cellNexus builds upon and extends the functionality of the previously
released CuratedAtlasQueryR, providing a unified query and access
interface to the harmonised, curated, and reannotated CELLxGENE human
cell atlas. It enables reproducible and programmatic exploration of
large-scale single-cell data resources, supporting retrieval at the
cell, sample, and dataset levels through flexible filtering by tissue,
cell type, experimental condition, or other metadata features. The
retrieved data are returned for downstream analysis.
cellNexus integrates over 40 million human cells processed with
standardised quality control, consistent normalisation, and unified
abundance representations—including single-cell, counts-per-million,
pseudobulk, and metacell layers. This harmonised design facilitates
efficient cross-dataset analyses and downstream integration.
Data are hosted on the ARDC Nectar Research Cloud, and most cellNexus
functions interact with Nectar via web requests, so a network connection
is required for most functionality.
cellNexus and CuratedAtlasQueryR both rely on pre-computed expression
layers, but they differ in how these layers were generated. cellNexus
applies a more standardised workflow with explicit empty droplet and
dead cell removal followed by harmonised QC, normalisation, and
multi-layer data generation. In doing so, it produces newly iterated
data that align with the evolving CELLxGENE releases.
devtools::install_github("MangiolaLaboratory/cellNexus")library(cellNexus)suppressPackageStartupMessages({
library(ggplot2)
})metadata <- get_metadata(cloud_metadata = METADATA_URL)
metadata#> ℹ Downloading 1 file, totalling 0 GB
#> ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellNexus-metadata/sample_metadata.1.3.0.parquet to /tmp/RtmpVDjbPV/sample_metadata.1.3.0.parquet
#> # Source: SQL [?? x 97]
#> # Database: DuckDB 1.4.3 [unknown@Linux 5.14.0-362.24.1.el9_3.x86_64:R 4.5.2/:memory:]
#> cell_id dataset_id observation_joinid sample_id cell_type cell_type_ontology_t…¹ sample_ assay
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 FCAImmP7528294-ACATA… cda2c8cd-… p5e=WoIq0d 034f0fb1… monocyte CL:0000576 034f0f… 10x …
#> 2 FCAImmP7528294-CATGA… cda2c8cd-… lx`7Bo-&7n 034f0fb1… monocyte CL:0000576 034f0f… 10x …
#> 3 FCAImmP7528294-AAGAC… cda2c8cd-… *NUPW@J{c2 034f0fb1… monocyte CL:0000576 034f0f… 10x …
#> 4 FCAImmP7528294-AATCG… cda2c8cd-… KIV>qGFIS? 034f0fb1… monocyte CL:0000576 034f0f… 10x …
#> 5 FCAImmP7528294-TTTGG… cda2c8cd-… *_#lQ<oUnT 034f0fb1… monocyte CL:0000576 034f0f… 10x …
#> 6 FCAImmP7528294-TCAGG… cda2c8cd-… zHCZWNmUHu 034f0fb1… monocyte CL:0000576 034f0f… 10x …
#> 7 FCAImmP7528294-GACGC… cda2c8cd-… -NL-OH3!IA 034f0fb1… monocyte CL:0000576 034f0f… 10x …
#> 8 FCAImmP7528294-CTTGG… cda2c8cd-… 6mRCZW}rOM 034f0fb1… monocyte CL:0000576 034f0f… 10x …
#> 9 FCAImmP7528294-CACTC… cda2c8cd-… I6>u{Gb-J_ 034f0fb1… monocyte CL:0000576 034f0f… 10x …
#> 10 FCAImmP7579218-TGTGG… cda2c8cd-… IdHwp1GBZm 03ddfd57… monocyte CL:0000576 03ddfd… 10x …
#> # ℹ more rows
#> # ℹ abbreviated name: ¹cell_type_ontology_term_id
#> # ℹ 89 more variables: assay_ontology_term_id <chr>, cell_count <int>, citation <chr>, collection_id <chr>,
#> # dataset_version_id <chr>, default_embedding <chr>, development_stage <chr>,
#> # development_stage_ontology_term_id <chr>, disease <chr>, disease_ontology_term_id <chr>, donor_id <chr>,
#> # experiment___ <chr>, explorer_url <chr>, feature_count <int>, filesize <dbl>, filetype <chr>,
#> # is_primary_data <chr>, mean_genes_per_cell <dbl>, organism <chr>, organism_ontology_term_id <chr>, …
Metadata is saved to get_default_cache_dir() unless a custom path is
provided via the cache_directory argument. The metadata variable can
then be re-used for all subsequent queries.
metadata |>
dplyr::distinct(tissue, cell_type_unified_ensemble)
#> # Source: SQL [?? x 2]
#> # Database: DuckDB 1.4.3 [unknown@Linux 5.14.0-362.24.1.el9_3.x86_64:R 4.5.2/:memory:]
#> tissue cell_type_unified_ensemble
#> <chr> <chr>
#> 1 thymus cd14 mono
#> 2 breast nk
#> 3 renal pelvis epithelial
#> 4 kidney epithelial
#> 5 kidney blood vessel epithelial
#> 6 lung parenchyma cd4 th2 em
#> 7 respiratory airway cd4 th2 em
#> 8 lung cd4 th1 em
#> 9 lung cd4 fh em
#> 10 nose cd4 fh em
#> # ℹ more rowscellNexus metadata applies standardised quality control to filter out empty droplets, dead or damaged cells, doublets, and samples with low gene counts.
metadata = metadata |>
dplyr::filter(empty_droplet == FALSE,
alive == TRUE,
scDblFinder.class != "doublet",
feature_count >= 5000)single_cell_counts =
metadata |>
dplyr::filter(
self_reported_ethnicity == "African" &
assay |> stringr::str_like("%10x%") &
tissue == "lung parenchyma" &
cell_type |> stringr::str_like("%CD4%")
) |>
head() |>
get_single_cell_experiment()
single_cell_counts#> class: SingleCellExperiment
#> dim: 56239 6
#> metadata(0):
#> assays(1): counts
#> rownames(56239): ENSG00000121410 ENSG00000268895 ... ENSG00000135605 ENSG00000109501
#> rowData names(0):
#> colnames(6): LAP92_CATTCTAGTGCGGATA-1_duong___9f222629-9e39-47d0-b83f-e08d610c7479_1
#> LAP92_CTCATGCCACCTGATA-1_duong___9f222629-9e39-47d0-b83f-e08d610c7479_1 ...
#> GCTCCTAAGGGTATCG_F02607___9f222629-9e39-47d0-b83f-e08d610c7479_1
#> AACACGTCACGCATCG_F01853___9f222629-9e39-47d0-b83f-e08d610c7479_2
#> colData names(98): dataset_id observation_joinid ... dir_prefix original_cell_
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
single_cell_cpm =
metadata |>
dplyr::filter(
self_reported_ethnicity == "African" &
assay |> stringr::str_like("%10x%") &
tissue == "lung parenchyma" &
cell_type |> stringr::str_like("%CD4%")
) |>
head() |>
get_single_cell_experiment(assays = "cpm")
single_cell_cpm#> class: SingleCellExperiment
#> dim: 56239 6
#> metadata(0):
#> assays(1): cpm
#> rownames(56239): ENSG00000121410 ENSG00000268895 ... ENSG00000135605 ENSG00000109501
#> rowData names(0):
#> colnames(6): LAP92_CATTCTAGTGCGGATA-1_duong___9f222629-9e39-47d0-b83f-e08d610c7479_1
#> LAP92_CTCATGCCACCTGATA-1_duong___9f222629-9e39-47d0-b83f-e08d610c7479_1 ...
#> GCTCCTAAGGGTATCG_F02607___9f222629-9e39-47d0-b83f-e08d610c7479_1
#> AACACGTCACGCATCG_F01853___9f222629-9e39-47d0-b83f-e08d610c7479_2
#> colData names(98): dataset_id observation_joinid ... dir_prefix original_cell_
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
pseudobulk_counts =
metadata |>
dplyr::filter(
self_reported_ethnicity == "African" &
assay |> stringr::str_like("%10x%") &
tissue == "lung parenchyma" &
cell_type |> stringr::str_like("%CD4%")
) |>
head() |>
get_pseudobulk()
pseudobulk_counts#> class: SingleCellExperiment
#> dim: 56239 3
#> metadata(0):
#> assays(1): counts
#> rownames(56239): ENSG00000000003 ENSG00000000005 ... ENSG00000290292 ENSG00000291237
#> rowData names(0):
#> colnames(3): a2459ad4272363e6eb775e8e99607c3e___cd4 th1 em
#> 9c8fa5a8d2ae37179b579a0217670512___LAP92_1_duong___cd4 th2 em
#> e4d7f8162faf68a85f61bdbd81dae627___cd4 th2 em
#> colData names(59): dataset_id sample_id ... dir_prefix sample_identifier
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
The metadata includes a series of metacell aggregation levels, beginning with 2, 4, 8, and so on. For example, the value of metacell_2 represents a grouping of cells that can be split into two distinct metacells.
metacell_counts =
metadata |>
dplyr::filter(!is.na(metacell_2)) |>
dplyr::filter(
self_reported_ethnicity == "African" &
assay |> stringr::str_like("%10x%") &
tissue == "lung parenchyma" &
cell_type |> stringr::str_like("%CD4%")
) |>
head() |>
get_metacell(cell_aggregation = "metacell_2")
metacell_counts#> class: SingleCellExperiment
#> dim: 56239 4
#> metadata(0):
#> assays(1): counts
#> rownames(56239): ENSG00000121410 ENSG00000268895 ... ENSG00000135605 ENSG00000109501
#> rowData names(0):
#> colnames(4): 9c8fa5a8d2ae37179b579a0217670512___LAP92_1_duong___1
#> 9c8fa5a8d2ae37179b579a0217670512___LAP92_1_duong___2 e4d7f8162faf68a85f61bdbd81dae627___1
#> a2459ad4272363e6eb775e8e99607c3e___1
#> colData names(39): metacell_2 dataset_id ... dir_prefix metacell_identifier
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
This is helpful if just few genes are of interest (e.g ENSG00000134644 (PUM1)), as they can be compared across samples. cellNexus uses ENSEMBL gene ID(s).
single_cell_cpm =
metadata |>
dplyr::filter(
self_reported_ethnicity == "African" &
assay |> stringr::str_like("%10x%") &
tissue == "lung parenchyma" &
cell_type |> stringr::str_like("%CD4%")
) |>
head() |>
get_single_cell_experiment(assays = "cpm", features = "ENSG00000134644")
single_cell_counts#> class: SingleCellExperiment
#> dim: 1 6
#> metadata(0):
#> assays(1): cpm
#> rownames(1): ENSG00000134644
#> rowData names(0):
#> colnames(6): LAP92_CATTCTAGTGCGGATA-1_duong___9f222629-9e39-47d0-b83f-e08d610c7479_1
#> LAP92_CTCATGCCACCTGATA-1_duong___9f222629-9e39-47d0-b83f-e08d610c7479_1 ...
#> GCTCCTAAGGGTATCG_F02607___9f222629-9e39-47d0-b83f-e08d610c7479_1
#> AACACGTCACGCATCG_F01853___9f222629-9e39-47d0-b83f-e08d610c7479_2
#> colData names(98): dataset_id observation_joinid ... dir_prefix original_cell_
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
This convert the H5 SingleCellExperiment to Seurat so it might take long time and occupy a lot of memory depending on how many cells you are requesting.
seurat_counts =
metadata |>
dplyr::filter(
self_reported_ethnicity == "African" &
assay |> stringr::str_like("%10x%") &
tissue == "lung parenchyma" &
cell_type |> stringr::str_like("%CD4%")
) |>
head() |>
get_seurat()
seurat_counts#> An object of class Seurat
#> 56239 features across 6 samples within 1 assay
#> Active assay: originalexp (56239 features, 0 variable features)
#> 2 layers present: counts, data
By default, data is downloaded to get_default_cache_dir() output. If
memory is a concern, users can specify a custom cache directory to
metadata and counts functions:
metadata <- get_metadata(cache_directory = "/MY/CUSTOM/PATH")single_cell_counts =
metadata |>
dplyr::filter(
self_reported_ethnicity == "African" &
assay |> stringr::str_like("%10x%") &
tissue == "lung parenchyma" &
cell_type |> stringr::str_like("%CD4%")
) |>
get_single_cell_experiment(cache_directory = "/MY/CUSTOM/PATH")
single_cell_countsSame strategy can be applied for functions get_pseuodbulk(),
get_metacell(), get_seurat() by passing your custom directory
character to “cache_directory” parameter.
The returned SingleCellExperiment can be saved with three modalities,
as .rds or as HDF5 or as H5AD.
Saving as .rds has the advantage of being fast, and the .rds file
occupies very little disk space as it only stores the links to the files
in your cache.
However it has the disadvantage that for big SingleCellExperiment
objects, which merge a lot of HDF5 from your
get_single_cell_experiment, the display and manipulation is going to
be slow. In addition, an .rds saved in this way is not portable: you
will not be able to share it with other users.
single_cell_counts |> saveRDS("single_cell_counts.rds")Saving as .hdf5 executes any computation on the SingleCellExperiment
and writes it to disk as a monolithic HDF5. Once this is done,
operations on the SingleCellExperiment will be comparatively very
fast. The resulting .hdf5 file will also be totally portable and
sharable.
However this .hdf5 has the disadvantage of being larger than the
corresponding .rds as it includes a copy of the count information, and
the saving process is going to be slow for large objects.
# ! IMPORTANT if you save 200K+ cells
HDF5Array::setAutoBlockSize(size = 1e+09)
single_cell_counts |>
HDF5Array::saveHDF5SummarizedExperiment(
"single_cell_counts",
replace = TRUE,
as.sparse = TRUE,
verbose = TRUE
)Saving as .h5ad executes any computation on the SingleCellExperiment
and writes it to disk as a monolithic H5AD. The H5AD format is the
HDF5 disk representation of the AnnData object and is well-supported in
Python.
However this .h5ad saving strategy has a bottleneck of handling
columns with only NA values of a SingleCellExperiment metadata.
# ! IMPORTANT if you save 200K+ cells
HDF5Array::setAutoBlockSize(size = 1e+09)
single_cell_counts |> anndataR::write_h5ad("single_cell_counts.h5ad",
compression = "gzip",
verbose = TRUE)We can gather all CD14 monocytes cells and plot the distribution of ENSG00000085265 (FCN1) across all tissues
# Plots with styling
counts <- metadata |>
# Filter and subset
dplyr::filter(cell_type_unified_ensemble == "cd14 mono") |>
# Get counts per million for FCN1 gene
get_single_cell_experiment(assays = "cpm", features = "ENSG00000085265") |>
suppressMessages() |>
# Add feature to table
tidySingleCellExperiment::join_features("ENSG00000085265", shape = "wide") |>
# Rank x axis
tibble::as_tibble() |>
# Rename to gene symbol
dplyr::rename(FCN1 = ENSG00000085265)
# Plot by disease
counts |>
dplyr::with_groups(disease, ~ .x |> dplyr::mutate(median_count = median(`FCN1`, rm.na=TRUE))) |>
# Plot
ggplot(aes(forcats::fct_reorder(disease, median_count,.desc = TRUE), `FCN1`,color = dataset_id)) +
geom_jitter(shape=".") +
# Style
guides(color="none") +
scale_y_log10() +
theme_bw() +
theme(axis.text.x = element_text(angle = 60, vjust = 1, hjust = 1)) +
xlab("Disease") +
ggtitle("FCN1 in CD14 monocytes by disease. Coloured by datasets") 
# Plot by tissue
counts |>
dplyr::with_groups(tissue, ~ .x |> dplyr::mutate(median_count = median(`FCN1`, rm.na=TRUE))) |>
# Plot
ggplot(aes(forcats::fct_reorder(tissue, median_count,.desc = TRUE), `FCN1`,color = dataset_id)) +
geom_jitter(shape=".") +
# Style
guides(color="none") +
scale_y_log10() +
theme_bw() +
theme(axis.text.x = element_text(angle = 60, vjust = 1, hjust = 1)) +
xlab("Tissue") +
ggtitle("FCN1 in CD14 monocytes by tissue. Colored by datasets") +
theme(legend.position = "none", axis.text.x = element_text(size = 6.5))
cellNexus not only enables users to query our metadata but also allows
integration with your local metadata. Additionally, users can integrate
with your metadata stored in the cloud.
To enable this feature, users must include
file_id_cellNexus_single_cell and atlas_id (e.g cellxgene/dd-mm-yy)
columns in the metadata. See metadata structure in cellNexus::pbmc3k_sce
# Set up local cache and paths
local_cache <- tempdir()
layer <- "counts"
meta_path <- file.path(local_cache, "pbmc3k_metadata.parquet")
# Extract and prepare metadata
pbmc3k_metadata <- cellNexus::pbmc3k_sce |>
S4Vectors::metadata() |>
purrr::pluck("data") |>
dplyr::mutate(
counts_directory = file.path(tempdir(), atlas_id, layer),
sce_path = file.path(counts_directory, file_id_cellNexus_single_cell)
)
# Get unique paths
counts_directory <- pbmc3k_metadata |>
dplyr::pull(counts_directory) |>
unique()
sce_path <- pbmc3k_metadata |>
dplyr::pull(sce_path) |>
unique()
# Create directory structure
dir.create(counts_directory, recursive = TRUE, showWarnings = FALSE)
# Save data to disk
cellNexus::pbmc3k_sce |>
S4Vectors::metadata() |>
purrr::pluck("data") |>
arrow::write_parquet(meta_path)
# Save SCE object
cellNexus::pbmc3k_sce |>
anndataR::write_h5ad(sce_path, compression = "gzip")# A cellNexus file
file_id_from_cloud <- "e52795dec7b626b6276b867d55328d9f___1.h5ad"
file_id_local <- basename(sce_path)
get_metadata(cloud_metadata = METADATA_URL,
local_metadata = meta_path,
cache_directory = local_cache) |>
# For illustration purpose, only filter a selected cloud metadata and the saved metadata
dplyr::filter(file_id_cellNexus_single_cell %in% c(file_id_from_cloud, file_id_local)) |>
dplyr::select(cell_id, sample_id, dataset_id, cell_type_unified_ensemble, atlas_id, file_id_cellNexus_single_cell ) |>
get_single_cell_experiment(cache_directory = local_cache)
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Downloading 1 file, totalling 0.02 GB
#> ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellNexus-anndata/cellxgene/21-08-2025/counts/e52795dec7b626b6276b867d55328d9f___1.h5ad to /tmp/RtmpVDjbPV/cellxgene/21-08-2025//counts/e52795dec7b626b6276b867d55328d9f___1.h5ad
#> ℹ Reading files.
#> ! cellNexus says: Not all genes completely overlap across the provided objects.Counts are generated by genes intersection.
#> ℹ Compiling Experiment.
#> class: SingleCellExperiment
#> dim: 12795 3572
#> metadata(1): data
#> assays(1): counts
#> rownames(12795): ENSG00000228463 ENSG00000228327 ... ENSG00000273748 ENSG00000278384
#> rowData names(0):
#> colnames(3572): AAACATACAACCAC_1 AAACATTGAGCTAC_1 ...
#> TCACAAGAGTTGAGTA_5_liao___9f222629-9e39-47d0-b83f-e08d610c7479_2
#> AGGGTGACACGCATCG_5_liao___9f222629-9e39-47d0-b83f-e08d610c7479_2
#> colData names(7): sample_id dataset_id ... dir_prefix original_cell_
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):Dataset-specific columns (definitions available at cellxgene.cziscience.com)
cell_count, collection_id, filetype, is_primary_data,
mean_genes_per_cell, published_at, revised_at, schema_version,
tombstone, x_normalization, explorer_url, dataset_id,
dataset_version_id
Sample-specific columns (definitions available at cellxgene.cziscience.com)
sample_id, sample_, age_days, assay, assay_ontology_term_id,
development_stage, development_stage_ontology_term_id,
self_reported_ethnicity, self_reported_ethnicity_ontology_term_id,
experiment___, organism, organism_ontology_term_id,
sample_placeholder, sex, sex_ontology_term_id, tissue,
tissue_type, tissue_ontology_term_id, tissue_groups, disease,
disease_ontology_term_id, is_primary_data, donor_id, is_immune
Cell-specific columns (definitions available at cellxgene.cziscience.com)
cell_id, cell_type, cell_type_ontology_term_id,
cell_annotation_azimuth_l2, cell_annotation_blueprint_singler,
observation_joinid, empty_droplet, alive, scDblFinder.class
Through harmonisation and curation we introduced custom column, not present in the original CELLxGENE metadata
age_days: donors’ age in dayscell_type_unified_ensemble: the consensus call identity (for immune cells) using the original and three novel annotations using Seurat Azimuth and SingleRcell_annotation_azimuth_l2: Azimuth cell annotationcell_annotation_blueprint_singler: SingleR cell annotation using Blueprint referencecell_annotation_blueprint_monaco: SingleR cell annotation using Monaco referencesample_heuristic: sample subdivision for internal usefile_id_cellNexus_single_cell: file subdivision for internal usefile_id_cellNexus_pseudobulk: file subdivision for internal usesample_id: sample IDnCount_RNA: total number of RNA detected in a cell per samplenFeature_expressed_in_sample: total number of genes expressed in a cell per sample
The counts assay includes RNA abundance in the positive real scale
(not transformed with non-linear functions, e.g. log sqrt). Originally
CELLxGENE include a mix of scales and transformations specified in the
x_normalization column.
The cpm assay includes counts per million.
The rank assay is the representation of each cell’s gene expression
profile where genes are ranked by expression intensity.
The pseudobulk assay includes aggregated RNA abundance for sample and
cell type combination.
The metacell (e.g metacell_2, metacell_4 etc) assays represent
hierarchical partitions of cells into metacell groups.
sessionInfo()
#> R version 4.5.2 (2025-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Red Hat Enterprise Linux 9.3 (Plow)
#>
#> Matrix products: default
#> BLAS: /stornext/System/data/software/rhel/9/base/tools/R/4.5.2/lib64/R/lib/libRblas.so
#> LAPACK: /stornext/System/data/software/rhel/9/base/tools/R/4.5.2/lib64/R/lib/libRlapack.so; LAPACK version 3.12.1
#>
#> 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: Australia/Melbourne
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] ggplot2_4.0.1 cellNexus_0.99.5 stringr_1.6.0
#> [4] dplyr_1.1.4 SummarizedExperiment_1.40.0 Biobase_2.70.0
#> [7] GenomicRanges_1.62.1 Seqinfo_1.0.0 IRanges_2.44.0
#> [10] S4Vectors_0.48.0 BiocGenerics_0.56.0 generics_0.1.4
#> [13] MatrixGenerics_1.22.0 matrixStats_1.5.0
#>
#> loaded via a namespace (and not attached):
#> [1] RcppAnnoy_0.0.22 splines_4.5.2 later_1.4.4
#> [4] filelock_1.0.3 tibble_3.3.0 polyclip_1.10-7
#> [7] fastDummies_1.7.5 lifecycle_1.0.4 rprojroot_2.1.1
#> [10] globals_0.18.0 lattice_0.22-7 MASS_7.3-65
#> [13] backports_1.5.0 magrittr_2.0.4 plotly_4.11.0
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