cellNexus

Introduction

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.

Repositories

R API: here

Python API: here

Article code: here

Query interface

Installation

devtools::install_github("MangiolaLaboratory/cellNexus")

Load the package

library(cellNexus)

Load additional packages

suppressPackageStartupMessages({
  library(ggplot2)
})

Load and explore the metadata

Load the metadata

By default, get_metadata() loads harmonised annotations. 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.

The unified pseudobulk AnnData object was pre-generated outside of this vignette applying quality control and retaining at least 15,000 intersecting genes across samples and hosted on Zenodo to avoid lengthy recompilation. Download the latest version: pseudobulk_se.h5ad. For all versions: 10.5281/zenodo.21633607.

The following sections demonstrate the metadata, quality control, generation of raw and normalised counts, and pseudobulk construction for the specified query.

metadata <- get_metadata()
metadata
#> # Source:   SQL [?? x 37]
#> # Database: DuckDB 1.4.3 [unknown@Linux 5.14.0-570.123.1.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 nFeature_expressed_i…¹ nCount_RNA
#>      <dbl> <chr>              <chr>        <chr>     <chr>   <chr>         <chr>            <chr>               <int> <chr>                          <int>      <dbl>
#>  1       1 `;+Wwc*oS9         574e9f9e-f8… b290d7ef… b290d7… ""            <NA>             BPH556PrGA2_Fco…    25915 prostate                        1025      113. 
#>  2       1 s<8rT5qe3X         574e9f9e-f8… b290d7ef… b290d7… ""            <NA>             BPH556PrGA2_Fco…    25915 prostate                        2586       77.6
#>  3       2 Se=|eIq*={         574e9f9e-f8… b290d7ef… b290d7… ""            <NA>             BPH556PrGA2_Fco…    25915 prostate                         992       57.6
#>  4       2 dcNO`ReB5o         574e9f9e-f8… b290d7ef… b290d7… ""            <NA>             BPH556PrGA2_Fco…    25915 prostate                        2002      163. 
#>  5       3 F_Jf~Pzj<!         574e9f9e-f8… b290d7ef… b290d7… ""            <NA>             BPH556PrGA2_Fco…    25915 prostate                         730       93.6
#>  6      16 i(U>N;cU4_         574e9f9e-f8… b290d7ef… b290d7… ""            <NA>             BPH556PrGA2_Fco…    25915 prostate                         718      342. 
#>  7       4 MK~^fbPVCl         574e9f9e-f8… b290d7ef… b290d7… ""            <NA>             BPH556PrGA2_Fco…    25915 prostate                         846       99.8
#>  8       4 J+o&MJmtR5         574e9f9e-f8… b290d7ef… b290d7… ""            <NA>             BPH556PrGA2_Fco…    25915 prostate                         829      123. 
#>  9      12 $LL!IWeW`F         574e9f9e-f8… b290d7ef… b290d7… ""            <NA>             BPH556PrGA2_Fco…    25915 prostate                        2482       61.8
#> 10      18 $zB;$PErEP         574e9f9e-f8… b290d7ef… b290d7… ""            <NA>             BPH556PrGA2_Fco…    25915 prostate                         828       86.6
#> # ℹ more rows
#> # ℹ abbreviated name: ¹nFeature_expressed_in_sample
#> # ℹ 25 more variables: 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>

Quality control

cellNexus 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()

nfeatures_df <- cellNexus:::get_cellxgene_metadata("dataset") |>
  dplyr::select(dplyr::where(~ !is.list(.x)))
  
metadata <- metadata |>
  dplyr::left_join(nfeatures_df,
                   by = "dataset_id",
                   copy = TRUE) |>
  dplyr::filter(feature_count >= 5000)

Join Census metadata

Original Census annotations can be retrieved by the function get_census_metadata(), and registered to lazy tibble format by DuckDB

census_metadata <- cellNexus:::get_census_metadata("2024-07-01")
#> ℹ Opening Census version 2024-07-01.
#> ℹ Reading Census obs table.

con <- dbplyr::remote_con(metadata)

duckdb::duckdb_register_arrow(con, "census_metadata", census_metadata)

metadata <- metadata |>
  dplyr::left_join(tbl(con, "census_metadata") |> 
                   dplyr::select(observation_joinid, dataset_id, tissue,
                                 self_reported_ethnicity, cell_type, assay,
                                 disease, sex))
#> Joining with `by = join_by(observation_joinid, dataset_id)`

Explore tissues

metadata |>
  dplyr::distinct(tissue, cell_type_unified_ensemble)
#> # Source:   SQL [?? x 2]
#> # Database: DuckDB 1.4.3 [unknown@Linux 5.14.0-570.123.1.el9_6.x86_64:R 4.5.3/:memory:]
#>    tissue                      cell_type_unified_ensemble
#>    <chr>                       <chr>                     
#>  1 subcutaneous adipose tissue nkt                       
#>  2 subcutaneous adipose tissue muscle                    
#>  3 subcutaneous adipose tissue macrophage                
#>  4 subcutaneous adipose tissue cd8 tem                   
#>  5 subcutaneous adipose tissue cd16 mono                 
#>  6 subcutaneous adipose tissue cd14 mono                 
#>  7 subcutaneous adipose tissue b naive                   
#>  8 subcutaneous adipose tissue cd4 th1/th17 em           
#>  9 subcutaneous adipose tissue granulocyte               
#> 10 subcutaneous adipose tissue cd4 naive                 
#> # ℹ more rows

Download single-cell RNA sequencing counts

Query raw counts

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.
#> 
Reading counts ■■■■                              10% | ETA: 11s

Reading counts ■■■■■■■                           20% | ETA:  7s

Reading counts ■■■■■■■■■■                        30% | ETA:  6s

Reading counts ■■■■■■■■■■■■■                     40% | ETA:  5s

Reading counts ■■■■■■■■■■■■■■■■                  50% | ETA:  4s

Reading counts ■■■■■■■■■■■■■■■■■■■               60% | ETA:  3s

Reading counts ■■■■■■■■■■■■■■■■■■■■■■            70% | ETA:  2s

Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■         80% | ETA:  1s

Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■■■■      90% | ETA:  1s

                                                                
ℹ Compiling Experiment.

single_cell_counts
#> # A SingleCellExperiment-tibble abstraction: 2,806 × 60
#> # [90mFeatures=33145 | Cells=2806 | 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 nCount_RNA
#>    <chr> <chr>              <chr>          <chr>     <chr>   <chr>         <chr>            <chr>               <int> <chr>                          <int>      <dbl>
#>  1 80_1  zz-!e5_XAo         842c6f5d-4a94… 1de3f3ba… 1de3f3… ""            <NA>             7fabaf1c-52fd-4…    14600 breast                          1749      10.8 
#>  2 81_1  -mb&DWckf(         842c6f5d-4a94… 1de3f3ba… 1de3f3… ""            <NA>             7fabaf1c-52fd-4…    14600 breast                          1993      12.4 
#>  3 73_1  z_=CTOs4{z         842c6f5d-4a94… 4b5e66fa… 4b5e66… ""            <NA>             04983012-bb56-4…    14600 breast                          2866      10.3 
#>  4 74_1  fNzorxA`Mf         842c6f5d-4a94… 4b5e66fa… 4b5e66… ""            <NA>             04983012-bb56-4…    14600 breast                          1942       7.58
#>  5 76_1  bTlx!HK=oS         842c6f5d-4a94… 52ab9222… 52ab92… ""            <NA>             7ce86149-8906-4…    14600 breast                          1671       9.65
#>  6 77_1  E4g5+)v;AV         842c6f5d-4a94… 52ab9222… 52ab92… ""            <NA>             7ce86149-8906-4…    14600 breast                          2340      11.9 
#>  7 78_1  +q?29B%2nH         842c6f5d-4a94… 52ab9222… 52ab92… ""            <NA>             7ce86149-8906-4…    14600 breast                          1714      13.2 
#>  8 79_1  zuJ#MBMWy;         842c6f5d-4a94… 52ab9222… 52ab92… ""            <NA>             7ce86149-8906-4…    14600 breast                          1506      12.3 
#>  9 1_1   I8a42<8st4         842c6f5d-4a94… 184fa234… 184fa2… ""            <NA>             c2aa4d8d-e9df-4…    14600 breast                          3395      11.8 
#> 10 72_1  8wGs7JgUjj         842c6f5d-4a94… 6b194412… 6b1944… ""            <NA>             b3ff1aad-40fd-4…    14600 breast                          2548      13.1 
#> # ℹ 2,796 more rows
#> # ℹ abbreviated name: ¹nFeature_expressed_in_sample
#> # ℹ 48 more variables: 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>, dataset_version_id <chr>, collection_id <chr>, …

Query counts scaled per million

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.
#> 
Reading cpm ■■■■■■■                           20% | ETA:  5s

Reading cpm ■■■■■■■■■■                        30% | ETA:  4s

Reading cpm ■■■■■■■■■■■■■                     40% | ETA:  4s

Reading cpm ■■■■■■■■■■■■■■■■                  50% | ETA:  3s

Reading cpm ■■■■■■■■■■■■■■■■■■■               60% | ETA:  3s

Reading cpm ■■■■■■■■■■■■■■■■■■■■■■            70% | ETA:  2s

Reading cpm ■■■■■■■■■■■■■■■■■■■■■■■■■         80% | ETA:  1s

Reading cpm ■■■■■■■■■■■■■■■■■■■■■■■■■■■■      90% | ETA:  1s

                                                             
ℹ Compiling Experiment.

single_cell_cpm
#> # A SingleCellExperiment-tibble abstraction: 2,806 × 60
#> # [90mFeatures=33145 | Cells=2806 | 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 nCount_RNA
#>    <chr> <chr>              <chr>          <chr>     <chr>   <chr>         <chr>            <chr>               <int> <chr>                          <int>      <dbl>
#>  1 80_1  zz-!e5_XAo         842c6f5d-4a94… 1de3f3ba… 1de3f3… ""            <NA>             7fabaf1c-52fd-4…    14600 breast                          1749      10.8 
#>  2 81_1  -mb&DWckf(         842c6f5d-4a94… 1de3f3ba… 1de3f3… ""            <NA>             7fabaf1c-52fd-4…    14600 breast                          1993      12.4 
#>  3 73_1  z_=CTOs4{z         842c6f5d-4a94… 4b5e66fa… 4b5e66… ""            <NA>             04983012-bb56-4…    14600 breast                          2866      10.3 
#>  4 74_1  fNzorxA`Mf         842c6f5d-4a94… 4b5e66fa… 4b5e66… ""            <NA>             04983012-bb56-4…    14600 breast                          1942       7.58
#>  5 76_1  bTlx!HK=oS         842c6f5d-4a94… 52ab9222… 52ab92… ""            <NA>             7ce86149-8906-4…    14600 breast                          1671       9.65
#>  6 77_1  E4g5+)v;AV         842c6f5d-4a94… 52ab9222… 52ab92… ""            <NA>             7ce86149-8906-4…    14600 breast                          2340      11.9 
#>  7 78_1  +q?29B%2nH         842c6f5d-4a94… 52ab9222… 52ab92… ""            <NA>             7ce86149-8906-4…    14600 breast                          1714      13.2 
#>  8 79_1  zuJ#MBMWy;         842c6f5d-4a94… 52ab9222… 52ab92… ""            <NA>             7ce86149-8906-4…    14600 breast                          1506      12.3 
#>  9 1_1   I8a42<8st4         842c6f5d-4a94… 184fa234… 184fa2… ""            <NA>             c2aa4d8d-e9df-4…    14600 breast                          3395      11.8 
#> 10 72_1  8wGs7JgUjj         842c6f5d-4a94… 6b194412… 6b1944… ""            <NA>             b3ff1aad-40fd-4…    14600 breast                          2548      13.1 
#> # ℹ 2,796 more rows
#> # ℹ abbreviated name: ¹nFeature_expressed_in_sample
#> # ℹ 48 more variables: 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>, dataset_version_id <chr>, collection_id <chr>, …

Query SCT normalised counts

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.
#> ! 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 ■■■■■■■                           20% | ETA:  5s

Reading sct ■■■■■■■■■■                        30% | ETA:  4s

Reading sct ■■■■■■■■■■■■■                     40% | 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 ■■■■■■■■■■■■■                     40% | ETA:  4s

Reading sct ■■■■■■■■■■■■■■■■                  50% | ETA:  3s

                                                             
! 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:  3s

Reading sct ■■■■■■■■■■■■■■■■■■■               60% | ETA:  3s

Reading sct ■■■■■■■■■■■■■■■■■■■■■■            70% | ETA:  2s

                                                             
! 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:  2s

Reading sct ■■■■■■■■■■■■■■■■■■■■■■■■■         80% | ETA:  1s

Reading sct ■■■■■■■■■■■■■■■■■■■■■■■■■■■■      90% | ETA:  1s

                                                             
! cellNexus says: 1613 cell(s) from your metadata are absent from the SCT assay across 4 file(s). This is expected: SCT normalisation is run per sample and may fail for samples with very few cells or extreme count distributions. The returned object contains only cells from samples where SCT succeeded. Affected sample_id(s): 52ab92226337d36c306466eefe67f9c1, 765554078ca8d1eaf2712000c0df0d6f, 8940e0767e7eca1b72d37b4138be2276, a79912cb9aaa8d8c0b1a3cdcc9294f8c, 5e641a2218d1d8b91f638989626c89e0.
#> ℹ Compiling Experiment.

single_cell_sct
#> # A SingleCellExperiment-tibble abstraction: 1,193 × 60
#> # [90mFeatures=33145 | Cells=1193 | 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 nCount_RNA
#>    <chr> <chr>              <chr>          <chr>     <chr>   <chr>         <chr>            <chr>               <int> <chr>                          <int>      <dbl>
#>  1 80_1  zz-!e5_XAo         842c6f5d-4a94… 1de3f3ba… 1de3f3… ""            <NA>             7fabaf1c-52fd-4…    14600 breast                          1749      10.8 
#>  2 81_1  -mb&DWckf(         842c6f5d-4a94… 1de3f3ba… 1de3f3… ""            <NA>             7fabaf1c-52fd-4…    14600 breast                          1993      12.4 
#>  3 73_1  z_=CTOs4{z         842c6f5d-4a94… 4b5e66fa… 4b5e66… ""            <NA>             04983012-bb56-4…    14600 breast                          2866      10.3 
#>  4 74_1  fNzorxA`Mf         842c6f5d-4a94… 4b5e66fa… 4b5e66… ""            <NA>             04983012-bb56-4…    14600 breast                          1942       7.58
#>  5 1_1   I8a42<8st4         842c6f5d-4a94… 184fa234… 184fa2… ""            <NA>             c2aa4d8d-e9df-4…    14600 breast                          3395      11.8 
#>  6 72_1  8wGs7JgUjj         842c6f5d-4a94… 6b194412… 6b1944… ""            <NA>             b3ff1aad-40fd-4…    14600 breast                          2548      13.1 
#>  7 75_1  F9G7A+GgjA         842c6f5d-4a94… db5a69ed… db5a69… ""            <NA>             49beb83c-66a1-4…    14600 breast                          1291      10.2 
#>  8 1_2   >8f0}-gXFY         842c6f5d-4a94… 81d05f17… 81d05f… ""            <NA>             b866c1d4-3dfd-4…    14600 breast                          2513      13.3 
#>  9 22_2  2lQ`<&l3-A         842c6f5d-4a94… 30967738… 309677… ""            <NA>             7d4045ff-3f48-4…    14600 breast                          2058       9.91
#> 10 5_2   +p4uNj_7$S         842c6f5d-4a94… a91e6814… a91e68… ""            <NA>             700a819c-03f9-4…    14600 breast                          1870      11.1 
#> # ℹ 1,183 more rows
#> # ℹ abbreviated name: ¹nFeature_expressed_in_sample
#> # ℹ 48 more variables: 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>, dataset_version_id <chr>, collection_id <chr>, …

Query pseudobulk

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.
#> 
Reading counts ■■■■■                             14% | ETA:  8s

Reading counts ■■■■■■■■■■                        29% | ETA: 11s

Reading counts ■■■■■■■■■■■■■■                    43% | ETA:  9s

Reading counts ■■■■■■■■■■■■■■■■■■                57% | ETA:  6s

Reading counts ■■■■■■■■■■■■■■■■■■■■■■            71% | ETA:  4s

Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■■■       86% | ETA:  2s

                                                                
! 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 × 43
#> # [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 cell_chunk
#>    <chr>           <chr>      <chr>     <chr>   <chr>         <chr>            <chr>               <int> <chr>         <chr>                         <int>      <int>
#>  1 2e8c9911c9bfbf… 0ba16f4b-… 2e8c9911… 2e8c99… ""            <NA>             HDBR15279,HDBR1…       NA respiratory … cd14 mono                         1          1
#>  2 0d874636bc714a… 1e6a6ef9-… 0d874636… 0d8746… ""            <NA>             Leader_Merad_20…    29930 respiratory … monocytic                         1          5
#>  3 0d874636bc714a… 1e6a6ef9-… 0d874636… 0d8746… ""            <NA>             Leader_Merad_20…    29930 respiratory … cd14 mono                         1          5
#>  4 0d874636bc714a… 1e6a6ef9-… 0d874636… 0d8746… ""            <NA>             Leader_Merad_20…    29930 respiratory … cd16 mono                         1          5
#>  5 0d874636bc714a… 1e6a6ef9-… 0d874636… 0d8746… ""            <NA>             Leader_Merad_20…    29930 respiratory … macrophage                        1          5
#>  6 0d874636bc714a… 1e6a6ef9-… 0d874636… 0d8746… ""            <NA>             Leader_Merad_20…    29930 respiratory … other                             1          5
#>  7 11721339cb1dfc… 1e6a6ef9-… 11721339… 117213… ""            <NA>             Leader_Merad_20…    26645 respiratory … monocytic                         1          7
#>  8 11721339cb1dfc… 1e6a6ef9-… 11721339… 117213… ""            <NA>             Leader_Merad_20…    26645 respiratory … cd14 mono                         1          7
#>  9 f71af64a552d45… 1e6a6ef9-… f71af64a… f71af6… ""            <NA>             Leader_Merad_20…    27010 respiratory … monocytic                         1          6
#> 10 f71af64a552d45… 1e6a6ef9-… f71af64a… f71af6… ""            <NA>             Leader_Merad_20…    27010 respiratory … cd14 mono                         1          6
#> # ℹ 129 more rows
#> # ℹ abbreviated name: ¹cell_type_unified_ensemble
#> # ℹ 31 more variables: 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>,
#> #   dataset_version_id <chr>, collection_id <chr>, cell_count <int>, citation <chr>, default_embedding <chr>, explorer_url <chr>, feature_count <int>,
#> #   mean_genes_per_cell <dbl>, primary_cell_count <int>, schema_version <chr>, title <chr>, tombstone <lgl>, x_approximate_distribution <chr>, published_at <date>,
#> #   revised_at <date>, tissue <chr>, self_reported_ethnicity <chr>, assay <chr>, disease <chr>, sex <chr>, sample_identifier <chr>

Download cell communication metadata

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 5.14.0-570.123.1.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 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+TGFBR2) TGFb         Secreted … KEGG: h…     0.000420        1     b290d7ef…
#> 2 b memory  b      TGFB1  TGFbR1_R2 0.000865    1    TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR2) TGFb         Secreted … KEGG: h…     0.00185         1     b290d7ef…
#> 3 b naive   b      TGFB1  TGFbR1_R2 0.000696    0.99 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR2) 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+TGFBR2) TGFb         Secreted … KEGG: h…     0.00472         0.924 b290d7ef…
#> 5 cd4 naive b      TGFB1  TGFbR1_R2 0.000957    1    TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR2) 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+TGFBR2) TGFb         Secreted … KEGG: h…     0.00467         0.797 b290d7ef…
#> # ℹ 2 more variables: interaction_count <dbl>, interaction_weight <dbl>

Extract only a subset of genes

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 ■■■■■■■                           20% | ETA:  5s

Reading cpm ■■■■■■■■■■                        30% | ETA:  4s

Reading cpm ■■■■■■■■■■■■■                     40% | ETA:  4s

Reading cpm ■■■■■■■■■■■■■■■■                  50% | ETA:  3s

Reading cpm ■■■■■■■■■■■■■■■■■■■               60% | ETA:  3s

Reading cpm ■■■■■■■■■■■■■■■■■■■■■■            70% | ETA:  2s

Reading cpm ■■■■■■■■■■■■■■■■■■■■■■■■■         80% | ETA:  1s

Reading cpm ■■■■■■■■■■■■■■■■■■■■■■■■■■■■      90% | ETA:  1s

                                                             
ℹ Compiling Experiment.

single_cell_cpm
#> # A SingleCellExperiment-tibble abstraction: 2,806 × 60
#> # [90mFeatures=1 | Cells=2806 | 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 nCount_RNA
#>    <chr> <chr>              <chr>          <chr>     <chr>   <chr>         <chr>            <chr>               <int> <chr>                          <int>      <dbl>
#>  1 80_1  zz-!e5_XAo         842c6f5d-4a94… 1de3f3ba… 1de3f3… ""            <NA>             7fabaf1c-52fd-4…    14600 breast                          1749      10.8 
#>  2 81_1  -mb&DWckf(         842c6f5d-4a94… 1de3f3ba… 1de3f3… ""            <NA>             7fabaf1c-52fd-4…    14600 breast                          1993      12.4 
#>  3 72_1  8wGs7JgUjj         842c6f5d-4a94… 6b194412… 6b1944… ""            <NA>             b3ff1aad-40fd-4…    14600 breast                          2548      13.1 
#>  4 75_1  F9G7A+GgjA         842c6f5d-4a94… db5a69ed… db5a69… ""            <NA>             49beb83c-66a1-4…    14600 breast                          1291      10.2 
#>  5 73_1  z_=CTOs4{z         842c6f5d-4a94… 4b5e66fa… 4b5e66… ""            <NA>             04983012-bb56-4…    14600 breast                          2866      10.3 
#>  6 74_1  fNzorxA`Mf         842c6f5d-4a94… 4b5e66fa… 4b5e66… ""            <NA>             04983012-bb56-4…    14600 breast                          1942       7.58
#>  7 76_1  bTlx!HK=oS         842c6f5d-4a94… 52ab9222… 52ab92… ""            <NA>             7ce86149-8906-4…    14600 breast                          1671       9.65
#>  8 77_1  E4g5+)v;AV         842c6f5d-4a94… 52ab9222… 52ab92… ""            <NA>             7ce86149-8906-4…    14600 breast                          2340      11.9 
#>  9 78_1  +q?29B%2nH         842c6f5d-4a94… 52ab9222… 52ab92… ""            <NA>             7ce86149-8906-4…    14600 breast                          1714      13.2 
#> 10 79_1  zuJ#MBMWy;         842c6f5d-4a94… 52ab9222… 52ab92… ""            <NA>             7ce86149-8906-4…    14600 breast                          1506      12.3 
#> # ℹ 2,796 more rows
#> # ℹ abbreviated name: ¹nFeature_expressed_in_sample
#> # ℹ 48 more variables: 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>, dataset_version_id <chr>, collection_id <chr>, …

Extract the counts as a Seurat object

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:  5s

Reading counts ■■■■■■■■■■                        30% | ETA:  5s

Reading counts ■■■■■■■■■■■■■                     40% | ETA:  4s

Reading counts ■■■■■■■■■■■■■■■■                  50% | ETA:  3s

Reading counts ■■■■■■■■■■■■■■■■■■■               60% | ETA:  3s

Reading counts ■■■■■■■■■■■■■■■■■■■■■■            70% | ETA:  3s

Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■         80% | ETA:  2s

Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■■■■      90% | ETA:  1s

                                                                
ℹ Compiling Experiment.

seurat_counts
#> An object of class Seurat 
#> 33145 features across 2806 samples within 1 assay 
#> Active assay: originalexp (33145 features, 0 variable features)
#>  2 layers present: counts, data

By default, data is downloaded to get_default_cache_dir() output. If memory is a concern, users can specify a custom 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().

Save your `SingleCellExperiment`

The returned SingleCellExperiment can be saved with three modalities, as .rds or as HDF5 or as H5AD.

Saving as RDS (fast saving, slow reading)

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 (slow saving, fast reading)

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 (slow saving, fast reading)

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
  )

Visualise gene transcription

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 of chunk plot-fcn1-disease

# 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.

plot of chunk plot-fcn1-tissue

Integrate cloud and local metadata

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 rows

Cell metadata

The complete metadata dictionary for the harmonised fields is available on the documentation site: cellNexus documentation.

RNA abundance

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.

Other representations

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.

Session Info

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] stats4    stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#>  [1] BiocStyle_2.38.0            RcppSpdlog_0.0.28           ggplot2_4.0.2               SummarizedExperiment_1.40.0 Biobase_2.70.0             
#>  [6] GenomicRanges_1.62.1        Seqinfo_1.0.0               IRanges_2.44.0              S4Vectors_0.49.1-1          BiocGenerics_0.56.0        
#> [11] generics_0.1.4              MatrixGenerics_1.22.0       matrixStats_1.5.0           shiny_1.13.0                anndataR_1.0.2             
#> [16] cellNexus_0.99.27           testthat_3.3.2              dplyr_1.2.1                
#> 
#> loaded via a namespace (and not attached):
#>   [1] fs_2.0.1                        spatstat.sparse_3.1-0           fontawesome_0.5.3               devtools_2.5.0                  httr_1.4.8                     
#>   [6] RColorBrewer_1.1-3              tools_4.5.3                     sctransform_0.4.3               backports_1.5.1                 utf8_1.2.6                     
#>  [11] R6_2.6.1                        DT_0.34.0                       HDF5Array_1.38.0                lazyeval_0.2.3                  uwot_0.2.4                     
#>  [16] rhdf5filters_1.22.0             withr_3.0.2                     sp_2.2-1                        gridExtra_2.3                   nanoarrow_0.8.0                
#>  [21] progressr_0.19.0                cli_3.6.6                       spatstat.explore_3.8-0          fastDummies_1.7.5               sass_0.4.10                    
#>  [26] Seurat_5.5.0.9002               arrow_23.0.1.2                  S7_0.2.1-1                      spatstat.data_3.1-9             ggridges_0.5.7                 
#>  [31] pbapply_1.7-4                   commonmark_2.0.0                R.utils_2.13.0                  parallelly_1.46.1               sessioninfo_1.2.3              
#>  [36] rstudioapi_0.18.0               ica_1.0-3                       spatstat.random_3.4-5           Matrix_1.7-4                    fansi_1.0.7                    
#>  [41] waldo_0.6.2                     rclipboard_0.2.1                abind_1.4-8                     R.methodsS3_1.8.2               lifecycle_1.0.5                
#>  [46] yaml_2.3.12                     rhdf5_2.54.1                    SparseArray_1.10.10             Rtsne_0.17                      grid_4.5.3                     
#>  [51] blob_1.3.0                      promises_1.5.0                  dir.expiry_1.18.0               miniUI_0.1.2                    lattice_0.22-9                 
#>  [56] cowplot_1.2.0                   pillar_1.11.1                   knitr_1.51                      future.apply_1.20.2             codetools_0.2-20               
#>  [61] glue_1.8.0                      tiledb_0.33.1                   spatstat.univar_3.1-7           data.table_1.18.2.1             tidySingleCellExperiment_1.20.1
#>  [66] vctrs_0.7.3                     png_0.1-9                       spam_2.11-3                     gtable_0.3.6                    aws.s3_0.3.22                  
#>  [71] assertthat_0.2.1                cachem_1.1.0                    xfun_0.57                       S4Arrays_1.10.1                 mime_0.13                      
#>  [76] rsconnect_1.8.0                 survival_3.8-6                  SingleCellExperiment_1.32.0     ellipsis_0.3.3                  fitdistrplus_1.2-6             
#>  [81] ROCR_1.0-12                     tiledbsoma_2.1.2                nlme_3.1-168                    RcppCCTZ_0.2.14                 usethis_3.2.1                  
#>  [86] bit64_4.6.0-1                   filelock_1.0.3                  RcppAnnoy_0.0.23                GenomeInfoDb_1.46.2             rprojroot_2.1.1                
#>  [91] R.cache_0.17.0                  bslib_0.10.0                    irlba_2.3.7                     KernSmooth_2.23-26              otel_0.2.0                     
#>  [96] DBI_1.3.0                       zellkonverter_1.20.1            duckdb_1.4.3                    tidyselect_1.2.1                cellxgene.census_1.16.1        
#> [101] bit_4.6.0                       compiler_4.5.3                  curl_7.0.0                      rjsoncons_1.3.2                 h5mread_1.2.1                  
#> [106] xml2_1.5.2                      nanotime_0.3.13                 desc_1.4.3                      DelayedArray_0.36.1             plotly_4.12.0                  
#> [111] bookdown_0.46                   checkmate_2.3.4                 scales_1.4.0                    lmtest_0.9-40                   spdl_0.0.5                     
#> [116] stringr_1.6.0                   digest_0.6.39                   goftest_1.2-3                   spatstat.utils_3.2-2            rmarkdown_2.31                 
#> [121] basilisk_1.22.0                 XVector_0.50.0                  htmltools_0.5.9                 pkgconfig_2.0.3                 base64enc_0.1-6                
#> [126] dbplyr_2.5.2                    fastmap_1.2.0                   rlang_1.2.0                     htmlwidgets_1.6.4               UCSC.utils_1.6.1               
#> [131] farver_2.1.2                    jquerylib_0.1.4                 zoo_1.8-15                      jsonlite_2.0.0                  R.oo_1.27.1                    
#> [136] magrittr_2.0.5                  dotCall64_1.2                   patchwork_1.3.2                 Rhdf5lib_1.32.0                 Rcpp_1.1.1-1                   
#> [141] reticulate_1.46.0               stringi_1.8.7                   brio_1.1.5                      MASS_7.3-65                     plyr_1.8.9                     
#> [146] pkgbuild_1.4.8                  parallel_4.5.3                  listenv_0.10.1                  ggrepel_0.9.8                   forcats_1.0.1                  
#> [151] deldir_2.0-4                    splines_4.5.3                   tensor_1.5.1                    igraph_2.2.3                    cellxgenedp_1.14.0             
#> [156] spatstat.geom_3.7-3             RcppHNSW_0.6.0                  reshape2_1.4.5                  pkgload_1.5.1                   ttservice_0.5.3                
#> [161] evaluate_1.0.5                  SeuratObject_5.4.0              BiocManager_1.30.27             httpuv_1.6.17                   RANN_2.6.2                     
#> [166] tidyr_1.3.2                     purrr_1.2.2                     polyclip_1.10-7                 future_1.70.0                   scattermore_1.2                
#> [171] xtable_1.8-8                    RSpectra_0.16-2                 roxygen2_7.3.3                  later_1.4.8                     viridisLite_0.4.3              
#> [176] tibble_3.3.1                    memoise_2.0.1                   aws.signature_0.6.0             cluster_2.1.8.2                 shinyWidgets_0.9.1             
#> [181] globals_0.19.1

Name		Name	Last commit message	Last commit date
Latest commit History 973 Commits
.github		.github
R		R
data		data
inst		inst
man		man
pkgdown		pkgdown
tests		tests
vignettes		vignettes
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
ATLAS_VERSIONS.md		ATLAS_VERSIONS.md
DESCRIPTION		DESCRIPTION
LICENSE.md		LICENSE.md
NAMESPACE		NAMESPACE
README.md		README.md
_pkgdown.yml		_pkgdown.yml
index.md		index.md

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cellNexus

Introduction

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R API: here

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Article code: here

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Load the package

Load additional packages

Load and explore the metadata

Load the metadata

Quality control

Join Census metadata

Explore tissues

Download single-cell RNA sequencing counts

Query raw counts

Query counts scaled per million

Query SCT normalised counts

Query pseudobulk

Download cell communication metadata

Extract only a subset of genes

Extract the counts as a Seurat object

Save your SingleCellExperiment

Saving as RDS (fast saving, slow reading)

Saving as HDF5 (slow saving, fast reading)

Saving as H5AD (slow saving, fast reading)

Visualise gene transcription

Integrate cloud and local metadata

Cell metadata

RNA abundance

Other representations

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