1 Available datasets

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_v3.13

2 Loading the data

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

3 Data representation

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):
## 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 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 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        97

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 altExps. E.g.:

spe <- HumanOvarianCancer()
altExpNames(spe)
## [1] "TargetedImmunology" "TargetedPanCancer"

Session information

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