imageFeatureTCGA

library(imageFeatureTCGA)
library(SummarizedExperiment)
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 File.ID File.Name
#>    <chr>    <chr>   <chr>     <chr>    <chr>     <chr>        <chr>   <chr>    <chr>   <chr>    
#>  1 hovernet geojson TCGA-02-… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02       0de078… TCGA-02-…
#>  2 hovernet geojson TCGA-02-… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02       27021a… TCGA-02-…
#>  3 hovernet geojson TCGA-02-… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02       6486cb… TCGA-02-…
#>  4 hovernet geojson TCGA-02-… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02       cf6cd3… TCGA-02-…
#>  5 hovernet geojson TCGA-02-… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02       0c0fbb… TCGA-02-…
#>  6 hovernet geojson TCGA-02-… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02       0ea47d… TCGA-02-…
#>  7 hovernet geojson TCGA-02-… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02       33e598… TCGA-02-…
#>  8 hovernet geojson TCGA-02-… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02       e8c171… TCGA-02-…
#>  9 hovernet geojson TCGA-02-… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02       aa05ed… TCGA-02-…
#> 10 hovernet geojson TCGA-02-… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02       e9c5af… TCGA-02-…
#> # ℹ 54,243 more rows
#> # ℹ 15 more variables: 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 File.ID File.Name
#>    <chr>    <chr>   <chr>     <chr>    <chr>     <chr>        <chr>   <chr>    <chr>   <chr>    
#>  1 hovernet geojson TCGA-02-… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02       0de078… TCGA-02-…
#>  2 hovernet geojson TCGA-02-… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02       27021a… TCGA-02-…
#>  3 hovernet geojson TCGA-02-… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02       6486cb… TCGA-02-…
#>  4 hovernet geojson TCGA-02-… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02       cf6cd3… TCGA-02-…
#>  5 hovernet geojson TCGA-02-… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02       0c0fbb… TCGA-02-…
#>  6 hovernet geojson TCGA-02-… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02       0ea47d… TCGA-02-…
#>  7 hovernet geojson TCGA-02-… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02       33e598… TCGA-02-…
#>  8 hovernet geojson TCGA-02-… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02       e8c171… TCGA-02-…
#>  9 hovernet geojson TCGA-02-… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02       aa05ed… TCGA-02-…
#> 10 hovernet geojson TCGA-02-… hoverne… TCGA-02-… TCGA-02-000… TCGA-0… 02       e9c5af… TCGA-02-…
#> # ℹ 33,167 more rows
#> # ℹ 15 more variables: 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 File.ID File.Name
#>    <chr>      <chr>  <chr>    <chr>    <chr>     <chr>        <chr>   <chr>    <chr>   <chr>    
#>  1 provgigap… csv    TCGA-02… provgig… TCGA-02-… TCGA-02-000… TCGA-0… 02       0de078… TCGA-02-…
#>  2 provgigap… csv    TCGA-02… provgig… TCGA-02-… TCGA-02-000… TCGA-0… 02       27021a… TCGA-02-…
#>  3 provgigap… csv    TCGA-02… provgig… TCGA-02-… TCGA-02-000… TCGA-0… 02       6486cb… TCGA-02-…
#>  4 provgigap… csv    TCGA-02… provgig… TCGA-02-… TCGA-02-000… TCGA-0… 02       cf6cd3… TCGA-02-…
#>  5 provgigap… csv    TCGA-02… provgig… TCGA-02-… TCGA-02-000… TCGA-0… 02       0c0fbb… TCGA-02-…
#>  6 provgigap… csv    TCGA-02… provgig… TCGA-02-… TCGA-02-000… TCGA-0… 02       0ea47d… TCGA-02-…
#>  7 provgigap… csv    TCGA-02… provgig… TCGA-02-… TCGA-02-000… TCGA-0… 02       33e598… TCGA-02-…
#>  8 provgigap… csv    TCGA-02… provgig… TCGA-02-… TCGA-02-000… TCGA-0… 02       e8c171… TCGA-02-…
#>  9 provgigap… csv    TCGA-02… provgig… TCGA-02-… TCGA-02-000… TCGA-0… 02       aa05ed… TCGA-02-…
#> 10 provgigap… csv    TCGA-02… provgig… TCGA-02-… TCGA-02-000… TCGA-0… 02       e9c5af… TCGA-02-…
#> # ℹ 21,066 more rows
#> # ℹ 15 more variables: 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 either a SpatialExperiment or SpatialFeatureExperiment. Here we selectively import a file based on its filename, but you can also filter by other metadata fields such as Project.ID, pipeline, format, etc.

hspe <- 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()
hspe
#> 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

colData(hspe)
#> DataFrame with 67081 rows and 10 columns
#>           cell_id         x         y      type type_prob       label         R         G
#>       <character> <numeric> <numeric> <integer> <numeric> <character> <integer> <integer>
#> 1               8   3894.64   15842.5         1 0.9696429      neopla       255         0
#> 2               9   3920.24   15864.1         1 0.9077253      neopla       255         0
#> 3              10   3857.38   15871.0         1 0.9575972      neopla       255         0
#> 4              11   3892.01   15872.0         1 0.9658887      neopla       255         0
#> 5              12   3943.50   15872.6         1 0.0384615      neopla       255         0
#> ...           ...       ...       ...       ...       ...         ...       ...       ...
#> 67077       88901   86252.5   8284.12         3  0.768924      connec         0         0
#> 67078       88902   86219.8   8291.11         2  0.993865      inflam         0       255
#> 67079       88903   86052.2   8301.45         4  0.750000      necros       255       255
#>               B   sample_id
#>       <integer> <character>
#> 1             0    sample01
#> 2             0    sample01
#> 3             0    sample01
#> 4             0    sample01
#> 5             0    sample01
#> ...         ...         ...
#> 67077       255    sample01
#> 67078         0    sample01
#> 67079         0    sample01
#>  [ reached 'max' / getOption("max.print") -- omitted 2 rows ]

Importing ProvGigaPath embeddings

Slide-level embeddings

ProvGigaPath embeddings summarize tile or slide-level image features. In this example, we import slide-level embeddings for a single file. Each row corresponds to a slide, with an embedding vector describing the image-derived 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()
#> # A tibble: 1 × 771
#>   slideName tumorType fileName     V1    V2     V3     V4     V5    V6    V7    V8     V9    V10
#>   <chr>     <chr>     <chr>     <dbl> <dbl>  <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl>
#> 1 TCGA-VG-… <NA>      TCGA-VG… -0.355 0.584 -0.402 -0.527 0.0351 0.205 -1.14 -1.84 -0.203 -0.529
#> # ℹ 758 more variables: 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>,
#> #   V49 <dbl>, V50 <dbl>, V51 <dbl>, V52 <dbl>, V53 <dbl>, V54 <dbl>, V55 <dbl>, V56 <dbl>,
#> #   V57 <dbl>, V58 <dbl>, V59 <dbl>, V60 <dbl>, V61 <dbl>, V62 <dbl>, V63 <dbl>, V64 <dbl>, …

Tile-level embeddings

ProvGigaPath tile-level embeddings provide a more granular representation of image features at the tile level. Each row corresponds to a tile, with spatial coordinates (tile_x, tile_y) and an embedding vector describing the image-derived features for that tile. In this example, we filter the catalog to the tile-level file corresponding to the same slide as above.

getCatalog("provgigapath") |>
    dplyr::filter(
        filename == paste(
            "TCGA-VG-A8LO-01A-01-DX1",
            "B39A4D64-82A1-4A04-8AB6-918F3058B83B",
            "csv",
            "gz",
            sep = "."
        ) &
        level == "tile_level"
    ) |>
    getFileURLs() |>
    ProvGiga() |>
    import()
#> # A tibble: 211 × 1,543
#>    slide_name     tile_id tile_name tile_x tile_y      `0`    `1`     `2`     `3`    `4`     `5`
#>    <chr>            <dbl> <chr>      <dbl>  <dbl>    <dbl>  <dbl>   <dbl>   <dbl>  <dbl>   <dbl>
#>  1 TCGA-VG-A8LO-…       0 02155x_1…   2155  18612  0.177   -1.42   0.787   0.0380  0.753  0.0848
#>  2 TCGA-VG-A8LO-…       1 03180x_1…   3180  15540  0.433   -1.33   1.11   -0.382   0.958  0.681 
#>  3 TCGA-VG-A8LO-…       2 03180x_1…   3180  16564  0.886   -1.71  -0.0505  0.317   0.568 -0.200 
#>  4 TCGA-VG-A8LO-…       3 03180x_1…   3180  17588  0.183   -1.31  -0.599  -0.138   0.534  0.304 
#>  5 TCGA-VG-A8LO-…       4 03180x_1…   3180  18612  0.631   -0.588  0.652   0.548   0.529  0.911 
#>  6 TCGA-VG-A8LO-…       5 03180x_1…   3180  19636  0.700   -1.51   0.909   0.380   0.971  0.388 
#>  7 TCGA-VG-A8LO-…       6 04204x_1…   4204  14516  0.821   -2.47   1.05   -0.411  -0.216 -0.134 
#>  8 TCGA-VG-A8LO-…       7 04204x_1…   4204  15540  0.251   -0.958  0.259  -0.186   0.567  0.806 
#>  9 TCGA-VG-A8LO-…       8 04204x_1…   4204  16564 -0.0607  -0.917 -0.168   0.727   0.292  0.886 
#> 10 TCGA-VG-A8LO-…       9 04204x_1…   4204  17588 -0.00672 -1.05   0.460   0.600  -0.246  0.798 
#> # ℹ 201 more rows
#> # ℹ 1,532 more variables: `6` <dbl>, `7` <dbl>, `8` <dbl>, `9` <dbl>, `10` <dbl>, `11` <dbl>,
#> #   `12` <dbl>, `13` <dbl>, `14` <dbl>, `15` <dbl>, `16` <dbl>, `17` <dbl>, `18` <dbl>,
#> #   `19` <dbl>, `20` <dbl>, `21` <dbl>, `22` <dbl>, `23` <dbl>, `24` <dbl>, `25` <dbl>,
#> #   `26` <dbl>, `27` <dbl>, `28` <dbl>, `29` <dbl>, `30` <dbl>, `31` <dbl>, `32` <dbl>,
#> #   `33` <dbl>, `34` <dbl>, `35` <dbl>, `36` <dbl>, `37` <dbl>, `38` <dbl>, `39` <dbl>,
#> #   `40` <dbl>, `41` <dbl>, `42` <dbl>, `43` <dbl>, `44` <dbl>, `45` <dbl>, `46` <dbl>, …

Importing multiple ProvGigaPath files

One can also import multiple files at once. Here we filter the catalog to the first three slide-level files for the TCGA-GBM project, and import them as a ProvGigaList. Each element of the list corresponds to a slide, with the same structure as described above for slide-level embeddings. Note that the ProvGigaList constructor can also accept a vector of file paths or URLs. The import method for ProvGigaList will then import each file in the list and return either a single SummarizedExperiment or a list of SummarizedExperiment objects based on the diversity of the data levels in the input files. In this example, the catalog is filtered to a slide-level subset, so the output is a single SummarizedExperiment object with three columns corresponding to the three slides.

pgl <- getCatalog("provgigapath") |>
    dplyr::filter(level == "slide_level", Project.ID == "TCGA-GBM") |>
    dplyr::slice(1:3) |>
    getFileURLs() |>
    ProvGigaList() |>
    import()
pgl
#> class: SummarizedExperiment 
#> dim: 768 3 
#> metadata(5): slideName tumorType fileName patientIds sampleIds
#> assays(1): embeddings
#> rownames: NULL
#> rowData names(0):
#> colnames(3): TCGA-02-0001-01Z-00-DX1 TCGA-02-0001-01Z-00-DX2 TCGA-02-0001-01Z-00-DX3
#> colData names(0):

Mixed level imports

The ProvGigaList constructor can also accept a mix of slide- and tile-level files. In this case, the import method will return a list of SummarizedExperiment objects, one for each data level. Here we filter the catalog to include both slide- and tile-level files for the same slide, and import them together.

pgl_mixed <- getCatalog("provgigapath") |>
    dplyr::filter(
        filename %in% c(
            paste(
                "TCGA-VG-A8LO-01A-01-DX1",
                "B39A4D64-82A1-4A04-8AB6-918F3058B83B",
                "csv",
                "gz",
                sep = "."
            )
        ) &
        level %in% c("slide_level", "tile_level")
    ) |>
    getFileURLs() |>
    ProvGigaList() |>
    import()
#> Warning in .as(from): NAs introduced by coercion
pgl_mixed
#> $slide_level
#> class: SummarizedExperiment 
#> dim: 768 1 
#> metadata(5): slideName tumorType fileName patientIds sampleIds
#> assays(1): embeddings
#> rownames: NULL
#> rowData names(0):
#> colnames(1): TCGA-VG-A8LO-01A-01-DX1
#> colData names(0):
#> 
#> $tile_level
#> class: SummarizedExperiment 
#> dim: 1538 1 
#> metadata(3): metadata sampleIds patientIds
#> assays(1): tiles
#> rownames: NULL
#> rowData names(0):
#> colnames(1): TCGA-VG-A8LO-01A-01-DX1
#> colData names(0):

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 24.04.4 LTS
#> 
#> Matrix products: default
#> BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
#>  [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
#> [10] LC_TELEPHONE=C             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   base     
#> 
#> other attached packages:
#>  [1] SummarizedExperiment_1.41.0 Biobase_2.71.0              GenomicRanges_1.63.1       
#>  [4] Seqinfo_1.1.0               IRanges_2.45.0              S4Vectors_0.49.0           
#>  [7] BiocGenerics_0.57.0         generics_0.1.4              MatrixGenerics_1.23.0      
#> [10] matrixStats_1.5.0           imageFeatureTCGA_0.99.56    dplyr_1.1.4                
#> [13] ImageFeatureTCGA_0.99.38    colorout_1.3-2             
#> 
#> loaded via a namespace (and not attached):
#>   [1] bitops_1.0-9                DBI_1.2.3                   httr2_1.2.2                
#>   [4] rlang_1.1.6                 magrittr_2.0.4              otel_0.2.0                 
#>   [7] compiler_4.6.0              RSQLite_2.4.5               GenomicFeatures_1.63.1     
#>  [10] png_0.1-8                   vctrs_0.6.5                 stringr_1.6.0              
#>  [13] rvest_1.0.5                 pkgconfig_2.0.3             SpatialExperiment_1.21.0   
#>  [16] crayon_1.5.3                fastmap_1.2.0               dbplyr_2.5.1               
#>  [19] magick_2.9.0                XVector_0.51.0              utf8_1.2.6                 
#>  [22] Rsamtools_2.27.0            promises_1.5.0              rmarkdown_2.30             
#>  [25] tzdb_0.5.0                  UCSC.utils_1.7.1            ps_1.9.1                   
#>  [28] purrr_1.2.0                 bit_4.6.0                   MultiAssayExperiment_1.37.2
#>  [31] xfun_0.56                   cachem_1.1.0                cigarillo_1.1.0            
#>  [34] GenomeInfoDb_1.47.2         jsonlite_2.0.0              blob_1.2.4                 
#>  [37] later_1.4.4                 DelayedArray_0.37.0         BiocParallel_1.45.0        
#>  [40] parallel_4.6.0              R6_2.6.1                    stringi_1.8.7              
#>  [43] RColorBrewer_1.1-3          rtracklayer_1.71.3          Rcpp_1.1.1                 
#>  [46] knitr_1.51                  readr_2.1.6                 BiocBaseUtils_1.13.0       
#>  [49] Matrix_1.7-4                tidyselect_1.2.1            rstudioapi_0.18.0          
#>  [52] dichromat_2.0-0.1           abind_1.4-8                 yaml_2.3.12                
#>  [55] codetools_0.2-20            websocket_1.4.4             curl_7.0.0                 
#>  [58] processx_3.8.6              rjsoncons_1.3.2             lattice_0.22-7             
#>  [61] tibble_3.3.0                BumpyMatrix_1.19.0          KEGGREST_1.51.1            
#>  [64] withr_3.0.2                 S7_0.2.1                    evaluate_1.0.5             
#>  [67] archive_1.1.12.1            BiocFileCache_3.1.0         xml2_1.5.1                 
#>  [70] Biostrings_2.79.4           pillar_1.11.1               filelock_1.0.3             
#>  [73] rsconnect_1.7.0             TCGAutils_1.31.4            RCurl_1.98-1.17            
#>  [76] vroom_1.6.6                 chromote_0.5.1              hms_1.1.4                  
#>  [79] ggplot2_4.0.1               scales_1.4.0                glue_1.8.0                 
#>  [82] tools_4.6.0                 BiocIO_1.21.0               data.table_1.18.2.1        
#>  [85] GenomicAlignments_1.47.0    XML_3.99-0.20               cowplot_1.2.0              
#>  [88] grid_4.6.0                  AnnotationDbi_1.73.0        SingleCellExperiment_1.33.0
#>  [91] restfulr_0.0.16             TENxIO_1.13.3               cli_3.6.5                  
#>  [94] rappdirs_0.3.4              GenomicDataCommons_1.35.1   S4Arrays_1.11.1            
#>  [97] gtable_0.3.6                digest_0.6.39               SparseArray_1.11.10        
#> [100] rjson_0.2.23               
#>  [ reached 'max' / getOption("max.print") -- omitted 6 entries ]

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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

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ProvGigaPath data

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Importing ProvGigaPath embeddings

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Importing multiple ProvGigaPath files

Mixed level imports

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Shiny App: imageTCGA

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waldronlab/imageFeatureTCGA

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imageFeatureTCGA

Overview

Installation

Available Data

Formats

HoVerNet data

ProvGigaPath data

Importing HoVerNet data

Importing ProvGigaPath embeddings

Slide-level embeddings

Tile-level embeddings

Importing multiple ProvGigaPath files

Mixed level imports

See also

Shiny App: imageTCGA

Session Info

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