CuratedAtlasQueryR

Lifecycle:maturing

CuratedAtlasQuery is a query interface that allow the programmatic exploration and retrieval of the harmonised, curated and reannotated CELLxGENE single-cell human cell atlas. Data can be retrieved at cell, sample, or dataset levels based on filtering criteria.

Harmonised data is stored in the ARDC Nectar Research Cloud, and most CuratedAtlasQuery functions interact with Nectar via web requests, so a network connection is required for most functionality.

Query interface

Installation

devtools::install_github("stemangiola/CuratedAtlasQueryR")

Load the package

library(CuratedAtlasQueryR)

Load and explore the metadata

Load the metadata

# Note: in real applications you should use the default value of remote_url 
metadata <- get_metadata(remote_url = METADATA_URL)
metadata
#> # Source:   SQL [?? x 56]
#> # Database: DuckDB v0.9.2 [biocbuild@Linux 5.15.0-88-generic:R 4.4.0/:memory:]
#>    cell_               sample_   cell_type cell_type_harmonised confidence_class
#>    <chr>               <chr>     <chr>     <chr>                           <dbl>
#>  1 TTATGCTAGGGTGTTG_12 039c558c… mature N… immune_unclassified                 5
#>  2 GCTTGAACATGGTCTA_12 039c558c… mature N… cd8 tem                             3
#>  3 GTCATTTGTGCACCAC_12 039c558c… mature N… immune_unclassified                 5
#>  4 AAGGAGCGTATTCTCT_12 039c558c… mature N… immune_unclassified                 5
#>  5 ATTGGACTCCGTACAA_12 039c558c… mature N… immune_unclassified                 5
#>  6 CTCGTCAAGTACGTTC_12 039c558c… mature N… immune_unclassified                 5
#>  7 CGTCTACTCTCTAAGG_11 07e64957… mature N… immune_unclassified                 5
#>  8 ACGATCAAGGTGGTTG_15 17640030… mature N… immune_unclassified                 5
#>  9 TCATACTTCCGCACGA_15 17640030… mature N… immune_unclassified                 5
#> 10 GAGACCCTCCCTTCCC_15 17640030… mature N… immune_unclassified                 5
#> # ℹ more rows
#> # ℹ 51 more variables: cell_annotation_azimuth_l2 <chr>,
#> #   cell_annotation_blueprint_singler <chr>,
#> #   cell_annotation_monaco_singler <chr>, sample_id_db <chr>,
#> #   `_sample_name` <chr>, assay <chr>, assay_ontology_term_id <chr>,
#> #   file_id_db <chr>, cell_type_ontology_term_id <chr>,
#> #   development_stage <chr>, development_stage_ontology_term_id <chr>, …

The metadata variable can then be re-used for all subsequent queries.

Explore the tissue

metadata |>
    dplyr::distinct(tissue, file_id) 
#> # Source:   SQL [?? x 2]
#> # Database: DuckDB v0.9.2 [biocbuild@Linux 5.15.0-88-generic:R 4.4.0/:memory:]
#>    tissue                file_id                             
#>    <chr>                 <chr>                               
#>  1 heart left ventricle  5775c8d8-e37e-40cd-94f4-8e78b05ca331
#>  2 kidney                5ef8b993-4a02-42ee-9202-a595f6e9a758
#>  3 thymus                5c1cc788-2645-45fb-b1d9-2f43d368bba8
#>  4 respiratory airway    fe1bbb3e-8c3b-4dfd-ae20-9d288b8a7699
#>  5 blood                 79d07078-90fd-43c3-b705-46c9b4d9d8d3
#>  6 mesenteric lymph node 59dfc135-19c1-4380-a9e8-958908273756
#>  7 kidney blood vessel   f7e94dbb-8638-4616-aaf9-16e2212c369f
#>  8 kidney blood vessel   8fee7b82-178b-4c04-bf23-04689415690d
#>  9 respiratory airway    6661ab3a-792a-4682-b58c-4afb98b2c016
#> 10 retina                94039710-0387-40e1-9667-dbbac4c469c1
#> # ℹ more rows

Download single-cell RNA sequencing counts

Query raw counts

single_cell_counts = 
    metadata |>
    dplyr::filter(
        ethnicity == "African" &
        stringr::str_like(assay, "%10x%") &
        tissue == "lung parenchyma" &
        stringr::str_like(cell_type, "%CD4%")
    ) |>
    get_single_cell_experiment()
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Downloading 0 files, totalling 0 GB
#> ℹ Reading files.
#> ℹ Compiling Single Cell Experiment.

single_cell_counts
#> class: SingleCellExperiment 
#> dim: 36229 1571 
#> metadata(0):
#> assays(1): counts
#> rownames(36229): A1BG A1BG-AS1 ... ZZEF1 ZZZ3
#> rowData names(0):
#> colnames(1571): ACACCAAAGCCACCTG_SC18_1 TCAGCTCCAGACAAGC_SC18_1 ...
#>   CAGCATAAGCTAACAA_F02607_1 AAGGAGCGTATAATGG_F02607_1
#> colData names(56): sample_ cell_type ... updated_at_y original_cell_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):

Query counts scaled per million

This is helpful if just few genes are of interest, as they can be compared across samples.

single_cell_counts = 
    metadata |>
    dplyr::filter(
        ethnicity == "African" &
        stringr::str_like(assay, "%10x%") &
        tissue == "lung parenchyma" &
        stringr::str_like(cell_type, "%CD4%")
    ) |>
    get_single_cell_experiment(assays = "cpm")
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Downloading 0 files, totalling 0 GB
#> ℹ Reading files.
#> ℹ Compiling Single Cell Experiment.

single_cell_counts
#> class: SingleCellExperiment 
#> dim: 36229 1571 
#> metadata(0):
#> assays(1): cpm
#> rownames(36229): A1BG A1BG-AS1 ... ZZEF1 ZZZ3
#> rowData names(0):
#> colnames(1571): ACACCAAAGCCACCTG_SC18_1 TCAGCTCCAGACAAGC_SC18_1 ...
#>   CAGCATAAGCTAACAA_F02607_1 AAGGAGCGTATAATGG_F02607_1
#> colData names(56): sample_ cell_type ... updated_at_y original_cell_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):

Extract only a subset of genes

single_cell_counts = 
    metadata |>
    dplyr::filter(
        ethnicity == "African" &
        stringr::str_like(assay, "%10x%") &
        tissue == "lung parenchyma" &
        stringr::str_like(cell_type, "%CD4%")
    ) |>
    get_single_cell_experiment(assays = "cpm", features = "PUM1")
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Downloading 0 files, totalling 0 GB
#> ℹ Reading files.
#> ℹ Compiling Single Cell Experiment.

single_cell_counts
#> class: SingleCellExperiment 
#> dim: 1 1571 
#> metadata(0):
#> assays(1): cpm
#> rownames(1): PUM1
#> rowData names(0):
#> colnames(1571): ACACCAAAGCCACCTG_SC18_1 TCAGCTCCAGACAAGC_SC18_1 ...
#>   CAGCATAAGCTAACAA_F02607_1 AAGGAGCGTATAATGG_F02607_1
#> colData names(56): sample_ cell_type ... updated_at_y original_cell_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):

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.

single_cell_counts_seurat = 
    metadata |>
    dplyr::filter(
        ethnicity == "African" &
        stringr::str_like(assay, "%10x%") &
        tissue == "lung parenchyma" &
        stringr::str_like(cell_type, "%CD4%")
    ) |>
    get_seurat()
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Downloading 0 files, totalling 0 GB
#> ℹ Reading files.
#> ℹ Compiling Single Cell Experiment.

single_cell_counts_seurat
#> An object of class Seurat 
#> 36229 features across 1571 samples within 1 assay 
#> Active assay: originalexp (36229 features, 0 variable features)
#>  2 layers present: counts, data

Save your SingleCellExperiment

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

Saving as RDS (fast saving, slow reading)

Saving as .rds has the advantage of being fast, andd 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.

single_cell_counts |> HDF5Array::saveHDF5SummarizedExperiment("single_cell_counts", replace = TRUE)

Visualise gene transcription

We can gather all CD14 monocytes cells and plot the distribution of HLA-A across all tissues

suppressPackageStartupMessages({
    library(ggplot2)
})

# Plots with styling
counts <- metadata |>
  # Filter and subset
  dplyr::filter(cell_type_harmonised == "cd14 mono") |>
  dplyr::filter(file_id_db != "c5a05f23f9784a3be3bfa651198a48eb") |> 
  
  # Get counts per million for HCA-A gene
  get_single_cell_experiment(assays = "cpm", features = "HLA-A") |> 
  suppressMessages() |>
  
  # Add feature to table
  tidySingleCellExperiment::join_features("HLA-A", shape = "wide") |> 
    
  # Rank x axis
  tibble::as_tibble()

# Plot by disease
counts |>
  dplyr::with_groups(disease, ~ .x |> dplyr::mutate(median_count = median(`HLA.A`, rm.na=TRUE))) |> 
  
  # Plot
  ggplot(aes(forcats::fct_reorder(disease, median_count,.desc = TRUE), `HLA.A`,color = file_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("HLA-A in CD14 monocytes by disease") 
#> Warning: Transformation introduced infinite values in continuous y-axis

# Plot by tissue
counts |> 
  dplyr::with_groups(tissue_harmonised, ~ .x |> dplyr::mutate(median_count = median(`HLA.A`, rm.na=TRUE))) |> 
  
  # Plot
  ggplot(aes(forcats::fct_reorder(tissue_harmonised, median_count,.desc = TRUE), `HLA.A`,color = file_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("HLA-A in CD14 monocytes by tissue") + 
  theme(legend.position = "none")
#> Warning: Transformation introduced infinite values in continuous y-axis

metadata |> 
    
  # Filter and subset
  dplyr::filter(cell_type_harmonised=="nk") |> 

  # Get counts per million for HCA-A gene 
  get_single_cell_experiment(assays = "cpm", features = "HLA-A") |> 
  suppressMessages() |>

  # Plot
  tidySingleCellExperiment::join_features("HLA-A", shape = "wide") |> 
  ggplot(aes(tissue_harmonised, `HLA.A`, color = file_id)) +
  theme_bw() +
  theme(
      axis.text.x = element_text(angle = 60, vjust = 1, hjust = 1),
      legend.position = "none"
  ) + 
  geom_jitter(shape=".") + 
  xlab("Tissue") + 
  ggtitle("HLA-A in nk cells by tissue")

Obtain Unharmonised Metadata

Various metadata fields are not common between datasets, so it does not make sense for these to live in the main metadata table. However, we can obtain it using the get_unharmonised_metadata() function. This function returns a data frame with one row per dataset, including the unharmonised column which contains unharmnised metadata as a nested data frame.

harmonised <- metadata |> dplyr::filter(tissue == "kidney blood vessel")
unharmonised <- get_unharmonised_metadata(harmonised)
unharmonised
#> # A tibble: 4 × 2
#>   file_id                              unharmonised   
#>   <chr>                                <list>         
#> 1 63523aa3-0d04-4fc6-ac59-5cadd3e73a14 <tbl_dck_[,17]>
#> 2 8fee7b82-178b-4c04-bf23-04689415690d <tbl_dck_[,12]>
#> 3 dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 <tbl_dck_[,14]>
#> 4 f7e94dbb-8638-4616-aaf9-16e2212c369f <tbl_dck_[,14]>

Notice that the columns differ between each dataset’s data frame:

dplyr::pull(unharmonised) |> head(2)
#> [[1]]
#> # Source:   SQL [?? x 17]
#> # Database: DuckDB v0.9.2 [biocbuild@Linux 5.15.0-88-generic:R 4.4.0/:memory:]
#>    cell_        file_id donor_age donor_uuid library_uuid mapped_reference_ann…¹
#>    <chr>        <chr>   <chr>     <chr>      <chr>        <chr>                 
#>  1 4602STDY701… 63523a… 27 months a8536b6a-… 5ddaeac7-0f… GENCODE 24            
#>  2 4602STDY701… 63523a… 27 months a8536b6a-… 5ddaeac7-0f… GENCODE 24            
#>  3 4602STDY701… 63523a… 27 months a8536b6a-… 5ddaeac7-0f… GENCODE 24            
#>  4 4602STDY701… 63523a… 27 months a8536b6a-… 5ddaeac7-0f… GENCODE 24            
#>  5 4602STDY701… 63523a… 27 months a8536b6a-… 5ddaeac7-0f… GENCODE 24            
#>  6 4602STDY701… 63523a… 27 months a8536b6a-… 5ddaeac7-0f… GENCODE 24            
#>  7 4602STDY701… 63523a… 27 months a8536b6a-… 5ddaeac7-0f… GENCODE 24            
#>  8 4602STDY701… 63523a… 27 months a8536b6a-… 5ddaeac7-0f… GENCODE 24            
#>  9 4602STDY709… 63523a… 19 months 46318131-… 67178571-39… GENCODE 24            
#> 10 4602STDY701… 63523a… 27 months a8536b6a-… 5ddaeac7-0f… GENCODE 24            
#> # ℹ more rows
#> # ℹ abbreviated name: ¹​mapped_reference_annotation
#> # ℹ 11 more variables: sample_uuid <chr>, suspension_type <chr>,
#> #   suspension_uuid <chr>, author_cell_type <chr>, cell_state <chr>,
#> #   reported_diseases <chr>, Short_Sample <chr>, Project <chr>,
#> #   Experiment <chr>, compartment <chr>, broad_celltype <chr>
#> 
#> [[2]]
#> # Source:   SQL [?? x 12]
#> # Database: DuckDB v0.9.2 [biocbuild@Linux 5.15.0-88-generic:R 4.4.0/:memory:]
#>    cell_ file_id      orig.ident nCount_RNA nFeature_RNA seurat_clusters Project
#>    <chr> <chr>        <chr>           <dbl> <chr>        <chr>           <chr>  
#>  1 1069  8fee7b82-17… 4602STDY7…      16082 3997         25              Experi…
#>  2 1214  8fee7b82-17… 4602STDY7…       1037 606          25              Experi…
#>  3 2583  8fee7b82-17… 4602STDY7…       3028 1361         25              Experi…
#>  4 2655  8fee7b82-17… 4602STDY7…       1605 859          25              Experi…
#>  5 3609  8fee7b82-17… 4602STDY7…       1144 682          25              Experi…
#>  6 3624  8fee7b82-17… 4602STDY7…       1874 963          25              Experi…
#>  7 3946  8fee7b82-17… 4602STDY7…       1296 755          25              Experi…
#>  8 5163  8fee7b82-17… 4602STDY7…      11417 3255         25              Experi…
#>  9 5446  8fee7b82-17… 4602STDY7…       1769 946          19              Experi…
#> 10 6275  8fee7b82-17… 4602STDY7…       3750 1559         25              Experi…
#> # ℹ more rows
#> # ℹ 5 more variables: donor_id <chr>, compartment <chr>, broad_celltype <chr>,
#> #   author_cell_type <chr>, Sample <chr>

Cell metadata

Dataset-specific columns (definitions available at cellxgene.cziscience.com)

cell_count, collection_id, created_at.x, created_at.y, dataset_deployments, dataset_id, file_id, filename, filetype, is_primary_data.y, is_valid, linked_genesets, mean_genes_per_cell, name, published, published_at, revised_at, revision, s3_uri, schema_version, tombstone, updated_at.x, updated_at.y, user_submitted, x_normalization

Sample-specific columns (definitions available at cellxgene.cziscience.com)

sample_, sample_name, age_days, assay, assay_ontology_term_id, development_stage, development_stage_ontology_term_id, ethnicity, ethnicity_ontology_term_id, experiment___, organism, organism_ontology_term_id, sample_placeholder, sex, sex_ontology_term_id, tissue, tissue_harmonised, tissue_ontology_term_id, disease, disease_ontology_term_id, is_primary_data.x

Cell-specific columns (definitions available at cellxgene.cziscience.com)

cell_, cell_type, cell_type_ontology_term_idm, cell_type_harmonised, confidence_class, cell_annotation_azimuth_l2, cell_annotation_blueprint_singler

Through harmonisation and curation we introduced custom column, not present in the original CELLxGENE metadata

RNA abundance

The raw assay includes RNA abundance in the positive real scale (not transformed with non-linear functions, e.g. log sqrt). Originally CELLxGENE include a mix of scales and transformations specified in the x_normalization column.

The cpm assay includes counts per million.

Session Info

sessionInfo()
#> R Under development (unstable) (2023-11-11 r85510)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.3 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_GB              LC_COLLATE=C              
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: America/New_York
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] ggplot2_3.4.4            CuratedAtlasQueryR_1.1.1
#> 
#> loaded via a namespace (and not attached):
#>   [1] RColorBrewer_1.1-3              jsonlite_1.8.8                 
#>   [3] magrittr_2.0.3                  spatstat.utils_3.0-4           
#>   [5] farver_2.1.1                    rmarkdown_2.25                 
#>   [7] zlibbioc_1.49.0                 vctrs_0.6.5                    
#>   [9] ROCR_1.0-11                     spatstat.explore_3.2-5         
#>  [11] RCurl_1.98-1.13                 forcats_1.0.0                  
#>  [13] htmltools_0.5.7                 S4Arrays_1.3.1                 
#>  [15] ttservice_0.4.0                 Rhdf5lib_1.25.0                
#>  [17] SparseArray_1.3.1               rhdf5_2.47.1                   
#>  [19] sass_0.4.8                      sctransform_0.4.1              
#>  [21] parallelly_1.36.0               KernSmooth_2.23-22             
#>  [23] bslib_0.6.1                     htmlwidgets_1.6.4              
#>  [25] ica_1.0-3                       plyr_1.8.9                     
#>  [27] plotly_4.10.3                   zoo_1.8-12                     
#>  [29] cachem_1.0.8                    igraph_1.5.1                   
#>  [31] mime_0.12                       lifecycle_1.0.4                
#>  [33] pkgconfig_2.0.3                 Matrix_1.6-4                   
#>  [35] R6_2.5.1                        fastmap_1.1.1                  
#>  [37] GenomeInfoDbData_1.2.11         MatrixGenerics_1.15.0          
#>  [39] fitdistrplus_1.1-11             future_1.33.0                  
#>  [41] shiny_1.8.0                     digest_0.6.33                  
#>  [43] colorspace_2.1-0                patchwork_1.1.3                
#>  [45] S4Vectors_0.41.2                rprojroot_2.0.4                
#>  [47] Seurat_5.0.1                    tensor_1.5                     
#>  [49] RSpectra_0.16-1                 irlba_2.3.5.1                  
#>  [51] GenomicRanges_1.55.1            labeling_0.4.3                 
#>  [53] progressr_0.14.0                fansi_1.0.5                    
#>  [55] spatstat.sparse_3.0-3           httr_1.4.7                     
#>  [57] polyclip_1.10-6                 abind_1.4-5                    
#>  [59] compiler_4.4.0                  withr_2.5.2                    
#>  [61] DBI_1.1.3                       fastDummies_1.7.3              
#>  [63] highr_0.10                      HDF5Array_1.31.1               
#>  [65] duckdb_0.9.2-1                  MASS_7.3-60.1                  
#>  [67] DelayedArray_0.29.0             tools_4.4.0                    
#>  [69] lmtest_0.9-40                   httpuv_1.6.13                  
#>  [71] future.apply_1.11.0             goftest_1.2-3                  
#>  [73] glue_1.6.2                      nlme_3.1-164                   
#>  [75] rhdf5filters_1.15.1             promises_1.2.1                 
#>  [77] grid_4.4.0                      Rtsne_0.17                     
#>  [79] cluster_2.1.6                   reshape2_1.4.4                 
#>  [81] generics_0.1.3                  gtable_0.3.4                   
#>  [83] spatstat.data_3.0-3             tidyr_1.3.0                    
#>  [85] data.table_1.14.8               sp_2.1-2                       
#>  [87] utf8_1.2.4                      XVector_0.43.0                 
#>  [89] BiocGenerics_0.49.1             spatstat.geom_3.2-7            
#>  [91] RcppAnnoy_0.0.21                ggrepel_0.9.4                  
#>  [93] RANN_2.6.1                      pillar_1.9.0                   
#>  [95] stringr_1.5.1                   spam_2.10-0                    
#>  [97] RcppHNSW_0.5.0                  later_1.3.2                    
#>  [99] splines_4.4.0                   dplyr_1.1.4                    
#> [101] lattice_0.22-5                  survival_3.5-7                 
#> [103] deldir_2.0-2                    tidyselect_1.2.0               
#> [105] SingleCellExperiment_1.25.0     miniUI_0.1.1.1                 
#> [107] pbapply_1.7-2                   knitr_1.45                     
#> [109] gridExtra_2.3                   IRanges_2.37.0                 
#> [111] SummarizedExperiment_1.33.1     scattermore_1.2                
#> [113] stats4_4.4.0                    xfun_0.41                      
#> [115] Biobase_2.63.0                  matrixStats_1.1.0              
#> [117] stringi_1.8.2                   lazyeval_0.2.2                 
#> [119] yaml_2.3.7                      evaluate_0.23                  
#> [121] codetools_0.2-19                tibble_3.2.1                   
#> [123] cli_3.6.1                       uwot_0.1.16                    
#> [125] xtable_1.8-4                    reticulate_1.34.0              
#> [127] munsell_0.5.0                   jquerylib_0.1.4                
#> [129] GenomeInfoDb_1.39.1             Rcpp_1.0.11                    
#> [131] spatstat.random_3.2-2           globals_0.16.2                 
#> [133] dbplyr_2.4.0                    png_0.1-8                      
#> [135] parallel_4.4.0                  ellipsis_0.3.2                 
#> [137] blob_1.2.4                      assertthat_0.2.1               
#> [139] dotCall64_1.1-1                 bitops_1.0-7                   
#> [141] tidySingleCellExperiment_1.13.1 listenv_0.9.0                  
#> [143] viridisLite_0.4.2               scales_1.3.0                   
#> [145] ggridges_0.5.4                  SeuratObject_5.0.1             
#> [147] leiden_0.4.3.1                  purrr_1.0.2                    
#> [149] crayon_1.5.2                    rlang_1.1.2                    
#> [151] cowplot_1.1.1