cellNexus is a query interface that allow the programmatic exploration
and retrieval of the harmonised, curated and reannotated CELLxGENE
single-cell human cell atlas. Data can be retrieved at cell, sample, or
dataset levels based on filtering criteria.
Harmonised data is stored in the ARDC Nectar Research Cloud, and most
cellNexus functions interact with Nectar via web requests, so a
network connection is required for most functionality.
devtools::install_github("MangiolaLaboratory/cellNexus")library(cellNexus)Load additional packages
suppressPackageStartupMessages({
library(ggplot2)
})metadata <- get_metadata()
metadata#> # Source: SQL [?? x 94]
#> # Database: DuckDB v1.2.2 [shen.m@Darwin 23.3.0:R 4.5.0/:memory:]
#> cell_id dataset_id observation_joinid sample_id cell_type cell_type_ontology_t…¹
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 10X383_6:C… 0ee5ae70-… *|Vt;4ATOI e732d3bf… leukocyte CL:0000738
#> 2 10X383_6:G… 0ee5ae70-… #z5ft!kwbn e732d3bf… leukocyte CL:0000738
#> 3 10X383_5:C… 0ee5ae70-… kd(R>I7G#- e732d3bf… leukocyte CL:0000738
#> 4 10X383_5:G… 0ee5ae70-… mSylksfIn3 e732d3bf… leukocyte CL:0000738
#> 5 10X218_3:C… 0ee5ae70-… XEl?wkHAcg 979206e1… leukocyte CL:0000738
#> 6 10X383_5:C… 0ee5ae70-… T_4$MfQ!Ss e732d3bf… leukocyte CL:0000738
#> 7 10X218_3:T… 0ee5ae70-… &yS%7ee<`? 979206e1… leukocyte CL:0000738
#> 8 10X218_3:C… 0ee5ae70-… 0*ArkI0ju; 979206e1… leukocyte CL:0000738
#> 9 10X218_3:A… 0ee5ae70-… r<pyq>qS=W 979206e1… leukocyte CL:0000738
#> 10 10X383_6:C… 0ee5ae70-… )tfjoT&}j{ e732d3bf… leukocyte CL:0000738
#> # ℹ more rows
#> # ℹ abbreviated name: ¹cell_type_ontology_term_id
#> # ℹ 88 more variables: sample_ <chr>, assay <chr>, assay_ontology_term_id <chr>,
#> # cell_count <chr>, 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>, …
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 v1.2.2 [shen.m@Darwin 23.3.0:R 4.5.0/:memory:]
#> tissue cell_type_unified_ensemble
#> <chr> <chr>
#> 1 lower lobe of left lung cd8 tem
#> 2 lower lobe of left lung t cd4
#> 3 trachea muscle
#> 4 trachea macrophage
#> 5 trachea pdc
#> 6 trachea cd16 mono
#> 7 trachea tgd
#> 8 lymph node cd8 tcm
#> 9 lymph node treg
#> 10 lung macrophage
#> # 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%")
) |>
get_single_cell_experiment()
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Reading files.
#> ℹ Compiling Experiment.
single_cell_counts
#> class: SingleCellExperiment
#> dim: 56239 1780
#> metadata(0):
#> assays(1): counts
#> rownames(56239): ENSG00000121410 ENSG00000268895 ... ENSG00000135605
#> ENSG00000109501
#> rowData names(0):
#> colnames(1780):
#> LAP87_ATCGTAGAGACTAGAT-1_duong___9f222629-9e39-47d0-b83f-e08d610c7479_1
#> LAP87_ATCTTCATCCTGTTAT-1_duong___9f222629-9e39-47d0-b83f-e08d610c7479_1 ...
#> LAP87_GCCGTGACAGGAGGAG-1_duong___9f222629-9e39-47d0-b83f-e08d610c7479_76
#> LAP87_TGCATGACAACCGACC-1_duong___9f222629-9e39-47d0-b83f-e08d610c7479_76
#> colData names(95): dataset_id observation_joinid ... dir_prefix
#> original_cell_
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):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(assays = "cpm")
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Reading files.
#> ℹ Compiling Experiment.
single_cell_counts
#> class: SingleCellExperiment
#> dim: 56239 1780
#> metadata(0):
#> assays(1): cpm
#> rownames(56239): ENSG00000121410 ENSG00000268895 ... ENSG00000135605
#> ENSG00000109501
#> rowData names(0):
#> colnames(1780):
#> LAP87_ATCGTAGAGACTAGAT-1_duong___9f222629-9e39-47d0-b83f-e08d610c7479_1
#> LAP87_ATCTTCATCCTGTTAT-1_duong___9f222629-9e39-47d0-b83f-e08d610c7479_1 ...
#> LAP87_GCCGTGACAGGAGGAG-1_duong___9f222629-9e39-47d0-b83f-e08d610c7479_76
#> LAP87_TGCATGACAACCGACC-1_duong___9f222629-9e39-47d0-b83f-e08d610c7479_76
#> colData names(95): 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%")
) |>
get_pseudobulk()
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Reading files.
#> ℹ Compiling Experiment.
pseudobulk_counts
#> class: SingleCellExperiment
#> dim: 56239 98
#> metadata(0):
#> assays(1): counts
#> rownames(56239): ENSG00000000003 ENSG00000000005 ... ENSG00000290292
#> ENSG00000291237
#> rowData names(0):
#> colnames(98): bfe624d44f7e5868cc22e11ad0f13866___t cd4
#> bfe624d44f7e5868cc22e11ad0f13866___cd4 tcm ...
#> 4f067f7e5f960bc72b0710684a521e84____SC86___cd4 th1/th17 em
#> e4d7f8162faf68a85f61bdbd81dae627___other
#> colData names(56): 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%")
) |>
get_metacell(cell_aggregation = "metacell_2")
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Reading files.
#> ℹ Compiling Experiment.
metacell_counts
#> class: SingleCellExperiment
#> dim: 56239 914
#> metadata(0):
#> assays(1): counts
#> rownames(56239): ENSG00000121410 ENSG00000268895 ... ENSG00000135605
#> ENSG00000109501
#> rowData names(0):
#> colnames(914): 9c8fa5a8d2ae37179b579a0217670512___LAP87_1_duong___3
#> 9c8fa5a8d2ae37179b579a0217670512___LAP87_1_duong___1 ...
#> 9c8fa5a8d2ae37179b579a0217670512___LAP87_1_duong___2
#> 9c8fa5a8d2ae37179b579a0217670512___LAP87_1_duong___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_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(assays = "cpm", features = "ENSG00000134644")
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Reading files.
#> ℹ Compiling Experiment.
single_cell_counts
#> class: SingleCellExperiment
#> dim: 1 1780
#> metadata(0):
#> assays(1): cpm
#> rownames(1): ENSG00000134644
#> rowData names(0):
#> colnames(1780):
#> LAP87_ATCGTAGAGACTAGAT-1_duong___9f222629-9e39-47d0-b83f-e08d610c7479_1
#> LAP87_ATCTTCATCCTGTTAT-1_duong___9f222629-9e39-47d0-b83f-e08d610c7479_1 ...
#> LAP87_GCCGTGACAGGAGGAG-1_duong___9f222629-9e39-47d0-b83f-e08d610c7479_76
#> LAP87_TGCATGACAACCGACC-1_duong___9f222629-9e39-47d0-b83f-e08d610c7479_76
#> colData names(95): 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%")
) |>
get_seurat()
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Reading files.
#> ℹ Compiling Experiment.
seurat_counts
#> An object of class Seurat
#> 56239 features across 1780 samples within 1 assay
#> Active assay: originalexp (56239 features, 0 variable features)
#> 2 layers present: counts, dataBy 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 |> zellkonverter::writeH5AD("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 (e.g sample_sce_obj.h5ad) and atlas_id
(e.g cellxgene/dd-mm-yy) columns in the metadata.
# Define local cache and metadata path
local_cache <- tempdir()
meta_path <- paste0(local_cache,"/my_metadata.parquet")
# Extract the date from example sce metadata
date <- cellNexus::sample_sce_obj |> S4Vectors::metadata() |>
purrr::pluck("data") |> dplyr::pull(atlas_id) |> unique()
# Create a directory for storing counts
counts_path = file.path(local_cache, date, "counts")
dir.create(counts_path, recursive = TRUE, showWarnings = FALSE)
# Define the SCE file path for saving
sce_path = file.path(counts_path, "sample_sce_obj.h5ad")
# Save metadata to disk
cellNexus::sample_sce_obj |> S4Vectors::metadata() |> purrr::pluck("data") |>
arrow::write_parquet(meta_path)
# Save SCE to disk
cellNexus::sample_sce_obj |> zellkonverter::writeH5AD(file = sce_path,
compression = "gzip")
#> ℹ Using the 'counts' assay as the X matrix# A file from cloud
file_id_from_cloud <- "68aea852584d77f78e5c91204558316d___1.h5ad"
get_metadata(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("sample_sce_obj.h5ad", file_id_from_cloud)) |>
get_single_cell_experiment(cache_directory = local_cache)
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> 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: 155 4
#> metadata(1): data
#> assays(1): counts
#> rownames(155): ENSG00000187634 ENSG00000188976 ... ENSG00000187144
#> ENSG00000157191
#> rowData names(0):
#> colnames(4):
#> GCTGCGAGTGGGTCAA-1-1-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-1-0-0-0-0___218acb0f-9f2f-4f76-b90b-15a4b7c7f629_1
#> ACATCAGGTCTGCCAG-1-1-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-1-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0-0___218acb0f-9f2f-4f76-b90b-15a4b7c7f629_1
#> Liver_cDNA_CCTATTAGTTTGTGAC-1_2 Liver_cDNA_CCTATTAGTTTGTGTG-1_2
#> colData names(95): dataset_id observation_joinid ... 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 4.5.0 (2025-04-11)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS Sonoma 14.3
#>
#> Matrix products: default
#> BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> time zone: Australia/Melbourne
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] ggplot2_3.5.2 cellNexus_1.6.13
#>
#> loaded via a namespace (and not attached):
#> [1] RcppAnnoy_0.0.22 splines_4.5.0
#> [3] later_1.4.2 filelock_1.0.3
#> [5] tibble_3.3.0 polyclip_1.10-7
#> [7] basilisk.utils_1.20.0 preprocessCore_1.70.0
#> [9] fastDummies_1.7.5 lifecycle_1.0.4
#> [11] rprojroot_2.0.4 globals_0.18.0
#> [13] lattice_0.22-6 MASS_7.3-65
#> [15] backports_1.5.0 magrittr_2.0.3
#> [17] plotly_4.10.4 sass_0.4.10
#> [19] rmarkdown_2.29 jquerylib_0.1.4
#> [21] yaml_2.3.10 httpuv_1.6.16
#> [23] Seurat_5.3.0 sctransform_0.4.2
#> [25] spam_2.11-1 sp_2.2-0
#> [27] spatstat.sparse_3.1-0 reticulate_1.42.0
#> [29] cowplot_1.1.3 pbapply_1.7-2
#> [31] DBI_1.2.3 RColorBrewer_1.1-3
#> [33] abind_1.4-8 Rtsne_0.17
#> [35] GenomicRanges_1.60.0 purrr_1.0.4
#> [37] BiocGenerics_0.54.0 GenomeInfoDbData_1.2.14
#> [39] IRanges_2.42.0 S4Vectors_0.46.0
#> [41] ggrepel_0.9.6 irlba_2.3.5.1
#> [43] listenv_0.9.1 spatstat.utils_3.1-4
#> [45] goftest_1.2-3 RSpectra_0.16-2
#> [47] spatstat.random_3.4-1 fitdistrplus_1.2-2
#> [49] parallelly_1.45.0 codetools_0.2-20
#> [51] DelayedArray_0.34.1 tidyselect_1.2.1
#> [53] UCSC.utils_1.4.0 farver_2.1.2
#> [55] shinyWidgets_0.9.0 matrixStats_1.5.0
#> [57] stats4_4.5.0 spatstat.explore_3.4-3
#> [59] duckdb_1.2.2 jsonlite_2.0.0
#> [61] progressr_0.15.1 ggridges_0.5.6
#> [63] survival_3.8-3 tools_4.5.0
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