# imageFeatureTCGA
``` r
library(imageFeatureTCGA)
library(SummarizedExperiment)
library(dplyr)
```
# Overview
`imageFeatureTCGA` 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
``` r
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("waldronlab/imageFeatureTCGA")
```
# Data structure and technical details
The datasets accessible through `imageFeatureTCGA` originate from
whole-slide histopathology images processed by deep learning pipelines.
They are distributed as precomputed features to avoid the computational
cost of running segmentation and embedding models locally.
## HoVerNet outputs
HoVerNet provides nuclei segmentation and classification results at the
single-cell level. Each detected nucleus is represented by:
- spatial coordinates (`x`, `y`) in pixel units relative to the slide
- predicted cell type labels
- class probabilities
- polygon contours describing nuclear boundaries (when available)
When imported as a `SpatialExperiment` or `SpatialFeatureExperiment`,
the data are structured as follows:
- **columns** represent individual nuclei
- **colData** stores cell-level metadata (coordinates, cell types)
- **assays** may contain quantitative features (e.g., probabilities)
- **metadata** may include segmentation contours and image information
These objects enable spatial analyses and integration with other
Bioconductor workflows for spatial transcriptomics and imaging data.
## ProvGigaPath embeddings
ProvGigaPath is a foundation model trained on large-scale pathology image
tiles that produces high-dimensional embeddings summarizing visual and
morphological features.
Two levels of embeddings are provided:
### Slide-level embeddings
Slide-level embeddings summarize the entire whole-slide image into a
single feature vector.
- one row per slide
- embedding dimension corresponds to the encoder output size
- suitable for slide-level prediction or clustering tasks
### Tile-level embeddings
Tile-level embeddings provide localized representations of tissue regions.
Each tile entry includes:
- spatial coordinates (`tile_x`, `tile_y`) corresponding to the tile
position on the slide
- a high-dimensional embedding vector
- optional metadata describing tile extraction parameters
These embeddings enable spatial analyses of tissue heterogeneity and can
be integrated with cell-level data from HoVerNet using complementary
packages such as `imageTCGAutils`.
# Available Data
Use the following function to download the catalog of available files:
``` r
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
``` r
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
``` r
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.
``` r
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
``` r
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.
``` r
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.
``` r
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.
``` r
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.
``` r
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):
```
# See also
You can explore the full documentation through the MOFA and Point
Pattern Analysis vignettes in the `imageTCGA` manuscript
[repository](https://github.com/billila/manuscript_imageTCGA/).
Note. More vignettes will be added as new feature types and workflows
become available.
# Shiny App: *imageTCGA*
The [imageTCGA](https://github.com/billila/imageTCGA) Shiny application
provides an interactive interface for exploring TCGA Diagnostic Image
Database metadata.
Click here to explore the shiny app:
[imageTCGA](https://shiny.sph.cuny.edu/app/imageTCGA/)
# Session Info
<details>
<summary>
Click here for Session Info
</summary>
``` r
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 ]
```
</details>
