0
## [49] fansi_1.0.3 lifecycle_1.0.1
## [51] R.methodsS3_1.8.1 edgeR_3.38.0
## [53] stringi_1.7.6 yaml_2.3.5
## [55] zlibbioc_1.42.0 rhdf5_2.40.0
## [57] grid_4.2.0 blob_1.2.3
## [59] parallel_4.2.0 promises_1.2.0.1
## [61] dqrng_0.3.0 crayon_1.5.1
## [63] lattice_0.20-45 Biostrings_2.64.0
## [65] beachmat_2.12.0 KEGGREST_1.36.0
## [67] magick_2.7.3 locfit_1.5-9.5
## [69] knitr_1.39 pillar_1.7.0
## [71] rjson_0.2.21 glue_1.6.2
## [73] BiocVersion_3.15.2 evaluate_0.15
## [75] BiocManager_1.30.17 png_0.1-7
## [77] vctrs_0.4.1 httpuv_1.6.5
## [79] purrr_0.3.4 assertthat_0.2.1
## [81] cachem_1.0.6 xfun_0.30
## [83] DropletUtils_1.16.0 mime_0.12
## [85] xtable_1.8-4 later_1.3.0
## [87] tibble_3.1.6 AnnotationDbi_1.58.0
## [89] memoise_2.0.1 ellipsis_0.3.2
## [91] interactiveDisplayBase_1.34.0