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(): 2023-10-24
## # $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):
## 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 altExps. E.g.:

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

Session information

sessionInfo()
## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.18-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] Matrix_1.6-1.1              TENxVisiumData_1.10.0      
##  [3] SpatialExperiment_1.12.0    SingleCellExperiment_1.24.0
##  [5] SummarizedExperiment_1.32.0 Biobase_2.62.0             
##  [7] GenomicRanges_1.54.0        GenomeInfoDb_1.38.0        
##  [9] IRanges_2.36.0              S4Vectors_0.40.0           
## [11] MatrixGenerics_1.14.0       matrixStats_1.0.0          
## [13] ExperimentHub_2.10.0        AnnotationHub_3.10.0       
## [15] BiocFileCache_2.10.0        dbplyr_2.3.4               
## [17] BiocGenerics_0.48.0         BiocStyle_2.30.0           
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.0              dplyr_1.1.3                  
##  [3] blob_1.2.4                    filelock_1.0.2               
##  [5] Biostrings_2.70.1             bitops_1.0-7                 
##  [7] fastmap_1.1.1                 RCurl_1.98-1.12              
##  [9] promises_1.2.1                digest_0.6.33                
## [11] mime_0.12                     lifecycle_1.0.3              
## [13] ellipsis_0.3.2                KEGGREST_1.42.0              
## [15] interactiveDisplayBase_1.40.0 RSQLite_2.3.1                
## [17] magrittr_2.0.3                compiler_4.3.1               
## [19] rlang_1.1.1                   sass_0.4.7                   
## [21] tools_4.3.1                   utf8_1.2.4                   
## [23] yaml_2.3.7                    knitr_1.44                   
## [25] S4Arrays_1.2.0                bit_4.0.5                    
## [27] curl_5.1.0                    DelayedArray_0.28.0          
## [29] abind_1.4-5                   withr_2.5.1                  
## [31] purrr_1.0.2                   grid_4.3.1                   
## [33] fansi_1.0.5                   xtable_1.8-4                 
## [35] cli_3.6.1                     rmarkdown_2.25               
## [37] crayon_1.5.2                  generics_0.1.3               
## [39] rjson_0.2.21                  httr_1.4.7                   
## [41] DBI_1.1.3                     cachem_1.0.8                 
## [43] zlibbioc_1.48.0               AnnotationDbi_1.64.0         
## [45] BiocManager_1.30.22           XVector_0.42.0               
## [47] vctrs_0.6.4                   jsonlite_1.8.7               
## [49] bookdown_0.36                 bit64_4.0.5                  
## [51] magick_2.8.1                  jquerylib_0.1.4              
## [53] glue_1.6.2                    BiocVersion_3.18.0           
## [55] later_1.3.1                   tibble_3.2.1                 
## [57] pillar_1.9.0                  rappdirs_0.3.3               
## [59] htmltools_0.5.6.1             GenomeInfoDbData_1.2.11      
## [61] R6_2.5.1                      evaluate_0.22                
## [63] shiny_1.7.5.1                 lattice_0.22-5               
## [65] png_0.1-8                     memoise_2.0.1                
## [67] httpuv_1.6.12                 bslib_0.5.1                  
## [69] Rcpp_1.0.11                   SparseArray_1.2.0            
## [71] xfun_0.40                     pkgconfig_2.0.3