Mangiola et al.
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,
normalised expression, and pseudobulk 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 builds on top of package CuratedAtlasQueryR. While both rely
on pre-computed expression layers, they differ in how these layers are
generated. cellNexus implements a more standardised workflow, including
explicit removal of empty droplets and dead cells, followed by
harmonised quality control, normalisation, and multi-layer data
generation. Through this process, it produces updated datasets that
remain aligned with the evolving CELLxGENE releases.
devtools::install_github("MangiolaLaboratory/cellNexus")library(cellNexus)suppressPackageStartupMessages({
library(ggplot2)
})By default, get_metadata() loads harmonised annotations. Users can
retrieve original Census annotations by the function
join_census_table().
metadata <- get_metadata() |>
join_census_table()
metadata#> # Source: SQL [?? x 73]
#> # Database: DuckDB 1.4.3 [unknown@Linux 5.14.0-362.24.1.el9_3.x86_64:R 4.5.3/:memory:]
#> cell_id observation_joinid dataset_id sample_id sample_ cell_count citation collection_id dataset_version_id
#> <dbl> <chr> <chr> <chr> <chr> <int> <chr> <chr> <chr>
#> 1 15 TjgA2vJ1;{ 842c6f5d-4a94-4eef-8510… 1119f482… 1119f4… 714331 Publica… 4195ab4c-20b… b8b5be07-061b-439…
#> 2 19 lNmuO5xs~3 842c6f5d-4a94-4eef-8510… 1119f482… 1119f4… 714331 Publica… 4195ab4c-20b… b8b5be07-061b-439…
#> 3 18 h22!#$}SJ* 842c6f5d-4a94-4eef-8510… 1119f482… 1119f4… 714331 Publica… 4195ab4c-20b… b8b5be07-061b-439…
#> 4 17 QRMCN*8*|# 842c6f5d-4a94-4eef-8510… 1119f482… 1119f4… 714331 Publica… 4195ab4c-20b… b8b5be07-061b-439…
#> 5 14 qxl7HJjL$L 842c6f5d-4a94-4eef-8510… 1119f482… 1119f4… 714331 Publica… 4195ab4c-20b… b8b5be07-061b-439…
#> 6 20 6Eu5c&aEH; 842c6f5d-4a94-4eef-8510… 1119f482… 1119f4… 714331 Publica… 4195ab4c-20b… b8b5be07-061b-439…
#> 7 16 j}0<Y>a#X~ 842c6f5d-4a94-4eef-8510… 1119f482… 1119f4… 714331 Publica… 4195ab4c-20b… b8b5be07-061b-439…
#> 8 5 N>_|{;6_6N 842c6f5d-4a94-4eef-8510… 1f755b9b… 1f755b… 714331 Publica… 4195ab4c-20b… b8b5be07-061b-439…
#> 9 4 CVfj>iWZ&P 842c6f5d-4a94-4eef-8510… 1f755b9b… 1f755b… 714331 Publica… 4195ab4c-20b… b8b5be07-061b-439…
#> 10 3 5jp7Uu{#@# 842c6f5d-4a94-4eef-8510… 1f755b9b… 1f755b… 714331 Publica… 4195ab4c-20b… b8b5be07-061b-439…
#> # ℹ more rows
#> # ℹ 64 more variables: default_embedding <chr>, experiment___ <chr>, explorer_url <chr>, feature_count <int>,
#> # filesize <dbl>, filetype <chr>, mean_genes_per_cell <dbl>, primary_cell_count <chr>, published_at <chr>,
#> # raw_data_location <chr>, revised_at <chr>, run_from_cell_id <chr>, sample_heuristic <chr>, schema_version <chr>,
#> # suspension_type <chr>, tissue_type <chr>, title <chr>, tombstone <lgl>, url <chr>, x_approximate_distribution <chr>,
#> # age_days <int>, tissue_groups <chr>, nFeature_expressed_in_sample <int>, nCount_RNA <dbl>, empty_droplet <lgl>,
#> # cell_type_unified_ensemble <chr>, is_immune <lgl>, subsets_Mito_percent <int>, subsets_Ribo_percent <int>, …
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 4.5.3/:memory:]
#> tissue cell_type_unified_ensemble
#> <chr> <chr>
#> 1 cortex of kidney cd16 mono
#> 2 cortex of kidney cd14 mono
#> 3 bone marrow nk
#> 4 blood cytotoxic
#> 5 blood cd4 tem
#> 6 blood granulocyte
#> 7 blood cd14 mono
#> 8 breast monocytic
#> 9 blood Unknown
#> 10 forebrain nk
#> # 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 |>
keep_quality_cells()
metadata <- metadata |>
dplyr::filter(feature_count >= 5000)single_cell_counts <-
metadata |>
dplyr::filter(
self_reported_ethnicity == "African American" &
assay == "10x 3' v3" &
tissue == "breast" &
cell_type == "T cell"
) |>
get_single_cell_experiment()
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Reading files.
#> ℹ Compiling Experiment.
single_cell_counts
#> class: SingleCellExperiment
#> dim: 33145 3
#> metadata(0):
#> assays(1): counts
#> rownames(33145): ENSG00000243485 ENSG00000237613 ... ENSG00000277475 ENSG00000268674
#> rowData names(0):
#> colnames(3): 1_1 2_1 3_1
#> colData names(73): observation_joinid dataset_id ... tissue_ontology_term_id original_cell_single_cell_cpm <-
metadata |>
dplyr::filter(
self_reported_ethnicity == "African American" &
assay == "10x 3' v3" &
tissue == "breast" &
cell_type == "T cell"
) |>
get_single_cell_experiment(assays = "cpm")
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Reading files.
#> ℹ Compiling Experiment.
single_cell_cpm
#> class: SingleCellExperiment
#> dim: 33145 3
#> metadata(0):
#> assays(1): cpm
#> rownames(33145): ENSG00000243485 ENSG00000237613 ... ENSG00000277475 ENSG00000268674
#> rowData names(0):
#> colnames(3): 1_1 2_1 3_1
#> colData names(73): observation_joinid dataset_id ... tissue_ontology_term_id original_cell_single_cell_sct <-
metadata |>
dplyr::filter(
self_reported_ethnicity == "African American" &
assay == "10x 3' v3" &
tissue == "breast" &
cell_type == "T cell"
) |>
get_single_cell_experiment(assays = "sct")
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Reading files.
#> ℹ Compiling Experiment.
single_cell_sct
#> class: SingleCellExperiment
#> dim: 33145 3
#> metadata(0):
#> assays(1): sct
#> rownames(33145): ENSG00000243485 ENSG00000237613 ... ENSG00000277475 ENSG00000268674
#> rowData names(0):
#> colnames(3): 1_1 2_1 3_1
#> colData names(73): observation_joinid dataset_id ... tissue_ontology_term_id original_cell_pseudobulk_counts <-
metadata |>
dplyr::filter(
assay == "10x 5' v1" &
tissue == "lung" &
cell_type == "classical monocyte"
) |>
get_pseudobulk()
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Reading files.
#> ℹ Compiling Experiment.
pseudobulk_counts
#> class: SingleCellExperiment
#> dim: 19979 1
#> metadata(0):
#> assays(1): counts
#> rownames(19979): ENSG00000243485 ENSG00000238009 ... ENSG00000275063 ENSG00000271254
#> rowData names(0):
#> colnames(1): 2e8c9911c9bfbffc07288adef93d3cf2___cd14 mono
#> colData names(56): dataset_id sample_id ... tissue_ontology_term_id sample_identifierCell communication metadata was generated based on post-QC cells per
sample using CellChat v2 method. It uses our harmonised cell type
annotation (cell_type_unified_ensemble) to infer the communication. It
captures inferred communication at both the ligand–receptor pair level
and the signalling pathway level.
interaction_count: The number of inferred interactions between each pair
of cell groups.
interaction_weight: The aggregated communication strength between each
pair of cell groups.
For definitions of additional annotations, please refer to the CellChat v2 documentation: https://github.com/jinworks/CellChat.
get_cell_communication_strength()#> # Source: SQL [?? x 16]
#> # Database: DuckDB 1.4.3 [unknown@Linux 5.14.0-362.24.1.el9_3.x86_64:R 4.5.3/:memory:]
#> source target ligand receptor lr_prob lr_pval interaction_name interaction_name_2 pathway_name annotation evidence
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
#> 1 b b TGFB1 TGFbR1_R2 0.000116 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TG… TGFb Secreted … KEGG: h…
#> 2 b memory b TGFB1 TGFbR1_R2 0.000865 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TG… TGFb Secreted … KEGG: h…
#> 3 b naive b TGFB1 TGFbR1_R2 0.000696 0.99 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TG… TGFb Secreted … KEGG: h…
#> 4 cd14 mono b TGFB1 TGFbR1_R2 0.00240 0.81 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TG… TGFb Secreted … KEGG: h…
#> 5 cd4 naive b TGFB1 TGFbR1_R2 0.000957 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TG… TGFb Secreted … KEGG: h…
#> 6 cd4 tem b TGFB1 TGFbR1_R2 0.00242 0.76 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TG… TGFb Secreted … KEGG: h…
#> # ℹ 5 more variables: pathway_prob <dbl>, pathway_pval <dbl>, sample_id <chr>, interaction_count <dbl>,
#> # interaction_weight <dbl>
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 American" &
assay == "10x 3' v3" &
tissue == "breast" &
cell_type == "T cell"
) |>
get_single_cell_experiment(assays = "cpm", features = "ENSG00000134644")
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Reading files.
#> ℹ Compiling Experiment.
single_cell_cpm
#> class: SingleCellExperiment
#> dim: 1 3
#> metadata(0):
#> assays(1): cpm
#> rownames(1): ENSG00000134644
#> rowData names(0):
#> colnames(3): 1_1 2_1 3_1
#> colData names(73): observation_joinid dataset_id ... tissue_ontology_term_id original_cell_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 American" &
assay == "10x 3' v3" &
tissue == "breast" &
cell_type == "T cell"
) |>
get_seurat()
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Reading files.
#> ℹ Compiling Experiment.
seurat_counts
#> An object of class Seurat
#> 33145 features across 3 samples within 1 assay
#> Active assay: originalexp (33145 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 American" &
assay == "10x 3' v3" &
tissue == "breast" &
cell_type == "T cell"
) |>
get_single_cell_experiment(cache_directory = "/MY/CUSTOM/PATH")
single_cell_countsSame strategy can be applied for functions get_pseuodbulk() and
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")
data(pbmc3k_sce)
# Extract and prepare metadata
pbmc3k_metadata <- 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
pbmc3k_sce |>
S4Vectors::metadata() |>
purrr::pluck("data") |>
arrow::write_parquet(meta_path)
# Save SCE object
pbmc3k_sce |>
anndataR::write_h5ad(sce_path, compression = "gzip", mode = "w")# 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 Reading files.
#> ℹ Compiling Experiment.
#> class: SingleCellExperiment
#> dim: 13132 500
#> metadata(1): data
#> assays(1): counts
#> rownames(13132): ENSG00000228463 ENSG00000228327 ... ENSG00000273748 ENSG00000278384
#> rowData names(0):
#> colnames(500): AAACATACAACCAC_1 AAACATTGAGCTAC_1 ... AGGAGTCTTGTCAG_1 AGGATAGACATTTC_1
#> colData names(6): sample_id dataset_id ... file_id_cellNexus_single_cell original_cell_The complete metadata dictionary (dataset-, sample-, and cell-level columns, plus cellNexus-specific harmonised fields) is available on the documentation site: cellNexus documentation.
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 sct assay includes normalised counts by sctranform.
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 detailed documentation for RNA abundance is available on the documentation site: cellNexus documentation.
sessionInfo()
#> R version 4.5.3 (2026-03-11)
#> 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/4.5.3/lib64/R/lib/libRblas.so
#> LAPACK: /stornext/System/data/software/rhel/9/base/tools/R/4.5.3/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 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C 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] cellNexus_0.99.20 arrow_22.0.0 shiny_1.12.1 anndataR_1.0.0
#> [5] testthat_3.3.1 tidyr_1.3.1 ttservice_0.5.3 SingleCellExperiment_1.32.0
#> [9] SummarizedExperiment_1.40.0 Biobase_2.70.0 GenomicRanges_1.62.1 Seqinfo_1.0.0
#> [13] ggplot2_4.0.1 BiocStyle_2.38.0 HDF5Array_1.38.0 h5mread_1.2.1
#> [17] rhdf5_2.54.1 DelayedArray_0.36.0 SparseArray_1.10.6 S4Arrays_1.10.1
#> [21] IRanges_2.44.0 abind_1.4-8 S4Vectors_0.48.0 MatrixGenerics_1.22.0
#> [25] matrixStats_1.5.0 BiocGenerics_0.56.0 generics_0.1.4 Matrix_1.7-4
#> [29] BiocParallel_1.44.0 crew.cluster_0.4.0 targets_1.11.4 dplyr_1.1.4
#>
#> loaded via a namespace (and not attached):
#> [1] igraph_2.2.1 graph_1.88.1 ica_1.0-3 plotly_4.11.0 Formula_1.2-5
#> [6] scater_1.38.0 devtools_2.4.6 BiocBaseUtils_1.12.0 tidyselect_1.2.1 bit_4.6.0
#> [11] doParallel_1.0.17 clue_0.3-66 lattice_0.22-9 rjson_0.2.21 blob_1.2.4
#> [16] stringr_1.6.0 rngtools_1.5.2 rclipboard_0.2.1 hunspell_3.0.6 parallel_4.5.3
#> [21] png_0.1-8 cli_3.6.5 registry_0.5-1 goftest_1.2-3 textshaping_1.0.4
#> [26] purrr_1.2.0 BiocNeighbors_2.4.0 ggnetwork_0.5.14 RUnit_0.4.33.1 stringdist_0.9.15
#> [31] uwot_0.2.4 curl_7.0.0 mime_0.13 evaluate_1.0.5 ComplexHeatmap_2.26.0
#> [36] stringi_1.8.7 backports_1.5.0 desc_1.4.3 XML_3.99-0.20 httpuv_1.6.16
#> [41] magrittr_2.0.4 rappdirs_0.3.3 splines_4.5.3 rcmdcheck_1.4.0 CellChat_2.2.0.9001
#> [46] nanonext_1.7.2 biocViews_1.78.0 sctransform_0.4.3 ggbeeswarm_0.7.3 xopen_1.0.1
#> [51] sessioninfo_1.2.3 DBI_1.2.3 jquerylib_0.1.4 withr_3.0.2 systemfonts_1.3.1
#> [56] rprojroot_2.1.1 lmtest_0.9-40 RBGL_1.86.0 brio_1.1.5 spelling_2.3.2
#> [61] BiocManager_1.30.27 duckdb_1.4.3 htmlwidgets_1.6.4 fs_1.6.6 ggrepel_0.9.6
#> [66] statnet.common_4.12.0 diffobj_0.3.6 reticulate_1.44.1 zoo_1.8-14 XVector_0.50.0
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