imageFeatureTCGA

library(imageFeatureTCGA)
library(dplyr)

Overview

imageFeatureTCGA (imageTCGA) provides convenient access to histopathology-derived data from TCGA through two complementary pipelines:

HoVerNet → cell segmentation and classification
ProvGigaPath → slide- and tile-level embeddings

These datasets can be imported directly into R as Bioconductor objects, facilitating downstream integration with TCGA omics and clinical data.

Installation

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("waldronlab/imageFeatureTCGA")

Available Data

Use the following function to download the catalog of available files:

getCatalog()
#> # A tibble: 54,253 × 25
#>    pipeline format  filename    fullpath fnsansext tcga_barcode Case.ID TSS.Code
#>    <chr>    <chr>   <chr>       <chr>    <chr>     <chr>        <chr>   <chr>   
#>  1 hovernet geojson TCGA-02-00… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  2 hovernet geojson TCGA-02-00… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  3 hovernet geojson TCGA-02-00… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  4 hovernet geojson TCGA-02-00… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  5 hovernet geojson TCGA-02-00… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  6 hovernet geojson TCGA-02-00… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  7 hovernet geojson TCGA-02-00… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  8 hovernet geojson TCGA-02-00… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  9 hovernet geojson TCGA-02-00… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#> 10 hovernet geojson TCGA-02-00… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#> # ℹ 54,243 more rows
#> # ℹ 17 more variables: File.ID <chr>, File.Name <chr>, Data.Category <chr>,
#> #   Data.Type <chr>, Project.ID <chr>, Sample.ID <chr>, Sample.Type <chr>,
#> #   Source.Site <chr>, Study.Name <chr>, BCR <chr>, city <chr>, state <chr>,
#> #   country <chr>, bcr_patient_uuid <chr>, lat <dbl>, lon <dbl>, level <chr>

Formats

HoVerNet data is available in JSON, GeoJSON, thumb and H5AD formats.
ProvGigaPath data is available in CSV format.

Note that the thumb format refers to the png thumbnails of the whole-slide images.

HoVerNet data

getCatalog("hovernet")
#> # A tibble: 33,177 × 25
#>    pipeline format  filename    fullpath fnsansext tcga_barcode Case.ID TSS.Code
#>    <chr>    <chr>   <chr>       <chr>    <chr>     <chr>        <chr>   <chr>   
#>  1 hovernet geojson TCGA-02-00… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  2 hovernet geojson TCGA-02-00… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  3 hovernet geojson TCGA-02-00… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  4 hovernet geojson TCGA-02-00… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  5 hovernet geojson TCGA-02-00… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  6 hovernet geojson TCGA-02-00… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  7 hovernet geojson TCGA-02-00… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  8 hovernet geojson TCGA-02-00… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  9 hovernet geojson TCGA-02-00… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#> 10 hovernet geojson TCGA-02-00… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#> # ℹ 33,167 more rows
#> # ℹ 17 more variables: File.ID <chr>, File.Name <chr>, Data.Category <chr>,
#> #   Data.Type <chr>, Project.ID <chr>, Sample.ID <chr>, Sample.Type <chr>,
#> #   Source.Site <chr>, Study.Name <chr>, BCR <chr>, city <chr>, state <chr>,
#> #   country <chr>, bcr_patient_uuid <chr>, lat <dbl>, lon <dbl>, level <chr>

ProvGigaPath data

getCatalog("provgigapath")
#> # A tibble: 21,076 × 25
#>    pipeline     format filename fullpath fnsansext tcga_barcode Case.ID TSS.Code
#>    <chr>        <chr>  <chr>    <chr>    <chr>     <chr>        <chr>   <chr>   
#>  1 provgigapath csv    TCGA-02… provgig… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  2 provgigapath csv    TCGA-02… provgig… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  3 provgigapath csv    TCGA-02… provgig… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  4 provgigapath csv    TCGA-02… provgig… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  5 provgigapath csv    TCGA-02… provgig… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  6 provgigapath csv    TCGA-02… provgig… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  7 provgigapath csv    TCGA-02… provgig… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  8 provgigapath csv    TCGA-02… provgig… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#>  9 provgigapath csv    TCGA-02… provgig… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#> 10 provgigapath csv    TCGA-02… provgig… TCGA-02-… TCGA-02-000… TCGA-0… 02      
#> # ℹ 21,066 more rows
#> # ℹ 17 more variables: File.ID <chr>, File.Name <chr>, Data.Category <chr>,
#> #   Data.Type <chr>, Project.ID <chr>, Sample.ID <chr>, Sample.Type <chr>,
#> #   Source.Site <chr>, Study.Name <chr>, BCR <chr>, city <chr>, state <chr>,
#> #   country <chr>, bcr_patient_uuid <chr>, lat <dbl>, lon <dbl>, level <chr>

Importing HoVerNet data

You can import HoVerNet segmentation results as a SpatialExperiment or SpatialFeatureExperiment.

getCatalog("hovernet") |>
    dplyr::filter(
        filename == paste(
            "TCGA-VG-A8LO-01A-01-DX1",
            "B39A4D64-82A1-4A04-8AB6-918F3058B83B",
            "json",
            "gz",
            sep = "."
        )
    ) |>
    getFileURLs() |>
    HoverNet(outClass = "SpatialExperiment") |>
    import()
#> adding rname 'https://store.cancerdatasci.org/hovernet/json/TCGA-VG-A8LO-01A-01-DX1.B39A4D64-82A1-4A04-8AB6-918F3058B83B.json.gz'
#> class: SpatialExperiment 
#> dim: 0 67081 
#> metadata(1): type_map
#> assays(1): counts
#> rownames: NULL
#> rowData names(0):
#> colnames: NULL
#> colData names(10): cell_id x ... B sample_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : x y
#> imgData names(0):

Each cell is represented with:

x, y spatial coordinates
cell type and type probabilities
optional contours stored in metadata

Importing ProvGigaPath embeddings

80

ProvGigaPath embeddings summarize tile or slide-level image features.

getCatalog("provgigapath") |>
    dplyr::filter(
        filename == paste(
            "TCGA-VG-A8LO-01A-01-DX1",
            "B39A4D64-82A1-4A04-8AB6-918F3058B83B",
            "csv",
            "gz",
            sep = "."
        ) &
        level == "slide_level"
    ) |>
    getFileURLs() |>
    ProvGiga() |>
    import()
#> adding rname 'https://store.cancerdatasci.org/provgigapath/slide_level/TCGA-VG-A8LO-01A-01-DX1.B39A4D64-82A1-4A04-8AB6-918F3058B83B.csv.gz'
#> # A tibble: 1 × 771
#>   slideName     tumorType fileName     V1    V2     V3     V4     V5    V6    V7
#>   <chr>         <chr>     <chr>     <dbl> <dbl>  <dbl>  <dbl>  <dbl> <dbl> <dbl>
#> 1 TCGA-VG-A8LO… <NA>      TCGA-VG… -0.355 0.584 -0.402 -0.527 0.0351 0.205 -1.14
#> # ℹ 761 more variables: V8 <dbl>, V9 <dbl>, V10 <dbl>, V11 <dbl>, V12 <dbl>,
#> #   V13 <dbl>, V14 <dbl>, V15 <dbl>, V16 <dbl>, V17 <dbl>, V18 <dbl>,
#> #   V19 <dbl>, V20 <dbl>, V21 <dbl>, V22 <dbl>, V23 <dbl>, V24 <dbl>,
#> #   V25 <dbl>, V26 <dbl>, V27 <dbl>, V28 <dbl>, V29 <dbl>, V30 <dbl>,
#> #   V31 <dbl>, V32 <dbl>, V33 <dbl>, V34 <dbl>, V35 <dbl>, V36 <dbl>,
#> #   V37 <dbl>, V38 <dbl>, V39 <dbl>, V40 <dbl>, V41 <dbl>, V42 <dbl>,
#> #   V43 <dbl>, V44 <dbl>, V45 <dbl>, V46 <dbl>, V47 <dbl>, V48 <dbl>, …

Each row corresponds to a slide, with an embedding vector describing the image-derived features.

Shiny App: imageTCGA

The imageTCGA Shiny application provides an interactive interface for exploring TCGA Diagnostic Image Database metadata.

Click here to explore the shiny app: imageTCGA

Session Info

Click here for Session Info

sessionInfo()
#> R Under development (unstable) (2025-10-28 r88973)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.5 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0  LAPACK version 3.10.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [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] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] dplyr_1.1.4              ImageFeatureTCGA_0.99.33
#> 
#> loaded via a namespace (and not attached):
#>  [1] tidyselect_1.2.1            farver_2.1.2               
#>  [3] blob_1.2.4                  bitops_1.0-9               
#>  [5] filelock_1.0.3              S7_0.2.1                   
#>  [7] RCurl_1.98-1.17             fastmap_1.2.0              
#>  [9] SingleCellExperiment_1.33.0 BiocFileCache_3.1.0        
#> [11] promises_1.5.0              digest_0.6.39              
#> [13] lifecycle_1.0.5             processx_3.8.6             
#> [15] RSQLite_2.4.5               magrittr_2.0.4             
#> [17] compiler_4.6.0              rlang_1.1.6                
#> [19] tools_4.6.0                 utf8_1.2.6                 
#> [21] yaml_2.3.12                 data.table_1.18.0          
#> [23] knitr_1.51                  S4Arrays_1.11.1            
#> [25] bit_4.6.0                   curl_7.0.0                 
#> [27] DelayedArray_0.37.0         xml2_1.5.1                 
#> [29] RColorBrewer_1.1-3          abind_1.4-8                
#> [31] websocket_1.4.4             withr_3.0.2                
#> [33] purrr_1.2.0                 BiocGenerics_0.57.0        
#> [35] grid_4.6.0                  stats4_4.6.0               
#> [37] ggplot2_4.0.1               scales_1.4.0               
#> [39] dichromat_2.0-0.1           SummarizedExperiment_1.41.0
#> [41] cli_3.6.5                   rmarkdown_2.30             
#> [43] crayon_1.5.3                generics_0.1.4             
#> [45] otel_0.2.0                  rstudioapi_0.17.1          
#> [47] httr_1.4.7                  tzdb_0.5.0                 
#> [49] rjson_0.2.23                BiocBaseUtils_1.13.0       
#> [51] cachem_1.1.0                DBI_1.2.3                  
#> [53] chromote_0.5.1              parallel_4.6.0             
#> [55] rvest_1.0.5                 BiocManager_1.30.27        
#> [57] XVector_0.51.0              matrixStats_1.5.0          
#> [59] vctrs_0.6.5                 Matrix_1.7-4               
#> [61] jsonlite_2.0.0              IRanges_2.45.0             
#> [63] hms_1.1.4                   S4Vectors_0.49.0           
#> [65] bit64_4.6.0-1               archive_1.1.12             
#> [67] TENxIO_1.13.2               magick_2.9.0               
#> [69] glue_1.8.0                  codetools_0.2-20           
#> [71] ps_1.9.1                    cowplot_1.2.0              
#> [73] gtable_0.3.6                later_1.4.5                
#> [75] BiocIO_1.21.0               GenomicRanges_1.63.1       
#> [77] tibble_3.3.0                pillar_1.11.1              
#> [79] rappdirs_0.3.3              htmltools_0.5.9            
#> [81] Seqinfo_1.1.0               dbplyr_2.5.1               
#> [83] R6_2.6.1                    httr2_1.2.2                
#> [85] vroom_1.6.6                 evaluate_1.0.5             
#> [87] lattice_0.22-7              Biobase_2.71.0             
#> [89] readr_2.1.5                 SpatialExperiment_1.21.0   
#> [91] memoise_2.0.1               rjsoncons_1.3.2            
#> [93] Rcpp_1.1.0.8.2              SparseArray_1.11.10        
#> [95] xfun_0.55                   MatrixGenerics_1.23.0      
#> [97] pkgconfig_2.0.3

Name		Name	Last commit message	Last commit date
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R		R
inst		inst
man		man
tests		tests
vignettes		vignettes
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
DESCRIPTION		DESCRIPTION
NAMESPACE		NAMESPACE
NEWS.md		NEWS.md
README.md		README.md
_pkgdown.yml		_pkgdown.yml

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imageFeatureTCGA

Overview

Installation

Available Data

Formats

HoVerNet data

ProvGigaPath data

Importing HoVerNet data

Importing ProvGigaPath embeddings

See also

Shiny App: imageTCGA

Session Info

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imageFeatureTCGA

Overview

Installation

Available Data

Formats

HoVerNet data

ProvGigaPath data

Importing HoVerNet data

Importing ProvGigaPath embeddings

See also

Shiny App: imageTCGA

Session Info

About

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