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.
This page focuses on expression-layer retrieval workflows after metadata filtering.
library(cellNexus)
library(dplyr)
metadata <- get_metadata(cloud_metadata = SAMPLE_DATABASE_URL)
#> ℹ Downloading 1 file, totalling 0 GB
#> ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellNexus-metadata/sample_hca2024_v2.3.2.parquet to /vast/scratch/users/shen.m/r_cache/R/cellNexus/sample_hca2024_v2.3.2.parquet
metadata <- metadata |>
keep_quality_cells()query_metadata <- metadata |>
dplyr::filter(
age_days >= 40*365,
cell_type_unified_ensemble == "cd16 mono",
tissue_groups == "breast",
imputed_ethnicity == "African American"
)
query_metadata
#> # Source: SQL [?? x 29]
#> # Database: DuckDB 1.4.3 [unknown@Linux 5.14.0-570.123.1.el9_6.x86_64:R 4.5.3/:memory:]
#> cell_id dataset_id sample_id age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA empty_droplet cell_type_unified_en…²
#> <dbl> <chr> <chr> <int> <chr> <int> <dbl> <lgl> <chr>
#> 1 19 842c6f5d-4a9… 1119f482… 14600 breast 1876 9.15 FALSE cd16 mono
#> 2 14 842c6f5d-4a9… 1119f482… 14600 breast 1547 10.5 FALSE cd16 mono
#> 3 16 842c6f5d-4a9… 1119f482… 14600 breast 2438 9.80 FALSE cd16 mono
#> 4 2 842c6f5d-4a9… 1f755b9b… 14600 breast 1342 9.40 FALSE cd16 mono
#> 5 24 842c6f5d-4a9… b0d0c16e… 14600 breast 1800 10.7 FALSE cd16 mono
#> 6 22 842c6f5d-4a9… b0d0c16e… 14600 breast 1759 11.1 FALSE cd16 mono
#> 7 21 842c6f5d-4a9… b0d0c16e… 14600 breast 1552 10.2 FALSE cd16 mono
#> 8 11 842c6f5d-4a9… bd5f6876… 14600 breast 399 11.2 FALSE cd16 mono
#> 9 25 842c6f5d-4a9… 04e410cb… 14600 breast 1324 13.0 FALSE cd16 mono
#> 10 24 842c6f5d-4a9… 04e410cb… 14600 breast 1254 13.8 FALSE cd16 mono
#> 11 13 842c6f5d-4a9… 30ea4b4f… 14600 breast 1368 11.0 FALSE cd16 mono
#> 12 6 842c6f5d-4a9… 49ef9551… 14600 breast 1771 11.6 FALSE cd16 mono
#> 13 9 842c6f5d-4a9… 49ef9551… 14600 breast 1767 12.3 FALSE cd16 mono
#> # ℹ abbreviated names: ¹nFeature_expressed_in_sample, ²cell_type_unified_ensemble
#> # ℹ 20 more variables: is_immune <lgl>, subsets_Mito_percent <int>, subsets_Ribo_percent <int>, high_mitochondrion <lgl>,
#> # high_ribosome <lgl>, alive <lgl>, scDblFinder.class <chr>, file_id_cellNexus_single_cell <chr>,
#> # file_id_cellNexus_pseudobulk <chr>, count_upper_bound <dbl>, nfeature_expressed_thresh <dbl>, inverse_transform <chr>,
#> # 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>sce_counts <- query_metadata |>
get_single_cell_experiment()
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Reading files.
#>
Reading counts ■■■■■■■■■■■■■■■■ 50% | ETA: 1s
ℹ Compiling Experiment.
sce_counts
#> # A SingleCellExperiment-tibble abstraction: 13 × 30
#> # [90mFeatures=33145 | Cells=13 | Assays=counts[0m
#> .cell dataset_id sample_id age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA empty_droplet cell_type_unified_en…²
#> <chr> <chr> <chr> <int> <chr> <int> <dbl> <lgl> <chr>
#> 1 19_1 842c6f5d-4a94-… 1119f482… 14600 breast 1876 9.15 FALSE cd16 mono
#> 2 14_1 842c6f5d-4a94-… 1119f482… 14600 breast 1547 10.5 FALSE cd16 mono
#> 3 16_1 842c6f5d-4a94-… 1119f482… 14600 breast 2438 9.80 FALSE cd16 mono
#> 4 2_1 842c6f5d-4a94-… 1f755b9b… 14600 breast 1342 9.40 FALSE cd16 mono
#> 5 24_1 842c6f5d-4a94-… b0d0c16e… 14600 breast 1800 10.7 FALSE cd16 mono
#> 6 22_1 842c6f5d-4a94-… b0d0c16e… 14600 breast 1759 11.1 FALSE cd16 mono
#> 7 21_1 842c6f5d-4a94-… b0d0c16e… 14600 breast 1552 10.2 FALSE cd16 mono
#> 8 11_1 842c6f5d-4a94-… bd5f6876… 14600 breast 399 11.2 FALSE cd16 mono
#> 9 25_2 842c6f5d-4a94-… 04e410cb… 14600 breast 1324 13.0 FALSE cd16 mono
#> 10 24_2 842c6f5d-4a94-… 04e410cb… 14600 breast 1254 13.8 FALSE cd16 mono
#> 11 13_2 842c6f5d-4a94-… 30ea4b4f… 14600 breast 1368 11.0 FALSE cd16 mono
#> 12 6_2 842c6f5d-4a94-… 49ef9551… 14600 breast 1771 11.6 FALSE cd16 mono
#> 13 9_2 842c6f5d-4a94-… 49ef9551… 14600 breast 1767 12.3 FALSE cd16 mono
#> # ℹ abbreviated names: ¹nFeature_expressed_in_sample, ²cell_type_unified_ensemble
#> # ℹ 21 more variables: is_immune <lgl>, subsets_Mito_percent <int>, subsets_Ribo_percent <int>, high_mitochondrion <lgl>,
#> # high_ribosome <lgl>, alive <lgl>, scDblFinder.class <chr>, file_id_cellNexus_single_cell <chr>,
#> # file_id_cellNexus_pseudobulk <chr>, count_upper_bound <dbl>, nfeature_expressed_thresh <dbl>, inverse_transform <chr>,
#> # 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>, original_cell_ <chr>sce_cpm <- query_metadata |>
get_single_cell_experiment(assays = "cpm")
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Reading files.
#> ℹ Compiling Experiment.
sce_cpm
#> # A SingleCellExperiment-tibble abstraction: 13 × 30
#> # [90mFeatures=33145 | Cells=13 | Assays=cpm[0m
#> .cell dataset_id sample_id age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA empty_droplet cell_type_unified_en…²
#> <chr> <chr> <chr> <int> <chr> <int> <dbl> <lgl> <chr>
#> 1 19_1 842c6f5d-4a94-… 1119f482… 14600 breast 1876 9.15 FALSE cd16 mono
#> 2 14_1 842c6f5d-4a94-… 1119f482… 14600 breast 1547 10.5 FALSE cd16 mono
#> 3 16_1 842c6f5d-4a94-… 1119f482… 14600 breast 2438 9.80 FALSE cd16 mono
#> 4 2_1 842c6f5d-4a94-… 1f755b9b… 14600 breast 1342 9.40 FALSE cd16 mono
#> 5 24_1 842c6f5d-4a94-… b0d0c16e… 14600 breast 1800 10.7 FALSE cd16 mono
#> 6 22_1 842c6f5d-4a94-… b0d0c16e… 14600 breast 1759 11.1 FALSE cd16 mono
#> 7 21_1 842c6f5d-4a94-… b0d0c16e… 14600 breast 1552 10.2 FALSE cd16 mono
#> 8 11_1 842c6f5d-4a94-… bd5f6876… 14600 breast 399 11.2 FALSE cd16 mono
#> 9 25_2 842c6f5d-4a94-… 04e410cb… 14600 breast 1324 13.0 FALSE cd16 mono
#> 10 24_2 842c6f5d-4a94-… 04e410cb… 14600 breast 1254 13.8 FALSE cd16 mono
#> 11 13_2 842c6f5d-4a94-… 30ea4b4f… 14600 breast 1368 11.0 FALSE cd16 mono
#> 12 6_2 842c6f5d-4a94-… 49ef9551… 14600 breast 1771 11.6 FALSE cd16 mono
#> 13 9_2 842c6f5d-4a94-… 49ef9551… 14600 breast 1767 12.3 FALSE cd16 mono
#> # ℹ abbreviated names: ¹nFeature_expressed_in_sample, ²cell_type_unified_ensemble
#> # ℹ 21 more variables: is_immune <lgl>, subsets_Mito_percent <int>, subsets_Ribo_percent <int>, high_mitochondrion <lgl>,
#> # high_ribosome <lgl>, alive <lgl>, scDblFinder.class <chr>, file_id_cellNexus_single_cell <chr>,
#> # file_id_cellNexus_pseudobulk <chr>, count_upper_bound <dbl>, nfeature_expressed_thresh <dbl>, inverse_transform <chr>,
#> # 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>, original_cell_ <chr>pb_counts <- query_metadata |>
get_pseudobulk()
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Reading files.
#> ℹ Compiling Experiment.
pb_counts
#> # A SingleCellExperiment-tibble abstraction: 7 × 25
#> # [90mFeatures=33145 | Cells=7 | Assays=counts[0m
#> .cell sample_id cell_type_unified_en…¹ dataset_id age_days tissue_groups empty_droplet is_immune high_mitochondrion alive
#> <chr> <chr> <chr> <chr> <int> <chr> <lgl> <lgl> <lgl> <lgl>
#> 1 1119f4825… 1119f482… cd16 mono 842c6f5d-… 14600 breast FALSE TRUE FALSE TRUE
#> 2 1f755b9b5… 1f755b9b… cd16 mono 842c6f5d-… 14600 breast FALSE TRUE FALSE TRUE
#> 3 b0d0c16ed… b0d0c16e… cd16 mono 842c6f5d-… 14600 breast FALSE TRUE FALSE TRUE
#> 4 bd5f6876c… bd5f6876… cd16 mono 842c6f5d-… 14600 breast FALSE TRUE FALSE TRUE
#> 5 04e410cba… 04e410cb… cd16 mono 842c6f5d-… 14600 breast FALSE TRUE FALSE TRUE
#> 6 30ea4b4f8… 30ea4b4f… cd16 mono 842c6f5d-… 14600 breast FALSE TRUE FALSE TRUE
#> 7 49ef9551c… 49ef9551… cd16 mono 842c6f5d-… 14600 breast FALSE TRUE FALSE TRUE
#> # ℹ abbreviated name: ¹cell_type_unified_ensemble
#> # ℹ 15 more variables: scDblFinder.class <chr>, file_id_cellNexus_single_cell <chr>, file_id_cellNexus_pseudobulk <chr>,
#> # count_upper_bound <dbl>, nfeature_expressed_thresh <dbl>, inverse_transform <chr>, 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>, sample_identifier <chr># ENSEMBL IDs are expected
sce_gene <- query_metadata |>
get_single_cell_experiment(
assays = "cpm",
features = "ENSG00000134644"
)
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Reading files.
#> ℹ Compiling Experiment.
sce_gene
#> # A SingleCellExperiment-tibble abstraction: 13 × 30
#> # [90mFeatures=1 | Cells=13 | Assays=cpm[0m
#> .cell dataset_id sample_id age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA empty_droplet cell_type_unified_en…²
#> <chr> <chr> <chr> <int> <chr> <int> <dbl> <lgl> <chr>
#> 1 19_1 842c6f5d-4a94-… 1119f482… 14600 breast 1876 9.15 FALSE cd16 mono
#> 2 14_1 842c6f5d-4a94-… 1119f482… 14600 breast 1547 10.5 FALSE cd16 mono
#> 3 16_1 842c6f5d-4a94-… 1119f482… 14600 breast 2438 9.80 FALSE cd16 mono
#> 4 2_1 842c6f5d-4a94-… 1f755b9b… 14600 breast 1342 9.40 FALSE cd16 mono
#> 5 24_1 842c6f5d-4a94-… b0d0c16e… 14600 breast 1800 10.7 FALSE cd16 mono
#> 6 22_1 842c6f5d-4a94-… b0d0c16e… 14600 breast 1759 11.1 FALSE cd16 mono
#> 7 21_1 842c6f5d-4a94-… b0d0c16e… 14600 breast 1552 10.2 FALSE cd16 mono
#> 8 11_1 842c6f5d-4a94-… bd5f6876… 14600 breast 399 11.2 FALSE cd16 mono
#> 9 25_2 842c6f5d-4a94-… 04e410cb… 14600 breast 1324 13.0 FALSE cd16 mono
#> 10 24_2 842c6f5d-4a94-… 04e410cb… 14600 breast 1254 13.8 FALSE cd16 mono
#> 11 13_2 842c6f5d-4a94-… 30ea4b4f… 14600 breast 1368 11.0 FALSE cd16 mono
#> 12 6_2 842c6f5d-4a94-… 49ef9551… 14600 breast 1771 11.6 FALSE cd16 mono
#> 13 9_2 842c6f5d-4a94-… 49ef9551… 14600 breast 1767 12.3 FALSE cd16 mono
#> # ℹ abbreviated names: ¹nFeature_expressed_in_sample, ²cell_type_unified_ensemble
#> # ℹ 21 more variables: is_immune <lgl>, subsets_Mito_percent <int>, subsets_Ribo_percent <int>, high_mitochondrion <lgl>,
#> # high_ribosome <lgl>, alive <lgl>, scDblFinder.class <chr>, file_id_cellNexus_single_cell <chr>,
#> # file_id_cellNexus_pseudobulk <chr>, count_upper_bound <dbl>, nfeature_expressed_thresh <dbl>, inverse_transform <chr>,
#> # 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>, original_cell_ <chr># Seurat conversion
seurat_obj <- query_metadata |>
get_seurat()
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Reading files.
#> ℹ Compiling Experiment.
seurat_obj
#> Warning: `when()` was deprecated in purrr 1.0.0.
#> ℹ Please use `if` instead.
#> ℹ The deprecated feature was likely used in the tidyseurat package.
#> Please report the issue at <https://github.com/stemangiola/tidyseurat/issues>.
#> This warning is displayed once per session.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
#> # A Seurat-tibble abstraction: 13 × 35
#> # [90mFeatures=33145 | Cells=13 | Active assay=counts | Assays=counts[0m
#> .cell orig.ident nCount_originalexp nFeature_originalexp dataset_id sample_id age_days tissue_groups nFeature_expressed_i…¹
#> <chr> <fct> <dbl> <int> <chr> <chr> <int> <chr> <int>
#> 1 19_1 19 13.0 1970 842c6f5d-4a… 1119f482… 14600 breast 1876
#> 2 14_1 14 13.1 1633 842c6f5d-4a… 1119f482… 14600 breast 1547
#> 3 16_1 16 13.0 2529 842c6f5d-4a… 1119f482… 14600 breast 2438
#> 4 2_1 2 14.0 1430 842c6f5d-4a… 1f755b9b… 14600 breast 1342
#> 5 24_1 24 13.9 1889 842c6f5d-4a… b0d0c16e… 14600 breast 1800
#> 6 22_1 22 14.0 1850 842c6f5d-4a… b0d0c16e… 14600 breast 1759
#> 7 21_1 21 13.8 1640 842c6f5d-4a… b0d0c16e… 14600 breast 1552
#> 8 11_1 11 14.1 456 842c6f5d-4a… bd5f6876… 14600 breast 399
#> 9 25_2 25 18.9 1416 842c6f5d-4a… 04e410cb… 14600 breast 1324
#> 10 24_2 24 18.3 1342 842c6f5d-4a… 04e410cb… 14600 breast 1254
#> 11 13_2 13 14.0 1456 842c6f5d-4a… 30ea4b4f… 14600 breast 1368
#> 12 6_2 6 15.3 1861 842c6f5d-4a… 49ef9551… 14600 breast 1771
#> 13 9_2 9 15.6 1857 842c6f5d-4a… 49ef9551… 14600 breast 1767
#> # ℹ abbreviated name: ¹nFeature_expressed_in_sample
#> # ℹ 26 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>, alive <lgl>,
#> # scDblFinder.class <chr>, file_id_cellNexus_single_cell <chr>, file_id_cellNexus_pseudobulk <chr>, count_upper_bound <dbl>,
#> # nfeature_expressed_thresh <dbl>, inverse_transform <chr>, 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>, original_cell_ <chr>, …counts for raw-scale abundance.cpm for normalized cross-cell comparisons.rank for ranked signature.sct for normalized cross-cell comparison by
Seurat::SCTransform.pseudobulk for sample/cell-type aggregation
analyses.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
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Australia/Melbourne
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] RcppSpdlog_0.0.28 cellNexus_0.99.30 purrr_1.2.2
#> [4] HPCell_0.6.0 ggplot2_4.0.2 tidyr_1.3.2
#> [7] tidySingleCellExperiment_1.20.1 ttservice_0.5.3 SingleCellExperiment_1.32.0
#> [10] anndataR_1.3.1 arrow_23.0.1.2 SummarizedExperiment_1.40.0
#> [13] Biobase_2.70.0 GenomicRanges_1.62.1 Seqinfo_1.0.0
#> [16] IRanges_2.44.0 S4Vectors_0.49.1-1 BiocGenerics_0.56.0
#> [19] generics_0.1.4 MatrixGenerics_1.22.0 matrixStats_1.5.0
#> [22] dplyr_1.2.1
#>
#> loaded via a namespace (and not attached):
#> [1] igraph_2.2.3 ica_1.0-3 plotly_4.12.0
#> [4] SingleR_2.12.0 scater_1.38.1 devtools_2.5.0
#> [7] tidyselect_1.2.1 bit_4.6.0 lattice_0.22-9
#> [10] rjson_0.2.21 blob_1.3.0 stringr_1.6.0
#> [13] S4Arrays_1.10.1 rclipboard_0.2.1 parallel_4.5.3
#> [16] png_0.1-9 cli_3.6.6 ProtGenerics_1.42.0
#> [19] askpass_1.2.1 openssl_2.4.2 goftest_1.2-3
#> [22] BiocIO_1.20.0 bluster_1.20.0 BiocNeighbors_2.4.0
#> [25] tarchetypes_0.14.1 uwot_0.2.4 curl_7.0.0
#> [28] mime_0.13 evaluate_1.0.5 stringi_1.8.7
#> [31] ids_1.0.1 backports_1.5.1 desc_1.4.3
#> [34] XML_3.99-0.23 httpuv_1.6.17 AnnotationDbi_1.72.0
#> [37] magrittr_2.0.5 rappdirs_0.3.4 splines_4.5.3
#> [40] nanonext_1.8.2 aws.signature_0.6.0 DT_0.34.0
#> [43] sctransform_0.4.3 ggbeeswarm_0.7.3 sessioninfo_1.2.3
#> [46] DBI_1.3.0 HDF5Array_1.38.0 jquerylib_0.1.4
#> [49] withr_3.0.2 reformulas_0.4.4 rprojroot_2.1.1
#> [52] xgboost_3.2.1.1 tidySummarizedExperiment_1.20.1 lmtest_0.9-40
#> [55] brio_1.1.5 BiocManager_1.30.27 rtracklayer_1.70.1
#> [58] duckdb_1.4.3 htmlwidgets_1.6.4 fs_2.0.1
#> [61] biomaRt_2.66.2 ggrepel_0.9.8 SparseArray_1.10.10
#> [64] tidyseurat_0.8.10 h5mread_1.2.1 reticulate_1.46.0
#> [67] zoo_1.8-15 tiledbsoma_2.1.2 XVector_0.50.0
#> [70] knitr_1.51 RcppCCTZ_0.2.14 UCSC.utils_1.6.1
#> [73] secretbase_1.2.1 fansi_1.0.7 patchwork_1.3.2
#> [76] pak_0.11.1 grid_4.5.3 data.table_1.18.2.1
#> [79] rhdf5_2.54.1 R.oo_1.27.1 RSpectra_0.16-2
#> [82] irlba_2.3.7 tiledb_0.33.1 commonmark_2.0.0
#> [85] fastDummies_1.7.5 ellipsis_0.3.3 base64url_1.4
#> [88] lazyeval_0.2.3 yaml_2.3.12 conflicted_1.2.0
#> [91] survival_3.8-6 scattermore_1.2 crayon_1.5.3
#> [94] mirai_2.6.1 RcppAnnoy_0.0.23 RColorBrewer_1.1-3
#> [97] progressr_0.19.0 later_1.4.8 ggridges_0.5.7
#> [100] codetools_0.2-20 base64enc_0.1-6 tidybulk_2.1.0
#> [103] Seurat_5.5.0.9002 KEGGREST_1.50.0 Rtsne_0.17
#> [106] limma_3.66.0 Rsamtools_2.26.0 filelock_1.0.3
#> [109] pkgconfig_2.0.3 xml2_1.5.2 spatstat.univar_3.1-7
#> [112] GenomicAlignments_1.46.0 spatstat.sparse_3.1-0 viridisLite_0.4.3
#> [115] xtable_1.8-8 plyr_1.8.9 httr_1.4.8
#> [118] rbibutils_2.4.1 tools_4.5.3 globals_0.19.1
#> [121] SeuratObject_5.4.0 pkgbuild_1.4.8 beeswarm_0.4.0
#> [124] checkmate_2.3.4 nlme_3.1-168 dbplyr_2.5.2
#> [127] assertthat_0.2.1 lme4_2.0-1 digest_0.6.39
#> [130] Matrix_1.7-4 dir.expiry_1.18.0 farver_2.1.2
#> [133] tzdb_0.5.0 AnnotationFilter_1.34.0 reshape2_1.4.5
#> [136] viridis_0.6.5 glue_1.8.0 cachem_1.1.0
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