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.22668580.
This page focuses on expression-layer retrieval workflows after metadata filtering.
query_metadata <- metadata |>
dplyr::filter(
age_days >= 40*365,
cell_type_unified_ensemble == "cd16 mono",
tissue |> stringr::str_detect("breast"),
imputed_ethnicity == "African American"
)
#> Error in `dplyr::filter()`:
#> ℹ In argument: `stringr::str_detect(tissue, "breast")`
#> Caused by error:
#> ! Object `tissue` not found.
query_metadata
#> Error:
#> ! object 'query_metadata' not foundcounts 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] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] BiocStyle_2.38.0 RcppSpdlog_0.0.28 ggplot2_4.0.2 dplyr_1.2.1 cellNexus_0.99.34
#>
#> loaded via a namespace (and not attached):
#> [1] fs_2.0.1 matrixStats_1.5.0 spatstat.sparse_3.1-0
#> [4] fontawesome_0.5.3 httr_1.4.8 RColorBrewer_1.1-3
#> [7] tools_4.5.3 sctransform_0.4.3 backports_1.5.1
#> [10] utf8_1.2.6 R6_2.6.1 DT_0.34.0
#> [13] 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
#> [19] gridExtra_2.3 nanoarrow_0.8.0 progressr_0.19.0
#> [22] cli_3.6.6 Biobase_2.70.0 spatstat.explore_3.8-0
#> [25] fastDummies_1.7.5 sass_0.4.10 Seurat_5.5.0.9002
#> [28] arrow_23.0.1.2 S7_0.2.1-1 spatstat.data_3.1-9
#> [31] ggridges_0.5.7 pbapply_1.7-4 commonmark_2.0.0
#> [34] parallelly_1.46.1 rstudioapi_0.18.0 generics_0.1.4
#> [37] ica_1.0-3 spatstat.random_3.4-5 Matrix_1.7-4
#> [40] fansi_1.0.7 S4Vectors_0.49.1-1 rclipboard_0.2.1
#> [43] abind_1.4-8 lifecycle_1.0.5 yaml_2.3.12
#> [46] SummarizedExperiment_1.40.0 rhdf5_2.54.1 SparseArray_1.10.10
#> [49] Rtsne_0.17 grid_4.5.3 blob_1.3.0
#> [52] promises_1.5.0 dir.expiry_1.18.0 miniUI_0.1.2
#> [55] lattice_0.22-9 cowplot_1.2.0 pillar_1.11.1
#> [58] knitr_1.51 GenomicRanges_1.62.1 future.apply_1.20.2
#> [61] codetools_0.2-20 glue_1.8.0 spatstat.univar_3.1-7
#> [64] tiledb_0.33.1 data.table_1.18.2.1 tidySingleCellExperiment_1.20.1
#> [67] vctrs_0.7.3 png_0.1-9 spam_2.11-3
#> [70] gtable_0.3.6 aws.s3_0.3.22 assertthat_0.2.1
#> [73] cachem_1.1.0 xfun_0.57 S4Arrays_1.10.1
#> [76] mime_0.13 Seqinfo_1.0.0 survival_3.8-6
#> [79] SingleCellExperiment_1.32.0 ellipsis_0.3.3 fitdistrplus_1.2-6
#> [82] ROCR_1.0-12 nlme_3.1-168 tiledbsoma_2.1.2
#> [85] RcppCCTZ_0.2.14 bit64_4.6.0-1 filelock_1.0.3
#> [88] RcppAnnoy_0.0.23 GenomeInfoDb_1.46.2 rprojroot_2.1.1
#> [91] bslib_0.10.0 irlba_2.3.7 KernSmooth_2.23-26
#> [94] otel_0.2.0 BiocGenerics_0.56.0 DBI_1.3.0
#> [97] zellkonverter_1.20.1 duckdb_1.4.3 tidyselect_1.2.1
#> [100] processx_3.8.7 cellxgene.census_1.16.1 bit_4.6.0
#> [103] compiler_4.5.3 curl_7.0.0 rjsoncons_1.3.2
#> [106] h5mread_1.2.1 xml2_1.5.2 nanotime_0.3.13
#> [109] DelayedArray_0.36.1 plotly_4.12.0 bookdown_0.46
#> [112] checkmate_2.3.4 scales_1.4.0 lmtest_0.9-40
#> [115] callr_3.7.6 spdl_0.0.5 stringr_1.6.0
#> [118] anndataR_1.3.1 digest_0.6.39 goftest_1.2-3
#> [121] spatstat.utils_3.2-2 rmarkdown_2.31 basilisk_1.22.0
#> [124] XVector_0.50.0 htmltools_0.5.9 pkgconfig_2.0.3
#> [127] base64enc_0.1-6 MatrixGenerics_1.22.0 dbplyr_2.5.2
#> [130] fastmap_1.2.0 rlang_1.2.0 htmlwidgets_1.6.4
#> [133] UCSC.utils_1.6.1 shiny_1.13.0 farver_2.1.2
#> [136] jquerylib_0.1.4 zoo_1.8-15 jsonlite_2.0.0
#> [139] magrittr_2.0.5 dotCall64_1.2 patchwork_1.3.2
#> [142] Rhdf5lib_1.32.0 Rcpp_1.1.1-1 reticulate_1.46.0
#> [145] stringi_1.8.7 brio_1.1.5 MASS_7.3-65
#> [148] plyr_1.8.9 parallel_4.5.3 listenv_0.10.1
#> [151] ggrepel_0.9.8 forcats_1.0.1 deldir_2.0-4
#> [154] splines_4.5.3 tensor_1.5.1 ps_1.9.2
#> [157] cellxgenedp_1.14.0 igraph_2.2.3 spatstat.geom_3.7-3
#> [160] RcppHNSW_0.6.0 reshape2_1.4.5 stats4_4.5.3
#> [163] evaluate_1.0.5 ttservice_0.5.3 SeuratObject_5.4.0
#> [166] BiocManager_1.30.27 httpuv_1.6.17 RANN_2.6.2
#> [169] tidyr_1.3.2 purrr_1.2.2 polyclip_1.10-7
#> [172] future_1.70.0 scattermore_1.2 xtable_1.8-8
#> [175] RSpectra_0.16-2 later_1.4.8 viridisLite_0.4.3
#> [178] tibble_3.3.1 memoise_2.0.1 aws.signature_0.6.0
#> [181] IRanges_2.44.0 cluster_2.1.8.2 shinyWidgets_0.9.1
#> [184] globals_0.19.1