cellNexus
cellNexus extends the functionality of CuratedAtlasQueryR by providing a unified interface for querying and accessing the harmonised, curated, and reannotated the 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 levels with flexible filtering based on tissue, cell type, and 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 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.
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:::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 0.48 GB
#> ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellNexus-metadata/census_cell_metadata.2.2.1.parquet to /vast/scratch/users/shen.m/r_cache/R/cellNexus/census_cell_metadata.2.2.1.parquet
#> ℹ Downloading 1 file, totalling 1.05 GB
#> ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellNexus-metadata/cellnexus_metadata.2.2.1.parquet to /vast/scratch/users/shen.m/r_cache/R/cellNexus/cellnexus_metadata.2.2.1.parquet
metadata
#> # Source: SQL [?? x 76]
#> # 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_ experiment___ run_from_cell_id sample_heuristic age_days tissue_groups nFeature_expressed_i…¹
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> <chr> <int>
#> 1 6839 POgui6S7Qc b693123e-95e6-433… 76a27a0f… 76a27a… "" <NA> 3177edf1-07ba-4… 10950 female repro… 3141
#> 2 1188 M<;(?MCuvK b693123e-95e6-433… 76a27a0f… 76a27a… "" <NA> 3177edf1-07ba-4… 10950 female repro… 1637
#> 3 5977 mxxJIMO(>% b693123e-95e6-433… 76a27a0f… 76a27a… "" <NA> 3177edf1-07ba-4… 10950 female repro… 1258
#> 4 6865 tt6~mWiv7? b693123e-95e6-433… 76a27a0f… 76a27a… "" <NA> 3177edf1-07ba-4… 10950 female repro… 2418
#> 5 5982 5X5EHORa~h b693123e-95e6-433… 76a27a0f… 76a27a… "" <NA> 3177edf1-07ba-4… 10950 female repro… 1221
#> 6 6881 iK<K(I`{<X b693123e-95e6-433… 76a27a0f… 76a27a… "" <NA> 3177edf1-07ba-4… 10950 female repro… 2231
#> 7 4425 (}x~|AA~Tk b693123e-95e6-433… 76a27a0f… 76a27a… "" <NA> 3177edf1-07ba-4… 10950 female repro… 810
#> 8 4426 {8`t7Ldm$^ b693123e-95e6-433… 76a27a0f… 76a27a… "" <NA> 3177edf1-07ba-4… 10950 female repro… 478
#> 9 2878 kkw$nx}I?a b693123e-95e6-433… 76a27a0f… 76a27a… "" <NA> 3177edf1-07ba-4… 10950 female repro… 1301
#> 10 1350 F#0nn;C3A7 b693123e-95e6-433… 76a27a0f… 76a27a… "" <NA> 3177edf1-07ba-4… 10950 female repro… 3439
#> # ℹ more rows
#> # ℹ abbreviated name: ¹nFeature_expressed_in_sample
#> # ℹ 65 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>, .aggregated_cells <int>, imputed_ethnicity <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 4.5.3/:memory:]
#> tissue cell_type_unified_ensemble
#> <chr> <chr>
#> 1 trachea pdc
#> 2 trachea treg
#> 3 lower lobe of left lung tgd
#> 4 frontal cortex endothelial
#> 5 bronchus b memory
#> 6 bronchus pneumocyte
#> 7 bronchus dc
#> 8 middle temporal gyrus neuron
#> 9 middle temporal gyrus glial
#> 10 duodenum epithelial
#> # 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 ■■■■■■■ 20% | ETA: 6s
Reading counts ■■■■■■■■■■ 30% | ETA: 5s
Reading counts ■■■■■■■■■■■■■ 40% | ETA: 4s
Reading counts ■■■■■■■■■■■■■■■■ 50% | ETA: 3s
Reading counts ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 2s
Reading counts ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 2s
Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■ 80% | ETA: 1s
Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 90% | ETA: 1s
ℹ Compiling Experiment.
single_cell_counts
#> class: SingleCellExperiment
#> dim: 33145 2908
#> metadata(0):
#> assays(1): counts
#> rownames(33145): ENSG00000243485 ENSG00000237613 ... ENSG00000277475 ENSG00000268674
#> rowData names(0):
#> colnames(2908): 79_1 76_1 ... 3_10 12_10
#> colData names(76): observation_joinid dataset_id ... url 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 Downloading 10 files, totalling 0.03 GB
#> ℹ Downloading 10 files in parallel...
#> ℹ Reading files.
#>
Reading cpm ■■■■■■■■■■ 30% | ETA: 4s
Reading cpm ■■■■■■■■■■■■■ 40% | ETA: 3s
Reading cpm ■■■■■■■■■■■■■■■■ 50% | ETA: 3s
Reading cpm ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 2s
Reading cpm ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 2s
Reading cpm ■■■■■■■■■■■■■■■■■■■■■■■■■ 80% | ETA: 1s
Reading cpm ■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 90% | ETA: 1s
ℹ Compiling Experiment.
single_cell_cpm
#> class: SingleCellExperiment
#> dim: 33145 2908
#> metadata(0):
#> assays(1): cpm
#> rownames(33145): ENSG00000243485 ENSG00000237613 ... ENSG00000277475 ENSG00000268674
#> rowData names(0):
#> colnames(2908): 72_1 81_1 ... 3_10 5_10
#> colData names(76): observation_joinid dataset_id ... url 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 Downloading 10 files, totalling 0.09 GB
#> ℹ Downloading 10 files in parallel...
#> ℹ Reading files.
#>
Reading sct ■■■■■■■ 20% | ETA: 4s
Reading sct ■■■■■■■■■■ 30% | ETA: 3s
Reading sct ■■■■■■■■■■■■■ 40% | ETA: 3s
Reading sct ■■■■■■■■■■■■■■■■ 50% | ETA: 3s
Reading sct ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 2s
Reading sct ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 2s
Reading sct ■■■■■■■■■■■■■■■■■■■■■■■■■ 80% | ETA: 1s
Reading sct ■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 90% | ETA: 0s
ℹ Compiling Experiment.
single_cell_sct
#> class: SingleCellExperiment
#> dim: 33145 2908
#> metadata(0):
#> assays(1): sct
#> rownames(33145): ENSG00000243485 ENSG00000237613 ... ENSG00000277475 ENSG00000268674
#> rowData names(0):
#> colnames(2908): 77_1 78_1 ... 2_10 3_10
#> colData names(76): observation_joinid dataset_id ... url 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 Downloading 7 files, totalling 1.22 GB
#> ℹ Downloading 7 files in parallel...
#> ℹ Reading files.
#>
Reading counts ■■■■■ 14% | ETA: 8s
Reading counts ■■■■■■■■■■ 29% | ETA: 7s
Reading counts ■■■■■■■■■■■■■■ 43% | ETA: 6s
Reading counts ■■■■■■■■■■■■■■■■■■ 57% | ETA: 4s
Reading counts ■■■■■■■■■■■■■■■■■■■■■■ 71% | ETA: 3s
Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■■■ 86% | ETA: 1s
! cellNexus says: Not all genes completely overlap across the provided objects. Counts are generated by genes intersection.
#> ℹ Compiling Experiment.
pseudobulk_counts
#> class: SingleCellExperiment
#> dim: 15888 139
#> metadata(0):
#> assays(1): counts
#> rownames(15888): ENSG00000177757 ENSG00000225880 ... ENSG00000160307 ENSG00000160310
#> rowData names(0):
#> colnames(139): 2e8c9911c9bfbffc07288adef93d3cf2___cd14 mono f71af64a552d45f5904ca03f4d12b461___cd14 mono ...
#> 1c7e90df93b48acabb013c8202830df5___cd14 mono 1c7e90df93b48acabb013c8202830df5___monocytic
#> colData names(59): dataset_id sample_id ... url 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.
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"))
#> ℹ Downloading 1 file, totalling 0 GB
#> ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellNexus-metadata/cellNexus_lr_signaling_pathway_strength_DEMO.parquet to /vast/scratch/users/shen.m/r_cache/R/cellNexus/cellNexus_lr_signaling_pathway_strength_DEMO.parquet
#> # 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 pathway_prob pathway_pval sample_id
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr>
#> 1 b b TGFB1 TGFbR1_R2 0.000116 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+T… TGFb Secreted … KEGG: h… 0.000420 1 b290d7ef…
#> 2 b memory b TGFB1 TGFbR1_R2 0.000865 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+T… TGFb Secreted … KEGG: h… 0.00185 1 b290d7ef…
#> 3 b naive b TGFB1 TGFbR1_R2 0.000696 0.99 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+T… TGFb Secreted … KEGG: h… 0.00146 0.994 b290d7ef…
#> 4 cd14 mono b TGFB1 TGFbR1_R2 0.00240 0.81 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+T… TGFb Secreted … KEGG: h… 0.00472 0.924 b290d7ef…
#> 5 cd4 naive b TGFB1 TGFbR1_R2 0.000957 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+T… TGFb Secreted … KEGG: h… 0.00201 0.998 b290d7ef…
#> 6 cd4 tem b TGFB1 TGFbR1_R2 0.00242 0.76 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+T… TGFb Secreted … KEGG: h… 0.00467 0.797 b290d7ef…
#> # ℹ 2 more variables: 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: 9s
Reading cpm ■■■■■■■ 20% | ETA: 6s
Reading cpm ■■■■■■■■■■ 30% | ETA: 5s
Reading cpm ■■■■■■■■■■■■■ 40% | ETA: 4s
Reading cpm ■■■■■■■■■■■■■■■■ 50% | ETA: 3s
Reading cpm ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 2s
Reading cpm ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 2s
Reading cpm ■■■■■■■■■■■■■■■■■■■■■■■■■ 80% | ETA: 1s
Reading cpm ■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 90% | ETA: 1s
ℹ Compiling Experiment.
single_cell_cpm
#> class: SingleCellExperiment
#> dim: 1 2908
#> metadata(0):
#> assays(1): cpm
#> rownames(1): ENSG00000134644
#> rowData names(0):
#> colnames(2908): 81_1 75_1 ... 3_10 10_10
#> colData names(76): observation_joinid dataset_id ... url 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.
#>
Reading counts ■■■■■■■ 20% | ETA: 6s
Reading counts ■■■■■■■■■■ 30% | ETA: 5s
Reading counts ■■■■■■■■■■■■■ 40% | ETA: 4s
Reading counts ■■■■■■■■■■■■■■■■ 50% | ETA: 3s
Reading counts ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 2s
Reading counts ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 2s
Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■ 80% | ETA: 1s
Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 90% | ETA: 1s
ℹ Compiling Experiment.
seurat_counts
#> An object of class Seurat
#> 33145 features across 2908 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)
# 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.
#> 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 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 9.3 (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 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] cellNexus_0.99.21 BiocStyle_2.38.0 ggplot2_4.0.2 dplyr_1.2.1
#>
#> loaded via a namespace (and not attached):
#> [1] fs_2.0.1 matrixStats_1.5.0 spatstat.sparse_3.1-0 xopen_1.0.1
#> [5] devtools_2.5.0 httr_1.4.8 RColorBrewer_1.1-3 tools_4.5.3
#> [9] sctransform_0.4.3 backports_1.5.1 utf8_1.2.6 R6_2.6.1
#> [13] HDF5Array_1.38.0 lazyeval_0.2.3 uwot_0.2.4 rhdf5filters_1.22.0
#> [17] withr_3.0.2 sp_2.2-1 prettyunits_1.2.0 gridExtra_2.3
#> [21] progressr_0.19.0 cli_3.6.6 Biobase_2.70.0 spatstat.explore_3.8-0
#> [25] fastDummies_1.7.5 sass_0.4.10 Seurat_5.4.0 arrow_23.0.1.2
#> [29] S7_0.2.1-1 spatstat.data_3.1-9 ggridges_0.5.7 pbapply_1.7-4
#> [33] commonmark_2.0.0 R.utils_2.13.0 parallelly_1.46.1 sessioninfo_1.2.3
#> [37] styler_1.11.0 rstudioapi_0.18.0 generics_0.1.4 ica_1.0-3
#> [41] spatstat.random_3.4-5 Matrix_1.7-4 fansi_1.0.7 S4Vectors_0.49.1-1
#> [45] rclipboard_0.2.1 abind_1.4-8 R.methodsS3_1.8.2 lifecycle_1.0.5
#> [49] yaml_2.3.12 SummarizedExperiment_1.40.0 rhdf5_2.54.1 SparseArray_1.10.10
#> [53] Rtsne_0.17 grid_4.5.3 blob_1.3.0 promises_1.5.0
#> [57] dir.expiry_1.18.0 miniUI_0.1.2 lattice_0.22-9 cowplot_1.2.0
#> [61] pillar_1.11.1 knitr_1.51 GenomicRanges_1.62.1 future.apply_1.20.2
#> [65] codetools_0.2-20 glue_1.8.0 spatstat.univar_3.1-7 data.table_1.18.2.1
#> [69] tidySingleCellExperiment_1.20.1 vctrs_0.7.3 png_0.1-9 spam_2.11-3
#> [73] testthat_3.3.2 rcmdcheck_1.4.0 gtable_0.3.6 assertthat_0.2.1
#> [77] cachem_1.1.0 xfun_0.57 S4Arrays_1.10.1 mime_0.13
#> [81] Seqinfo_1.0.0 rsconnect_1.8.0 survival_3.8-6 SingleCellExperiment_1.32.0
#> [85] ellipsis_0.3.3 fitdistrplus_1.2-6 ROCR_1.0-12 nlme_3.1-168
#> [89] usethis_3.2.1 bit64_4.6.0-1 filelock_1.0.3 RcppAnnoy_0.0.23
#> [93] GenomeInfoDb_1.46.2 rprojroot_2.1.1 R.cache_0.17.0 bslib_0.10.0
#> [97] irlba_2.3.7 KernSmooth_2.23-26 otel_0.2.0 BiocGenerics_0.56.0
#> [101] DBI_1.3.0 zellkonverter_1.20.1 duckdb_1.4.3 processx_3.8.7
#> [105] tidyselect_1.2.1 bit_4.6.0 compiler_4.5.3 curl_7.0.0
#> [109] h5mread_1.2.1 xml2_1.5.2 desc_1.4.3 DelayedArray_0.36.1
#> [113] plotly_4.12.0 bookdown_0.46 checkmate_2.3.4 scales_1.4.0
#> [117] lmtest_0.9-40 callr_3.7.6 stringr_1.6.0 anndataR_1.0.2
#> [121] digest_0.6.39 goftest_1.2-3 spatstat.utils_3.2-2 rmarkdown_2.31
#> [125] basilisk_1.22.0 XVector_0.50.0 htmltools_0.5.9 pkgconfig_2.0.3
#> [129] MatrixGenerics_1.22.0 dbplyr_2.5.2 fastmap_1.2.0 rlang_1.2.0
#> [133] htmlwidgets_1.6.4 UCSC.utils_1.6.1 shiny_1.13.0 farver_2.1.2
#> [137] jquerylib_0.1.4 zoo_1.8-15 jsonlite_2.0.0 R.oo_1.27.1
#> [141] magrittr_2.0.5 dotCall64_1.2 patchwork_1.3.2 Rhdf5lib_1.32.0
#> [145] Rcpp_1.1.1-1 reticulate_1.46.0 stringi_1.8.7 brio_1.1.5
#> [149] MASS_7.3-65 plyr_1.8.9 pkgbuild_1.4.8 parallel_4.5.3
#> [153] listenv_0.10.1 ggrepel_0.9.8 forcats_1.0.1 deldir_2.0-4
#> [157] splines_4.5.3 tensor_1.5.1 ps_1.9.2 igraph_2.2.3
#> [161] spatstat.geom_3.7-3 RcppHNSW_0.6.0 reshape2_1.4.5 stats4_4.5.3
#> [165] pkgload_1.5.1 evaluate_1.0.5 ttservice_0.5.3 SeuratObject_5.4.0
#> [169] BiocManager_1.30.27 httpuv_1.6.17 RANN_2.6.2 tidyr_1.3.2
#> [173] purrr_1.2.2 polyclip_1.10-7 future_1.70.0 scattermore_1.2
#> [177] xtable_1.8-8 RSpectra_0.16-2 roxygen2_7.3.3 later_1.4.8
#> [181] viridisLite_0.4.3 tibble_3.3.1 memoise_2.0.1 IRanges_2.44.0
#> [185] cluster_2.1.8.2 shinyWidgets_0.9.1 globals_0.19.1