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:

• HumanBreastCancerIDC
  • Human Breast Cancer (Block A Section 1)
  • Human Breast Cancer (Block A Section 2)
• HumanBreastCancerILC
  • Human Breast Cancer: Whole Transcriptome Analysis
  • Human Breast Cancer: Targeted, Immunology Panel
• HumanCerebellum
  • Human Cerebellum: Whole Transcriptome Analysis
  • Human Cerebellum: Targeted, Neuroscience Panel
• HumanColorectalCancer
  • Human Colorectal Cancer: Whole Transcriptome Analysis
  • Human Colorectal Cancer: Targeted, Gene Signature Panel
• HumanGlioblastoma
  • Human Glioblastoma: Whole Transcriptome Analysis
  • Human Glioblastoma: Targeted, Pan-Cancer Panel
• HumanHeart
  • Human Heart
• HumanLymphNode
  • Human Lymph Node
• HumanOvarianCancer
  • Human Ovarian Cancer: Whole Transcriptome Analysis
  • Human Ovarian Cancer: Targeted, Immunology Panel
  • Human Ovarian Cancer: Targeted, Pan-Cancer Panel
• HumanSpinalCord
  • Human Spinal Cord: Whole Transcriptome Analysis
  • Human Spinal Cord: Targeted, Neuroscience Panel
• MouseBrainCoronal
  • Mouse Brain Section (Coronal)
• MouseBrainSagittalAnterior
  • Mouse Brain Serial Section 1 (Sagittal-Anterior)
  • Mouse Brain Serial Section 2 (Sagittal-Anterior)
• MouseBrainSagittalPosterior
  • Mouse Brain Serial Section 1 (Sagittal-Posterior)
  • Mouse Brain Serial Section 2 (Sagittal-Posterior)
• MouseKidneyCoronal
  • Mouse Kidney Section (Coronal)

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(): 2026-10-07
## # $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

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

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"