Mangiola et al.
cellNexus extends the functionality of CuratedAtlasQueryR by
providing a unified interface for querying and accessing the harmonised,
curated, and reannotated CELLxGENE human cell atlas. It enables
reproducible, programmatic exploration of large-scale single-cell
datasets, supporting data retrieval at the cell, sample, and dataset
levels with flexible filtering based on tissue, cell type, experimental
condition, and other metadata. Retrieved data are returned in formats
ready for downstream analysis.
The package integrates over 44 million human cells processed through a standardised pipeline, including consistent quality control, normalisation, and unified abundance representations (e.g., single-cell, counts-per-million, normalised expression, and pseudobulk). This harmonisation facilitates efficient cross-dataset comparison and integration.
Data are hosted on the ARDC Nectar Research Cloud, and most functions access them via web requests; therefore, an active network connection is required for typical use.
While both cellNexus and CuratedAtlasQueryR rely on precomputed expression layers, cellNexus adopts a more standardised and transparent processing workflow. This includes explicit removal of empty droplets and dead cells, followed by harmonised quality control, normalisation, and multi-layer data generation, ensuring alignment with 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()
#> ℹ Downloading 1 file, totalling 0.43 GB
#> ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellNexus-metadata/census_cell_metadata.2.3.0.parquet to /vast/scratch/users/shen.m/r_cache/R/cellNexus/census_cell_metadata.2.3.0.parquet
#> ℹ Downloading 1 file, totalling 0.9 GB
#> ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellNexus-metadata/cellnexus_metadata.2.3.0.parquet to /vast/scratch/users/shen.m/r_cache/R/cellNexus/cellnexus_metadata.2.3.0.parquet
metadata
#> # Source: SQL [?? x 76]
#> # Database: DuckDB 1.4.3 [unknown@Linux 5.14.0-570.112.1.el9_6.x86_64:R 4.5.3/:memory:]
#> cell_id observation_joinid dataset_id sample_id sample_ experiment___ run_from_cell_id sample_heuristic age_days tissue_groups
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 3670 -08E8se6Ii 30cd5311-6c09-46c9-94f1-71fe4b9… 420ce6ff… 420ce6… "" <NA> HGR0000124___bl… 28835 blood
#> 2 519 &7%VnKpYm6 30cd5311-6c09-46c9-94f1-71fe4b9… 420ce6ff… 420ce6… "" <NA> HGR0000124___bl… 28835 blood
#> 3 3674 dz@)@k#<V# 30cd5311-6c09-46c9-94f1-71fe4b9… 420ce6ff… 420ce6… "" <NA> HGR0000124___bl… 28835 blood
#> 4 3430 9v{tS;L+wQ 30cd5311-6c09-46c9-94f1-71fe4b9… 420ce6ff… 420ce6… "" <NA> HGR0000124___bl… 28835 blood
#> 5 3471 Z*;~@?yoi( 30cd5311-6c09-46c9-94f1-71fe4b9… 420ce6ff… 420ce6… "" <NA> HGR0000124___bl… 28835 blood
#> 6 3474 Qa9?c3UqN^ 30cd5311-6c09-46c9-94f1-71fe4b9… 420ce6ff… 420ce6… "" <NA> HGR0000124___bl… 28835 blood
#> 7 3477 7|N`hFx0Qr 30cd5311-6c09-46c9-94f1-71fe4b9… 420ce6ff… 420ce6… "" <NA> HGR0000124___bl… 28835 blood
#> 8 3483 `!a(s{Z6^s 30cd5311-6c09-46c9-94f1-71fe4b9… 420ce6ff… 420ce6… "" <NA> HGR0000124___bl… 28835 blood
#> 9 3748 (g+_FXEA&4 30cd5311-6c09-46c9-94f1-71fe4b9… 420ce6ff… 420ce6… "" <NA> HGR0000124___bl… 28835 blood
#> 10 3500 _oid2*7KJQ 30cd5311-6c09-46c9-94f1-71fe4b9… 420ce6ff… 420ce6… "" <NA> HGR0000124___bl… 28835 blood
#> # ℹ more rows
#> # ℹ 66 more variables: 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>, high_mitochondrion <lgl>, high_ribosome <lgl>, scDblFinder.class <chr>,
#> # sample_chunk <int>, cell_chunk <int>, sample_pseudobulk_chunk <int>, file_id_cellNexus_single_cell <chr>, file_id_cellNexus_pseudobulk <chr>,
#> # count_upper_bound <dbl>, nfeature_expressed_thresh <dbl>, inverse_transform <chr>, alive <lgl>, cell_annotation_blueprint_singler <chr>,
#> # cell_annotation_monaco_singler <chr>, cell_annotation_azimuth_l2 <chr>, ethnicity_flagging_score <dbl>, low_confidence_ethnicity <chr>,
#> # .aggregated_cells <int>, imputed_ethnicity <chr>, atlas_id <chr>, cell_type <chr>, cell_type_ontology_term_id <chr>, assay <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 5.14.0-570.112.1.el9_6.x86_64:R 4.5.3/:memory:]
#> tissue cell_type_unified_ensemble
#> <chr> <chr>
#> 1 cerebellum pericyte
#> 2 cerebellum endothelial
#> 3 cerebellum other
#> 4 cerebellum dc
#> 5 cerebellum immune
#> 6 spleen b
#> 7 spleen nk
#> 8 spleen b memory
#> 9 spleen cdc
#> 10 spleen t cd4
#> # 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 Downloading 10 files, totalling 0.03 GB
#> ℹ Downloading 10 files in parallel...
#> ℹ Reading files.
#>
Reading counts ■■■■ 10% | ETA: 15s
Reading counts ■■■■■■■ 20% | ETA: 12s
Reading counts ■■■■■■■■■■ 30% | ETA: 12s
Reading counts ■■■■■■■■■■■■■ 40% | ETA: 10s
Reading counts ■■■■■■■■■■■■■■■■ 50% | ETA: 8s
Reading counts ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 6s
Reading counts ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 5s
Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■ 80% | ETA: 3s
Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 90% | ETA: 2s
ℹ Compiling Experiment.
single_cell_counts
#> # A SingleCellExperiment-tibble abstraction: 2,924 × 77
#> # �[90mFeatures=33145 | Cells=2924 | Assays=counts�[0m
#> .cell observation_joinid dataset_id sample_id sample_ experiment___ run_from_cell_id sample_heuristic age_days tissue_groups nFeature_expressed_i…¹
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> <chr> <int>
#> 1 1_1 I8a42<8st4 842c6f5d-4… 184fa234… 184fa2… "" <NA> c2aa4d8d-e9df-4… 14600 breast 3395
#> 2 76_1 bTlx!HK=oS 842c6f5d-4… 52ab9222… 52ab92… "" <NA> 7ce86149-8906-4… 14600 breast 1671
#> 3 77_1 E4g5+)v;AV 842c6f5d-4… 52ab9222… 52ab92… "" <NA> 7ce86149-8906-4… 14600 breast 2340
#> 4 78_1 +q?29B%2nH 842c6f5d-4… 52ab9222… 52ab92… "" <NA> 7ce86149-8906-4… 14600 breast 1714
#> 5 79_1 zuJ#MBMWy; 842c6f5d-4… 52ab9222… 52ab92… "" <NA> 7ce86149-8906-4… 14600 breast 1506
#> 6 72_1 8wGs7JgUjj 842c6f5d-4… 6b194412… 6b1944… "" <NA> b3ff1aad-40fd-4… 14600 breast 2548
#> 7 75_1 F9G7A+GgjA 842c6f5d-4… db5a69ed… db5a69… "" <NA> 49beb83c-66a1-4… 14600 breast 1291
#> 8 73_1 z_=CTOs4{z 842c6f5d-4… 4b5e66fa… 4b5e66… "" <NA> 04983012-bb56-4… 14600 breast 2866
#> 9 74_1 fNzorxA`Mf 842c6f5d-4… 4b5e66fa… 4b5e66… "" <NA> 04983012-bb56-4… 14600 breast 1942
#> 10 80_1 zz-!e5_XAo 842c6f5d-4… 1de3f3ba… 1de3f3… "" <NA> 7fabaf1c-52fd-4… 14600 breast 1749
#> # ℹ 2,914 more rows
#> # ℹ abbreviated name: ¹nFeature_expressed_in_sample
#> # ℹ 66 more variables: nCount_RNA <dbl>, empty_droplet <lgl>, cell_type_unified_ensemble <chr>, is_immune <lgl>, subsets_Mito_percent <int>,
#> # subsets_Ribo_percent <int>, high_mitochondrion <lgl>, high_ribosome <lgl>, scDblFinder.class <chr>, sample_chunk <int>, cell_chunk <int>,
#> # sample_pseudobulk_chunk <int>, file_id_cellNexus_single_cell <chr>, file_id_cellNexus_pseudobulk <chr>, count_upper_bound <dbl>,
#> # nfeature_expressed_thresh <dbl>, inverse_transform <chr>, alive <lgl>, cell_annotation_blueprint_singler <chr>,
#> # cell_annotation_monaco_singler <chr>, cell_annotation_azimuth_l2 <chr>, ethnicity_flagging_score <dbl>, low_confidence_ethnicity <chr>, …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 Downloading 10 files, totalling 0.03 GB
#> ℹ Downloading 10 files in parallel...
#> ℹ Reading files.
#>
Reading cpm ■■■■ 10% | ETA: 12s
Reading cpm ■■■■■■■ 20% | ETA: 18s
Reading cpm ■■■■■■■■■■ 30% | ETA: 13s
Reading cpm ■■■■■■■■■■■■■ 40% | ETA: 10s
Reading cpm ■■■■■■■■■■■■■■■■ 50% | ETA: 8s
Reading cpm ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 6s
Reading cpm ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 5s
Reading cpm ■■■■■■■■■■■■■■■■■■■■■■■■■ 80% | ETA: 3s
Reading cpm ■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 90% | ETA: 1s
ℹ Compiling Experiment.
single_cell_cpm
#> # A SingleCellExperiment-tibble abstraction: 2,924 × 77
#> # �[90mFeatures=33145 | Cells=2924 | Assays=cpm�[0m
#> .cell observation_joinid dataset_id sample_id sample_ experiment___ run_from_cell_id sample_heuristic age_days tissue_groups nFeature_expressed_i…¹
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> <chr> <int>
#> 1 72_1 8wGs7JgUjj 842c6f5d-4… 6b194412… 6b1944… "" <NA> b3ff1aad-40fd-4… 14600 breast 2548
#> 2 75_1 F9G7A+GgjA 842c6f5d-4… db5a69ed… db5a69… "" <NA> 49beb83c-66a1-4… 14600 breast 1291
#> 3 73_1 z_=CTOs4{z 842c6f5d-4… 4b5e66fa… 4b5e66… "" <NA> 04983012-bb56-4… 14600 breast 2866
#> 4 74_1 fNzorxA`Mf 842c6f5d-4… 4b5e66fa… 4b5e66… "" <NA> 04983012-bb56-4… 14600 breast 1942
#> 5 80_1 zz-!e5_XAo 842c6f5d-4… 1de3f3ba… 1de3f3… "" <NA> 7fabaf1c-52fd-4… 14600 breast 1749
#> 6 81_1 -mb&DWckf( 842c6f5d-4… 1de3f3ba… 1de3f3… "" <NA> 7fabaf1c-52fd-4… 14600 breast 1993
#> 7 1_1 I8a42<8st4 842c6f5d-4… 184fa234… 184fa2… "" <NA> c2aa4d8d-e9df-4… 14600 breast 3395
#> 8 76_1 bTlx!HK=oS 842c6f5d-4… 52ab9222… 52ab92… "" <NA> 7ce86149-8906-4… 14600 breast 1671
#> 9 77_1 E4g5+)v;AV 842c6f5d-4… 52ab9222… 52ab92… "" <NA> 7ce86149-8906-4… 14600 breast 2340
#> 10 78_1 +q?29B%2nH 842c6f5d-4… 52ab9222… 52ab92… "" <NA> 7ce86149-8906-4… 14600 breast 1714
#> # ℹ 2,914 more rows
#> # ℹ abbreviated name: ¹nFeature_expressed_in_sample
#> # ℹ 66 more variables: nCount_RNA <dbl>, empty_droplet <lgl>, cell_type_unified_ensemble <chr>, is_immune <lgl>, subsets_Mito_percent <int>,
#> # subsets_Ribo_percent <int>, high_mitochondrion <lgl>, high_ribosome <lgl>, scDblFinder.class <chr>, sample_chunk <int>, cell_chunk <int>,
#> # sample_pseudobulk_chunk <int>, file_id_cellNexus_single_cell <chr>, file_id_cellNexus_pseudobulk <chr>, count_upper_bound <dbl>,
#> # nfeature_expressed_thresh <dbl>, inverse_transform <chr>, alive <lgl>, cell_annotation_blueprint_singler <chr>,
#> # cell_annotation_monaco_singler <chr>, cell_annotation_azimuth_l2 <chr>, ethnicity_flagging_score <dbl>, low_confidence_ethnicity <chr>, …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 Downloading 10 files, totalling 0.03 GB
#> ℹ Downloading 10 files in parallel...
#> ℹ Reading files.
#> ! The number of cells in the SingleCellExperiment will be less than the number of cells you have selected from the metadata. Are cell IDs duplicated? Or, do cell IDs correspond to the counts file?
#>
Reading sct ■■■■ 10% | ETA: 14s
Reading sct ■■■■■■■ 20% | ETA: 11s
Reading sct ■■■■■■■■■■ 30% | ETA: 11s
Reading sct ■■■■■■■■■■■■■ 40% | ETA: 9s
! The number of cells in the SingleCellExperiment will be less than the number of cells you have selected from the metadata. Are cell IDs duplicated? Or, do cell IDs correspond to the counts file?
#> Reading sct ■■■■■■■■■■■■■ 40% | ETA: 9s
Reading sct ■■■■■■■■■■■■■■■■ 50% | ETA: 7s
! The number of cells in the SingleCellExperiment will be less than the number of cells you have selected from the metadata. Are cell IDs duplicated? Or, do cell IDs correspond to the counts file?
#> Reading sct ■■■■■■■■■■■■■■■■ 50% | ETA: 7s
Reading sct ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 6s
Reading sct ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 4s
! The number of cells in the SingleCellExperiment will be less than the number of cells you have selected from the metadata. Are cell IDs duplicated? Or, do cell IDs correspond to the counts file?
#> Reading sct ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 4s
Reading sct ■■■■■■■■■■■■■■■■■■■■■■■■■ 80% | ETA: 3s
Reading sct ■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 90% | ETA: 1s
ℹ Compiling Experiment.
single_cell_sct
#> # A SingleCellExperiment-tibble abstraction: 1,244 × 77
#> # �[90mFeatures=33145 | Cells=1244 | Assays=sct�[0m
#> .cell observation_joinid dataset_id sample_id sample_ experiment___ run_from_cell_id sample_heuristic age_days tissue_groups nFeature_expressed_i…¹
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> <chr> <int>
#> 1 72_1 8wGs7JgUjj 842c6f5d-4… 6b194412… 6b1944… "" <NA> b3ff1aad-40fd-4… 14600 breast 2548
#> 2 75_1 F9G7A+GgjA 842c6f5d-4… db5a69ed… db5a69… "" <NA> 49beb83c-66a1-4… 14600 breast 1291
#> 3 1_1 I8a42<8st4 842c6f5d-4… 184fa234… 184fa2… "" <NA> c2aa4d8d-e9df-4… 14600 breast 3395
#> 4 73_1 z_=CTOs4{z 842c6f5d-4… 4b5e66fa… 4b5e66… "" <NA> 04983012-bb56-4… 14600 breast 2866
#> 5 74_1 fNzorxA`Mf 842c6f5d-4… 4b5e66fa… 4b5e66… "" <NA> 04983012-bb56-4… 14600 breast 1942
#> 6 80_1 zz-!e5_XAo 842c6f5d-4… 1de3f3ba… 1de3f3… "" <NA> 7fabaf1c-52fd-4… 14600 breast 1749
#> 7 81_1 -mb&DWckf( 842c6f5d-4… 1de3f3ba… 1de3f3… "" <NA> 7fabaf1c-52fd-4… 14600 breast 1993
#> 8 22_2 2lQ`<&l3-A 842c6f5d-4… 30967738… 309677… "" <NA> 7d4045ff-3f48-4… 14600 breast 2058
#> 9 23_2 sqV-|vcI4R 842c6f5d-4… d8ecdd92… d8ecdd… "" <NA> bc909bba-be16-4… 14600 breast 2375
#> 10 5_2 +p4uNj_7$S 842c6f5d-4… a91e6814… a91e68… "" <NA> 700a819c-03f9-4… 14600 breast 1870
#> # ℹ 1,234 more rows
#> # ℹ abbreviated name: ¹nFeature_expressed_in_sample
#> # ℹ 66 more variables: nCount_RNA <dbl>, empty_droplet <lgl>, cell_type_unified_ensemble <chr>, is_immune <lgl>, subsets_Mito_percent <int>,
#> # subsets_Ribo_percent <int>, high_mitochondrion <lgl>, high_ribosome <lgl>, scDblFinder.class <chr>, sample_chunk <int>, cell_chunk <int>,
#> # sample_pseudobulk_chunk <int>, file_id_cellNexus_single_cell <chr>, file_id_cellNexus_pseudobulk <chr>, count_upper_bound <dbl>,
#> # nfeature_expressed_thresh <dbl>, inverse_transform <chr>, alive <lgl>, cell_annotation_blueprint_singler <chr>,
#> # cell_annotation_monaco_singler <chr>, cell_annotation_azimuth_l2 <chr>, ethnicity_flagging_score <dbl>, low_confidence_ethnicity <chr>, …pseudobulk_counts <-
metadata |>
dplyr::filter(
assay == "10x 5' v1" &
tissue == "lung" &
cell_type == "classical monocyte"
) |>
get_pseudobulk()
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Downloading 6 files, totalling 0.98 GB
#> ℹ Downloading 6 files in parallel...
#> ℹ Reading files.
#>
Reading counts ■■■■■ 14% | ETA: 19s
Reading counts ■■■■■■■■■■ 29% | ETA: 20s
Reading counts ■■■■■■■■■■■■■■ 43% | ETA: 16s
Reading counts ■■■■■■■■■■■■■■■■■■ 57% | ETA: 12s
Reading counts ■■■■■■■■■■■■■■■■■■■■■■ 71% | ETA: 8s
Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■■■ 86% | ETA: 4s
! cellNexus says: Not all genes completely overlap across the provided objects. Counts are generated by genes intersection.
#> ℹ Compiling Experiment.
pseudobulk_counts
#> # A SingleCellExperiment-tibble abstraction: 139 × 60
#> # �[90mFeatures=15888 | Cells=139 | Assays=counts�[0m
#> .cell dataset_id sample_id sample_ experiment___ run_from_cell_id sample_heuristic age_days tissue_groups cell_type_unified_en…¹ sample_chunk
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> <chr> <chr> <int>
#> 1 2e8c9911c9b… 0ba16f4b-… 2e8c9911… 2e8c99… "" <NA> HDBR15279,HDBR1… NA respiratory … cd14 mono 1
#> 2 f71af64a552… 1e6a6ef9-… f71af64a… f71af6… "" <NA> Leader_Merad_20… 27010 respiratory … monocytic 1
#> 3 f71af64a552… 1e6a6ef9-… f71af64a… f71af6… "" <NA> Leader_Merad_20… 27010 respiratory … cd14 mono 1
#> 4 f71af64a552… 1e6a6ef9-… f71af64a… f71af6… "" <NA> Leader_Merad_20… 27010 respiratory … cd8 tem 1
#> 5 0d874636bc7… 1e6a6ef9-… 0d874636… 0d8746… "" <NA> Leader_Merad_20… 29930 respiratory … cd14 mono 1
#> 6 0d874636bc7… 1e6a6ef9-… 0d874636… 0d8746… "" <NA> Leader_Merad_20… 29930 respiratory … monocytic 1
#> 7 0d874636bc7… 1e6a6ef9-… 0d874636… 0d8746… "" <NA> Leader_Merad_20… 29930 respiratory … cd16 mono 1
#> 8 0d874636bc7… 1e6a6ef9-… 0d874636… 0d8746… "" <NA> Leader_Merad_20… 29930 respiratory … macrophage 1
#> 9 0d874636bc7… 1e6a6ef9-… 0d874636… 0d8746… "" <NA> Leader_Merad_20… 29930 respiratory … other 1
#> 10 11721339cb1… 1e6a6ef9-… 11721339… 117213… "" <NA> Leader_Merad_20… 26645 respiratory … monocytic 1
#> # ℹ 129 more rows
#> # ℹ abbreviated name: ¹cell_type_unified_ensemble
#> # ℹ 49 more variables: cell_chunk <int>, sample_pseudobulk_chunk <int>, file_id_cellNexus_pseudobulk <chr>, count_upper_bound <dbl>,
#> # nfeature_expressed_thresh <dbl>, inverse_transform <chr>, ethnicity_flagging_score <dbl>, low_confidence_ethnicity <chr>, .aggregated_cells <int>,
#> # imputed_ethnicity <chr>, atlas_id <chr>, assay <chr>, assay_ontology_term_id <chr>, development_stage <chr>,
#> # development_stage_ontology_term_id <chr>, disease <chr>, disease_ontology_term_id <chr>, donor_id <chr>, is_primary_data <chr>, organism <chr>,
#> # organism_ontology_term_id <chr>, self_reported_ethnicity <chr>, self_reported_ethnicity_ontology_term_id <chr>, sex <chr>, …Cell 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.
For demonstration purpose, read cell communication metadata from a demo file here. Users do not need to specify cloud_metadata argument in this case.
get_cell_communication_strength(cloud_metadata = get_metadata_url("cellNexus_lr_signaling_pathway_strength_DEMO.parquet"))
#> # Source: SQL [?? x 16]
#> # Database: DuckDB 1.4.3 [unknown@Linux 5.14.0-570.112.1.el9_6.x86_64:R 4.5.3/:memory:]
#> source target ligand receptor lr_prob lr_pval interaction_name interaction_name_2 pathway_name annotation evidence pathway_prob pathway_pval
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 b b TGFB1 TGFbR1_R2 0.000116 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TG… TGFb Secreted … KEGG: h… 0.000420 1
#> 2 b memory b TGFB1 TGFbR1_R2 0.000865 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TG… TGFb Secreted … KEGG: h… 0.00185 1
#> 3 b naive b TGFB1 TGFbR1_R2 0.000696 0.99 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TG… TGFb Secreted … KEGG: h… 0.00146 0.994
#> 4 cd14 mono b TGFB1 TGFbR1_R2 0.00240 0.81 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TG… TGFb Secreted … KEGG: h… 0.00472 0.924
#> 5 cd4 naive b TGFB1 TGFbR1_R2 0.000957 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TG… TGFb Secreted … KEGG: h… 0.00201 0.998
#> 6 cd4 tem b TGFB1 TGFbR1_R2 0.00242 0.76 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TG… TGFb Secreted … KEGG: h… 0.00467 0.797
#> # ℹ 3 more variables: 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.
#>
Reading cpm ■■■■ 10% | ETA: 12s
Reading cpm ■■■■■■■ 20% | ETA: 10s
Reading cpm ■■■■■■■■■■ 30% | ETA: 10s
Reading cpm ■■■■■■■■■■■■■ 40% | ETA: 8s
Reading cpm ■■■■■■■■■■■■■■■■ 50% | ETA: 7s
Reading cpm ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 5s
Reading cpm ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 4s
Reading cpm ■■■■■■■■■■■■■■■■■■■■■■■■■ 80% | ETA: 3s
Reading cpm ■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 90% | ETA: 1s
ℹ Compiling Experiment.
single_cell_cpm
#> # A SingleCellExperiment-tibble abstraction: 2,924 × 77
#> # �[90mFeatures=1 | Cells=2924 | Assays=cpm�[0m
#> .cell observation_joinid dataset_id sample_id sample_ experiment___ run_from_cell_id sample_heuristic age_days tissue_groups nFeature_expressed_i…¹
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> <chr> <int>
#> 1 1_1 I8a42<8st4 842c6f5d-4… 184fa234… 184fa2… "" <NA> c2aa4d8d-e9df-4… 14600 breast 3395
#> 2 76_1 bTlx!HK=oS 842c6f5d-4… 52ab9222… 52ab92… "" <NA> 7ce86149-8906-4… 14600 breast 1671
#> 3 77_1 E4g5+)v;AV 842c6f5d-4… 52ab9222… 52ab92… "" <NA> 7ce86149-8906-4… 14600 breast 2340
#> 4 78_1 +q?29B%2nH 842c6f5d-4… 52ab9222… 52ab92… "" <NA> 7ce86149-8906-4… 14600 breast 1714
#> 5 79_1 zuJ#MBMWy; 842c6f5d-4… 52ab9222… 52ab92… "" <NA> 7ce86149-8906-4… 14600 breast 1506
#> 6 72_1 8wGs7JgUjj 842c6f5d-4… 6b194412… 6b1944… "" <NA> b3ff1aad-40fd-4… 14600 breast 2548
#> 7 75_1 F9G7A+GgjA 842c6f5d-4… db5a69ed… db5a69… "" <NA> 49beb83c-66a1-4… 14600 breast 1291
#> 8 73_1 z_=CTOs4{z 842c6f5d-4… 4b5e66fa… 4b5e66… "" <NA> 04983012-bb56-4… 14600 breast 2866
#> 9 74_1 fNzorxA`Mf 842c6f5d-4… 4b5e66fa… 4b5e66… "" <NA> 04983012-bb56-4… 14600 breast 1942
#> 10 80_1 zz-!e5_XAo 842c6f5d-4… 1de3f3ba… 1de3f3… "" <NA> 7fabaf1c-52fd-4… 14600 breast 1749
#> # ℹ 2,914 more rows
#> # ℹ abbreviated name: ¹nFeature_expressed_in_sample
#> # ℹ 66 more variables: nCount_RNA <dbl>, empty_droplet <lgl>, cell_type_unified_ensemble <chr>, is_immune <lgl>, subsets_Mito_percent <int>,
#> # subsets_Ribo_percent <int>, high_mitochondrion <lgl>, high_ribosome <lgl>, scDblFinder.class <chr>, sample_chunk <int>, cell_chunk <int>,
#> # sample_pseudobulk_chunk <int>, file_id_cellNexus_single_cell <chr>, file_id_cellNexus_pseudobulk <chr>, count_upper_bound <dbl>,
#> # nfeature_expressed_thresh <dbl>, inverse_transform <chr>, alive <lgl>, cell_annotation_blueprint_singler <chr>,
#> # cell_annotation_monaco_singler <chr>, cell_annotation_azimuth_l2 <chr>, ethnicity_flagging_score <dbl>, low_confidence_ethnicity <chr>, …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.
#>
Reading counts ■■■■ 10% | ETA: 12s
Reading counts ■■■■■■■ 20% | ETA: 10s
Reading counts ■■■■■■■■■■ 30% | ETA: 9s
Reading counts ■■■■■■■■■■■■■ 40% | ETA: 8s
Reading counts ■■■■■■■■■■■■■■■■ 50% | ETA: 7s
Reading counts ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 5s
Reading counts ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 4s
Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■ 80% | ETA: 3s
Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 90% | ETA: 1s
ℹ Compiling Experiment.
seurat_counts
#> An object of class Seurat
#> 33145 features across 2924 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 path to metadata and
counts cache_directory argument. For example,
get_metadata(cache_directory = "your_own_path") and
get_single_cell_experiment(cache_directory = "your_own_path").
Same strategy can be applied for functions get_pseuodbulk() and
get_seurat().
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.
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)
#> Error:
#> ! error in evaluating the argument '.data' in selecting a method for function 'join_features': ℹ In index: 1.
#> ℹ With name: cpm.
#> Caused by error in `map()` at cellNexus/R/counts.R:363:5:
#> ℹ In index: 1.
#> Caused by error in `group_to_data_container()` at cellNexus/R/counts.R:366:7:
#> ! Your cache does not contain the file
#> /vast/scratch/users/shen.m/r_cache/R/cellNexus/cellxgene_2024/0.4.0/cpm/001f82656d61ccb98f0ae26a2eb9e5ba___1.h5ad you attempted to query. Please
#> provide the repository parameter so that files can be synchronised from the internet
# 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")
#> Warning in scale_y_log10(): log-10 transformation introduced infinite values.
# 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))
#> Warning in scale_y_log10(): log-10 transformation introduced infinite values.
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 = cellNexus::SAMPLE_DATABASE_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.
#> # A SingleCellExperiment-tibble abstraction: 500 × 7
#> # �[90mFeatures=13132 | Cells=500 | Assays=counts�[0m
#> .cell sample_id dataset_id cell_type_unified_ensemble atlas_id file_id_cellNexus_single_cell original_cell_
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 AAACATACAACCAC_1 pbmc3k pbmc3k Memory CD4 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACATACAACCAC
#> 2 AAACATTGAGCTAC_1 pbmc3k pbmc3k B cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACATTGAGCTAC
#> 3 AAACATTGATCAGC_1 pbmc3k pbmc3k Memory CD4 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACATTGATCAGC
#> 4 AAACCGTGCTTCCG_1 pbmc3k pbmc3k CD14+ Mono cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACCGTGCTTCCG
#> 5 AAACCGTGTATGCG_1 pbmc3k pbmc3k NK cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACCGTGTATGCG
#> 6 AAACGCACTGGTAC_1 pbmc3k pbmc3k Memory CD4 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACGCACTGGTAC
#> 7 AAACGCTGACCAGT_1 pbmc3k pbmc3k CD8 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACGCTGACCAGT
#> 8 AAACGCTGGTTCTT_1 pbmc3k pbmc3k CD8 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACGCTGGTTCTT
#> 9 AAACGCTGTAGCCA_1 pbmc3k pbmc3k Naive CD4 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACGCTGTAGCCA
#> 10 AAACGCTGTTTCTG_1 pbmc3k pbmc3k FCGR3A+ Mono cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACGCTGTTTCTG
#> # ℹ 490 more rowsThe complete metadata dictionary for the harmonised fields is available on the documentation site: cellNexus documentation.
The counts assay represents RNA abundance on the positive real scale,
without non-linear transformations (e.g., log or square root). In the
original CELLxGENE data, values were provided using a mix of scales and
transformations. The method required to invert these transformations is
recorded in inverse_transform 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 using
singscore.
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 9.6 (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 LC_MONETARY=en_US.UTF-8
#> [6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Australia/Melbourne
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] BiocStyle_2.38.0 ggplot2_4.0.2 dplyr_1.2.1 cellNexus_0.99.22
#>
#> loaded via a namespace (and not attached):
#> [1] RcppAnnoy_0.0.23 splines_4.5.3 later_1.4.8 filelock_1.0.3
#> [5] tibble_3.3.1 polyclip_1.10-7 fastDummies_1.7.5 lifecycle_1.0.5
#> [9] rprojroot_2.1.1 globals_0.19.1 lattice_0.22-9 MASS_7.3-65
#> [13] backports_1.5.1 magrittr_2.0.5 sass_0.4.10 plotly_4.12.0
#> [17] rmarkdown_2.31 jquerylib_0.1.4 yaml_2.3.12 httpuv_1.6.17
#> [21] otel_0.2.0 Seurat_5.5.0.9002 sctransform_0.4.3 spam_2.11-3
#> [25] sp_2.2-1 sessioninfo_1.2.3 pkgbuild_1.4.8 spatstat.sparse_3.1-0
#> [29] reticulate_1.46.0 cowplot_1.2.0 pbapply_1.7-4 DBI_1.3.0
#> [33] RColorBrewer_1.1-3 abind_1.4-8 pkgload_1.5.1 Rtsne_0.17
#> [37] GenomicRanges_1.62.1 purrr_1.2.2 BiocGenerics_0.56.0 tidySingleCellExperiment_1.20.1
#> [41] IRanges_2.44.0 S4Vectors_0.49.1-1 ggrepel_0.9.8 irlba_2.3.7
#> [45] listenv_0.10.1 spatstat.utils_3.2-2 goftest_1.2-3 RSpectra_0.16-2
#> [49] spatstat.random_3.4-5 fitdistrplus_1.2-6 parallelly_1.46.1 commonmark_2.0.0
#> [53] codetools_0.2-20 DelayedArray_0.36.1 xml2_1.5.2 tidyselect_1.2.1
#> [57] rclipboard_0.2.1 UCSC.utils_1.6.1 farver_2.1.2 shinyWidgets_0.9.1
#> [61] matrixStats_1.5.0 stats4_4.5.3 spatstat.explore_3.8-0 duckdb_1.4.3
#> [65] Seqinfo_1.0.0 roxygen2_7.3.3 jsonlite_2.0.0 ellipsis_0.3.3
#> [69] progressr_0.19.0 ggridges_0.5.7 survival_3.8-6 tools_4.5.3
#> [73] ica_1.0-3 Rcpp_1.1.1-1 glue_1.8.0 gridExtra_2.3
#> [77] SparseArray_1.10.10 xfun_0.57 MatrixGenerics_1.22.0 usethis_3.2.1
#> [81] GenomeInfoDb_1.46.2 HDF5Array_1.38.0 withr_3.0.2 BiocManager_1.30.27
#> [85] fastmap_1.2.0 basilisk_1.22.0 fansi_1.0.7 rhdf5filters_1.22.0
#> [89] ttservice_0.5.3 digest_0.6.39 R6_2.6.1 mime_0.13
#> [93] scattermore_1.2 tensor_1.5.1 spatstat.data_3.1-9 h5mread_1.2.1
#> [97] utf8_1.2.6 tidyr_1.3.2 generics_0.1.4 data.table_1.18.2.1
#> [101] httr_1.4.8 htmlwidgets_1.6.4 S4Arrays_1.10.1 uwot_0.2.4
#> [105] pkgconfig_2.0.3 gtable_0.3.6 rsconnect_1.8.0 blob_1.3.0
#> [109] lmtest_0.9-40 S7_0.2.1-1 SingleCellExperiment_1.32.0 XVector_0.50.0
#> [113] htmltools_0.5.9 bookdown_0.46 dotCall64_1.2 SeuratObject_5.4.0
#> [117] scales_1.4.0 Biobase_2.70.0 png_0.1-9 spatstat.univar_3.1-7
#> [121] knitr_1.51 rstudioapi_0.18.0 reshape2_1.4.5 checkmate_2.3.4
#> [125] nlme_3.1-168 curl_7.0.0 anndataR_1.0.2 rhdf5_2.54.1
#> [129] cachem_1.1.0 zoo_1.8-15 stringr_1.6.0 KernSmooth_2.23-26
#> [133] parallel_4.5.3 miniUI_0.1.2 arrow_23.0.1.2 zellkonverter_1.20.1
#> [137] desc_1.4.3 pillar_1.11.1 grid_4.5.3 vctrs_0.7.3
#> [141] RANN_2.6.2 promises_1.5.0 dbplyr_2.5.2 xtable_1.8-8
#> [145] cluster_2.1.8.2 evaluate_1.0.5 cli_3.6.6 compiler_4.5.3
#> [149] rlang_1.2.0 future.apply_1.20.2 forcats_1.0.1 plyr_1.8.9
#> [153] fs_2.0.1 stringi_1.8.7 viridisLite_0.4.3 deldir_2.0-4
#> [157] assertthat_0.2.1 lazyeval_0.2.3 devtools_2.5.0 spatstat.geom_3.7-3
#> [161] Matrix_1.7-4 dir.expiry_1.18.0 RcppHNSW_0.6.0 patchwork_1.3.2
#> [165] bit64_4.6.0-1 future_1.70.0 Rhdf5lib_1.32.0 shiny_1.13.0
#> [169] SummarizedExperiment_1.40.0 ROCR_1.0-12 igraph_2.2.3 memoise_2.0.1
#> [173] bslib_0.10.0 bit_4.6.0