# Gene Expression Explore

# Overview

This page focuses on expression-layer retrieval workflows after metadata filtering.

    library(cellNexus)
    library(dplyr)

    metadata <- get_metadata(cloud_metadata = SAMPLE_DATABASE_URL["cellnexus"])
    #> ℹ Downloading 1 file, totalling 0 GB
    #> ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellNexus-metadata/cellnexus_sample_metadata.2.3.0.parquet to /vast/scratch/users/shen.m/r_cache/R/cellNexus/cellnexus_sample_metadata.2.3.0.parquet
    metadata <- metadata |>
      keep_quality_cells()

# Choose cells through metadata filters

    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 58]
    #> # Database: DuckDB 1.4.3 [unknown@Linux 5.14.0-570.112.1.el9_6.x86_64:R 4.5.3/:memory:]
    #>    cell_id observation_joinid dataset_id                       sample_id sample_ experiment___ run_from_cell_id sample_heuristic age_days tissue_groups
    #>      <dbl> <chr>              <chr>                            <chr>     <chr>   <chr>         <chr>            <chr>               <int> <chr>
    #>  1      16 j}0<Y>a#X~         842c6f5d-4a94-4eef-8510-8c792d1… 1119f482… 1119f4… ""            <NA>             182a61cc-b041-4…    14600 breast
    #>  2      19 lNmuO5xs~3         842c6f5d-4a94-4eef-8510-8c792d1… 1119f482… 1119f4… ""            <NA>             182a61cc-b041-4…    14600 breast
    #>  3      14 qxl7HJjL$L         842c6f5d-4a94-4eef-8510-8c792d1… 1119f482… 1119f4… ""            <NA>             182a61cc-b041-4…    14600 breast
    #>  4       2 $jvBt8wHSK         842c6f5d-4a94-4eef-8510-8c792d1… 1f755b9b… 1f755b… ""            <NA>             9ca47fe5-873e-4…    14600 breast
    #>  5      21 Mq^|(c<-#3         842c6f5d-4a94-4eef-8510-8c792d1… b0d0c16e… b0d0c1… ""            <NA>             0033e380-cba5-4…    14600 breast
    #>  6      24 I`4{4__f#J         842c6f5d-4a94-4eef-8510-8c792d1… b0d0c16e… b0d0c1… ""            <NA>             0033e380-cba5-4…    14600 breast
    #>  7      22 %vkLP;!cqY         842c6f5d-4a94-4eef-8510-8c792d1… b0d0c16e… b0d0c1… ""            <NA>             0033e380-cba5-4…    14600 breast
    #>  8      11 gncTL3)pV~         842c6f5d-4a94-4eef-8510-8c792d1… bd5f6876… bd5f68… ""            <NA>             c5d33ad8-c134-4…    14600 breast
    #>  9      25 rfOnkhfWl8         842c6f5d-4a94-4eef-8510-8c792d1… 04e410cb… 04e410… ""            <NA>             68150f23-cfed-4…    14600 breast
    #> 10      24 =tj7A<!2TZ         842c6f5d-4a94-4eef-8510-8c792d1… 04e410cb… 04e410… ""            <NA>             68150f23-cfed-4…    14600 breast
    #> 11      13 Py{Fqs?~!!         842c6f5d-4a94-4eef-8510-8c792d1… 30ea4b4f… 30ea4b… ""            <NA>             2f6cb696-f78d-4…    14600 breast
    #> 12       9 s$u5u14ye$         842c6f5d-4a94-4eef-8510-8c792d1… 49ef9551… 49ef95… ""            <NA>             6fa99d77-112d-4…    14600 breast
    #> 13       6 ?y4kdGGQ!^         842c6f5d-4a94-4eef-8510-8c792d1… 49ef9551… 49ef95… ""            <NA>             6fa99d77-112d-4…    14600 breast
    #> # ℹ 48 more variables: nFeature_expressed_in_sample <int>, nCount_RNA <dbl>, empty_droplet <lgl>, cell_type_unified_ensemble <chr>, is_immune <lgl>,
    #> #   subsets_Mito_percent <int>, subsets_Ribo_percent <int>, high_mitochondrion <lgl>, high_ribosome <lgl>, scDblFinder.class <chr>,
    #> #   sample_chunk <int>, cell_chunk <int>, sample_pseudobulk_chunk <int>, file_id_cellNexus_single_cell <chr>, file_id_cellNexus_pseudobulk <chr>,
    #> #   count_upper_bound <dbl>, nfeature_expressed_thresh <dbl>, inverse_transform <chr>, alive <lgl>, cell_annotation_blueprint_singler <chr>,
    #> #   cell_annotation_monaco_singler <chr>, cell_annotation_azimuth_l2 <chr>, ethnicity_flagging_score <dbl>, low_confidence_ethnicity <chr>,
    #> #   .aggregated_cells <int>, imputed_ethnicity <chr>, atlas_id <chr>, citation <chr>, collection_id <chr>, dataset_version_id <chr>,
    #> #   default_embedding <chr>, published_at <chr>, raw_data_location <chr>, revised_at <chr>, primary_cell_count <chr>, schema_version <chr>, …

# Retrieve expression by representation

## Single-cell counts

    sce_counts <- query_metadata |>
      get_single_cell_experiment()
    #> ℹ Realising metadata.
    #> ℹ Synchronising files
    #> ℹ Reading files.
    #>
    Reading counts ■■■■■■■■■■■■■■■■                  50% | ETA:  2s

    ℹ Compiling Experiment.

## Counts per million

    sce_cpm <- query_metadata |>
      get_single_cell_experiment(assays = "cpm")
    #> ℹ Realising metadata.
    #> ℹ Synchronising files
    #> ℹ Reading files.
    #>
    Reading cpm ■■■■■■■■■■■■■■■■                  50% | ETA:  6s

    ℹ Compiling Experiment.
    sce_cpm
    #> # A SingleCellExperiment-tibble abstraction: 13 × 59
    #> # [90mFeatures=33145 | Cells=13 | Assays=cpm[0m
    #>    .cell observation_joinid dataset_id  sample_id sample_ experiment___ run_from_cell_id sample_heuristic age_days tissue_groups nFeature_expressed_i…¹
    #>    <chr> <chr>              <chr>       <chr>     <chr>   <chr>         <chr>            <chr>               <int> <chr>                          <int>
    #>  1 16_1  j}0<Y>a#X~         842c6f5d-4… 1119f482… 1119f4… ""            <NA>             182a61cc-b041-4…    14600 breast                          2438
    #>  2 19_1  lNmuO5xs~3         842c6f5d-4… 1119f482… 1119f4… ""            <NA>             182a61cc-b041-4…    14600 breast                          1876
    #>  3 14_1  qxl7HJjL$L         842c6f5d-4… 1119f482… 1119f4… ""            <NA>             182a61cc-b041-4…    14600 breast                          1547
    #>  4 2_1   $jvBt8wHSK         842c6f5d-4… 1f755b9b… 1f755b… ""            <NA>             9ca47fe5-873e-4…    14600 breast                          1342
    #>  5 21_1  Mq^|(c<-#3         842c6f5d-4… b0d0c16e… b0d0c1… ""            <NA>             0033e380-cba5-4…    14600 breast                          1552
    #>  6 24_1  I`4{4__f#J         842c6f5d-4… b0d0c16e… b0d0c1… ""            <NA>             0033e380-cba5-4…    14600 breast                          1800
    #>  7 22_1  %vkLP;!cqY         842c6f5d-4… b0d0c16e… b0d0c1… ""            <NA>             0033e380-cba5-4…    14600 breast                          1759
    #>  8 11_1  gncTL3)pV~         842c6f5d-4… bd5f6876… bd5f68… ""            <NA>             c5d33ad8-c134-4…    14600 breast                           399
    #>  9 25_2  rfOnkhfWl8         842c6f5d-4… 04e410cb… 04e410… ""            <NA>             68150f23-cfed-4…    14600 breast                          1324
    #> 10 24_2  =tj7A<!2TZ         842c6f5d-4… 04e410cb… 04e410… ""            <NA>             68150f23-cfed-4…    14600 breast                          1254
    #> 11 13_2  Py{Fqs?~!!         842c6f5d-4… 30ea4b4f… 30ea4b… ""            <NA>             2f6cb696-f78d-4…    14600 breast                          1368
    #> 12 9_2   s$u5u14ye$         842c6f5d-4… 49ef9551… 49ef95… ""            <NA>             6fa99d77-112d-4…    14600 breast                          1767
    #> 13 6_2   ?y4kdGGQ!^         842c6f5d-4… 49ef9551… 49ef95… ""            <NA>             6fa99d77-112d-4…    14600 breast                          1771
    #> # ℹ abbreviated name: ¹​nFeature_expressed_in_sample
    #> # ℹ 48 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>, atlas_id <chr>, citation <chr>, collection_id <chr>, dataset_version_id <chr>, …

## Pseudobulk

    pb_counts <- query_metadata |>
      get_pseudobulk()
    #> ℹ Realising metadata.
    #> ℹ Synchronising files
    #> ℹ Reading files.
    #> ℹ Compiling Experiment.
    sce_cpm
    #> # A SingleCellExperiment-tibble abstraction: 13 × 59
    #> # [90mFeatures=33145 | Cells=13 | Assays=cpm[0m
    #>    .cell observation_joinid dataset_id  sample_id sample_ experiment___ run_from_cell_id sample_heuristic age_days tissue_groups nFeature_expressed_i…¹
    #>    <chr> <chr>              <chr>       <chr>     <chr>   <chr>         <chr>            <chr>               <int> <chr>                          <int>
    #>  1 16_1  j}0<Y>a#X~         842c6f5d-4… 1119f482… 1119f4… ""            <NA>             182a61cc-b041-4…    14600 breast                          2438
    #>  2 19_1  lNmuO5xs~3         842c6f5d-4… 1119f482… 1119f4… ""            <NA>             182a61cc-b041-4…    14600 breast                          1876
    #>  3 14_1  qxl7HJjL$L         842c6f5d-4… 1119f482… 1119f4… ""            <NA>             182a61cc-b041-4…    14600 breast                          1547
    #>  4 2_1   $jvBt8wHSK         842c6f5d-4… 1f755b9b… 1f755b… ""            <NA>             9ca47fe5-873e-4…    14600 breast                          1342
    #>  5 21_1  Mq^|(c<-#3         842c6f5d-4… b0d0c16e… b0d0c1… ""            <NA>             0033e380-cba5-4…    14600 breast                          1552
    #>  6 24_1  I`4{4__f#J         842c6f5d-4… b0d0c16e… b0d0c1… ""            <NA>             0033e380-cba5-4…    14600 breast                          1800
    #>  7 22_1  %vkLP;!cqY         842c6f5d-4… b0d0c16e… b0d0c1… ""            <NA>             0033e380-cba5-4…    14600 breast                          1759
    #>  8 11_1  gncTL3)pV~         842c6f5d-4… bd5f6876… bd5f68… ""            <NA>             c5d33ad8-c134-4…    14600 breast                           399
    #>  9 25_2  rfOnkhfWl8         842c6f5d-4… 04e410cb… 04e410… ""            <NA>             68150f23-cfed-4…    14600 breast                          1324
    #> 10 24_2  =tj7A<!2TZ         842c6f5d-4… 04e410cb… 04e410… ""            <NA>             68150f23-cfed-4…    14600 breast                          1254
    #> 11 13_2  Py{Fqs?~!!         842c6f5d-4… 30ea4b4f… 30ea4b… ""            <NA>             2f6cb696-f78d-4…    14600 breast                          1368
    #> 12 9_2   s$u5u14ye$         842c6f5d-4… 49ef9551… 49ef95… ""            <NA>             6fa99d77-112d-4…    14600 breast                          1767
    #> 13 6_2   ?y4kdGGQ!^         842c6f5d-4… 49ef9551… 49ef95… ""            <NA>             6fa99d77-112d-4…    14600 breast                          1771
    #> # ℹ abbreviated name: ¹​nFeature_expressed_in_sample
    #> # ℹ 48 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>, atlas_id <chr>, citation <chr>, collection_id <chr>, dataset_version_id <chr>, …

# Targeted gene queries

    # 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.
    #>
    Reading cpm ■■■■■■■■■■■■■■■■                  50% | ETA:  6s

    ℹ Compiling Experiment.
    sce_gene
    #> # A SingleCellExperiment-tibble abstraction: 13 × 59
    #> # [90mFeatures=1 | Cells=13 | Assays=cpm[0m
    #>    .cell observation_joinid dataset_id  sample_id sample_ experiment___ run_from_cell_id sample_heuristic age_days tissue_groups nFeature_expressed_i…¹
    #>    <chr> <chr>              <chr>       <chr>     <chr>   <chr>         <chr>            <chr>               <int> <chr>                          <int>
    #>  1 16_1  j}0<Y>a#X~         842c6f5d-4… 1119f482… 1119f4… ""            <NA>             182a61cc-b041-4…    14600 breast                          2438
    #>  2 19_1  lNmuO5xs~3         842c6f5d-4… 1119f482… 1119f4… ""            <NA>             182a61cc-b041-4…    14600 breast                          1876
    #>  3 14_1  qxl7HJjL$L         842c6f5d-4… 1119f482… 1119f4… ""            <NA>             182a61cc-b041-4…    14600 breast                          1547
    #>  4 2_1   $jvBt8wHSK         842c6f5d-4… 1f755b9b… 1f755b… ""            <NA>             9ca47fe5-873e-4…    14600 breast                          1342
    #>  5 21_1  Mq^|(c<-#3         842c6f5d-4… b0d0c16e… b0d0c1… ""            <NA>             0033e380-cba5-4…    14600 breast                          1552
    #>  6 24_1  I`4{4__f#J         842c6f5d-4… b0d0c16e… b0d0c1… ""            <NA>             0033e380-cba5-4…    14600 breast                          1800
    #>  7 22_1  %vkLP;!cqY         842c6f5d-4… b0d0c16e… b0d0c1… ""            <NA>             0033e380-cba5-4…    14600 breast                          1759
    #>  8 11_1  gncTL3)pV~         842c6f5d-4… bd5f6876… bd5f68… ""            <NA>             c5d33ad8-c134-4…    14600 breast                           399
    #>  9 25_2  rfOnkhfWl8         842c6f5d-4… 04e410cb… 04e410… ""            <NA>             68150f23-cfed-4…    14600 breast                          1324
    #> 10 24_2  =tj7A<!2TZ         842c6f5d-4… 04e410cb… 04e410… ""            <NA>             68150f23-cfed-4…    14600 breast                          1254
    #> 11 13_2  Py{Fqs?~!!         842c6f5d-4… 30ea4b4f… 30ea4b… ""            <NA>             2f6cb696-f78d-4…    14600 breast                          1368
    #> 12 9_2   s$u5u14ye$         842c6f5d-4… 49ef9551… 49ef95… ""            <NA>             6fa99d77-112d-4…    14600 breast                          1767
    #> 13 6_2   ?y4kdGGQ!^         842c6f5d-4… 49ef9551… 49ef95… ""            <NA>             6fa99d77-112d-4…    14600 breast                          1771
    #> # ℹ abbreviated name: ¹​nFeature_expressed_in_sample
    #> # ℹ 48 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>, atlas_id <chr>, citation <chr>, collection_id <chr>, dataset_version_id <chr>, …

# Seurat

    # Seurat conversion
    seurat_obj <- query_metadata |>
      get_seurat()
    #> ℹ Realising metadata.
    #> ℹ Synchronising files
    #> ℹ Reading files.
    #>
    Reading counts ■■■■■■■■■■■■■■■■                  50% | ETA:  4s

    ℹ Compiling Experiment.
    seurat_obj
    #> An object of class Seurat
    #> 33145 features across 13 samples within 1 assay
    #> Active assay: originalexp (33145 features, 0 variable features)
    #>  2 layers present: counts, data

# Portable output examples

    saveRDS(sce_counts, "single_cell_counts.rds")
    HDF5Array::saveHDF5SummarizedExperiment(
      sce_counts,
      "single_cell_counts",
      replace = TRUE,
      as.sparse = TRUE
    )
    anndataR::write_h5ad(sce_counts, "single_cell_counts.h5ad")

# Interpretation notes

- Use `counts` for raw-scale abundance.
- Use `cpm` for normalized cross-cell comparisons.
- Use `rank` for ranked signature.
- Use `sct` for normalized cross-cell comparison by `Seurat::SCTransform`.
- Use `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     LC_MONETARY=en_US.UTF-8
    #>  [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C
    #> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    #>
    #> time zone: Australia/Melbourne
    #> tzcode source: system (glibc)
    #>
    #> attached base packages:
    #> [1] stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> other attached packages:
    #> [1] BiocStyle_2.38.0  ggplot2_4.0.2     dplyr_1.2.1       cellNexus_0.99.22
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] RcppAnnoy_0.0.23                splines_4.5.3                   later_1.4.8                     filelock_1.0.3
    #>   [5] tibble_3.3.1                    polyclip_1.10-7                 fastDummies_1.7.5               lifecycle_1.0.5
    #>   [9] rprojroot_2.1.1                 globals_0.19.1                  lattice_0.22-9                  MASS_7.3-65
    #>  [13] backports_1.5.1                 magrittr_2.0.5                  sass_0.4.10                     plotly_4.12.0
    #>  [17] rmarkdown_2.31                  jquerylib_0.1.4                 yaml_2.3.12                     httpuv_1.6.17
    #>  [21] otel_0.2.0                      Seurat_5.5.0.9002               sctransform_0.4.3               spam_2.11-3
    #>  [25] sp_2.2-1                        sessioninfo_1.2.3               pkgbuild_1.4.8                  spatstat.sparse_3.1-0
    #>  [29] reticulate_1.46.0               cowplot_1.2.0                   pbapply_1.7-4                   DBI_1.3.0
    #>  [33] RColorBrewer_1.1-3              abind_1.4-8                     pkgload_1.5.1                   Rtsne_0.17
    #>  [37] GenomicRanges_1.62.1            purrr_1.2.2                     BiocGenerics_0.56.0             tidySingleCellExperiment_1.20.1
    #>  [41] IRanges_2.44.0                  S4Vectors_0.49.1-1              ggrepel_0.9.8                   irlba_2.3.7
    #>  [45] listenv_0.10.1                  spatstat.utils_3.2-2            goftest_1.2-3                   RSpectra_0.16-2
    #>  [49] spatstat.random_3.4-5           fitdistrplus_1.2-6              parallelly_1.46.1               commonmark_2.0.0
    #>  [53] codetools_0.2-20                DelayedArray_0.36.1             xml2_1.5.2                      tidyselect_1.2.1
    #>  [57] rclipboard_0.2.1                UCSC.utils_1.6.1                farver_2.1.2                    shinyWidgets_0.9.1
    #>  [61] matrixStats_1.5.0               stats4_4.5.3                    spatstat.explore_3.8-0          duckdb_1.4.3
    #>  [65] Seqinfo_1.0.0                   roxygen2_7.3.3                  jsonlite_2.0.0                  ellipsis_0.3.3
    #>  [69] progressr_0.19.0                ggridges_0.5.7                  survival_3.8-6                  tools_4.5.3
    #>  [73] ica_1.0-3                       Rcpp_1.1.1-1                    glue_1.8.0                      gridExtra_2.3
    #>  [77] SparseArray_1.10.10             xfun_0.57                       MatrixGenerics_1.22.0           usethis_3.2.1
    #>  [81] GenomeInfoDb_1.46.2             HDF5Array_1.38.0                withr_3.0.2                     BiocManager_1.30.27
    #>  [85] fastmap_1.2.0                   basilisk_1.22.0                 fansi_1.0.7                     rhdf5filters_1.22.0
    #>  [89] ttservice_0.5.3                 digest_0.6.39                   R6_2.6.1                        mime_0.13
    #>  [93] scattermore_1.2                 tensor_1.5.1                    spatstat.data_3.1-9             h5mread_1.2.1
    #>  [97] utf8_1.2.6                      tidyr_1.3.2                     generics_0.1.4                  data.table_1.18.2.1
    #> [101] httr_1.4.8                      htmlwidgets_1.6.4               S4Arrays_1.10.1                 uwot_0.2.4
    #> [105] pkgconfig_2.0.3                 gtable_0.3.6                    rsconnect_1.8.0                 blob_1.3.0
    #> [109] lmtest_0.9-40                   S7_0.2.1-1                      SingleCellExperiment_1.32.0     XVector_0.50.0
    #> [113] htmltools_0.5.9                 bookdown_0.46                   dotCall64_1.2                   SeuratObject_5.4.0
    #> [117] scales_1.4.0                    Biobase_2.70.0                  png_0.1-9                       spatstat.univar_3.1-7
    #> [121] knitr_1.51                      rstudioapi_0.18.0               reshape2_1.4.5                  checkmate_2.3.4
    #> [125] nlme_3.1-168                    curl_7.0.0                      anndataR_1.0.2                  rhdf5_2.54.1
    #> [129] cachem_1.1.0                    zoo_1.8-15                      stringr_1.6.0                   KernSmooth_2.23-26
    #> [133] parallel_4.5.3                  miniUI_0.1.2                    arrow_23.0.1.2                  zellkonverter_1.20.1
    #> [137] desc_1.4.3                      pillar_1.11.1                   grid_4.5.3                      vctrs_0.7.3
    #> [141] RANN_2.6.2                      promises_1.5.0                  dbplyr_2.5.2                    xtable_1.8-8
    #> [145] cluster_2.1.8.2                 evaluate_1.0.5                  cli_3.6.6                       compiler_4.5.3
    #> [149] rlang_1.2.0                     future.apply_1.20.2             forcats_1.0.1                   plyr_1.8.9
    #> [153] fs_2.0.1                        stringi_1.8.7                   viridisLite_0.4.3               deldir_2.0-4
    #> [157] assertthat_0.2.1                lazyeval_0.2.3                  devtools_2.5.0                  spatstat.geom_3.7-3
    #> [161] Matrix_1.7-4                    dir.expiry_1.18.0               RcppHNSW_0.6.0                  patchwork_1.3.2
    #> [165] bit64_4.6.0-1                   future_1.70.0                   Rhdf5lib_1.32.0                 shiny_1.13.0
    #> [169] SummarizedExperiment_1.40.0     ROCR_1.0-12                     igraph_2.2.3                    memoise_2.0.1
    #> [173] bslib_0.10.0                    bit_4.6.0