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(): 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

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 4.2.0 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