This page is a standalone metadata guide for cellNexus
and documents the key fields used in downstream analysis.
library(cellNexus)
metadata <- get_metadata(cloud_metadata = SAMPLE_DATABASE_URL)
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 18 842c6f5d-4a9… 1119f482… 14600 breast 1894 10.3 FALSE cd14 mono
#> 2 19 842c6f5d-4a9… 1119f482… 14600 breast 1876 9.15 FALSE cd16 mono
#> 3 20 842c6f5d-4a9… 1119f482… 14600 breast 1441 10.3 FALSE cd14 mono
#> 4 14 842c6f5d-4a9… 1119f482… 14600 breast 1547 10.5 FALSE cd16 mono
#> 5 15 842c6f5d-4a9… 1119f482… 14600 breast 1701 8.99 FALSE cd14 mono
#> 6 16 842c6f5d-4a9… 1119f482… 14600 breast 2438 9.80 FALSE cd16 mono
#> 7 17 842c6f5d-4a9… 1119f482… 14600 breast 2122 9.46 FALSE cd14 mono
#> 8 2 842c6f5d-4a9… 1f755b9b… 14600 breast 1342 9.40 FALSE cd16 mono
#> 9 5 842c6f5d-4a9… 1f755b9b… 14600 breast 1820 9.25 FALSE cd14 mono
#> 10 4 842c6f5d-4a9… 1f755b9b… 14600 breast 1514 9.30 FALSE cd14 mono
#> # ℹ more rows
#> # 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>, …cellNexus metadata are harmonised to support
cross-dataset analysis:
| Column | Description |
|---|---|
cell_id |
Cell identifier. |
observation_joinid |
Cell ID join key linking metadata. |
dataset_id |
Primary dataset identifier in the atlas. |
sample_id |
Harmonised sample identifier. |
donor_id |
Donor identifier. |
age_days |
Donor age in days. |
tissue_groups |
Coarse tissue grouping for analysis. |
nFeature_expressed_in_sample |
Number of expressed features per cell. |
nCount_RNA |
Total RNA counts per cell (sample-aware). |
empty_droplet |
Quality-control flag for empty droplets. |
cell_type_unified_ensemble |
Consensus immune identity from Azimuth and SingleR
(Blueprint, Monaco). |
is_immune |
Curated flag for immune-cell context. |
subsets_Mito_percent |
Percent of each cell’s total counts coming from mitochondrial genes in a sample. |
subsets_Ribo_percent |
Percent of each cell’s total counts coming from ribosomal genes in a sample. |
high_mitochondrion |
TRUE if the cell’s mitochondrial percent exceeds the QC cutoff. |
high_ribosome |
TRUE if the cell’s ribosomal percent exceeds the QC cutoff. |
scDblFinder.class |
Quality-control flag for doublet classification from
scDblFinder. |
file_id_cellNexus_single_cell |
Internal file id for single-cell layers. |
file_id_cellNexus_pseudobulk |
Internal file id for pseudobulk layers. |
count_upper_bound |
Count capping threshold used in transformation. |
nfeature_expressed_thresh |
Threshold of the number of expressed features per cell. |
inverse_transform |
Transformation method used in pre-processing pipeline. |
alive |
Quality-control flag for viable cells (e.g. mitochondrial signal). |
cell_annotation_blueprint_singler |
SingleR annotation (Blueprint). |
cell_annotation_monaco_singler |
SingleR annotation (Monaco). |
cell_annotation_azimuth_l2 |
Azimuth cell annotation. |
ethnicity_flagging_score |
Supporting score for ethnicity imputation. |
low_confidence_ethnicity |
Supporting flag for low-confidence ethnicity calls. |
.aggregated_cells |
Post-QC cells combined into each pseudobulk sample. |
imputed_ethnicity |
Imputed ethnicity label. |
atlas_id |
cellNexus atlas release identifier (internal use). |
# Which columns are available?
colnames(metadata)
#> [1] "cell_id" "dataset_id" "sample_id"
#> [4] "age_days" "tissue_groups" "nFeature_expressed_in_sample"
#> [7] "nCount_RNA" "empty_droplet" "cell_type_unified_ensemble"
#> [10] "is_immune" "subsets_Mito_percent" "subsets_Ribo_percent"
#> [13] "high_mitochondrion" "high_ribosome" "alive"
#> [16] "scDblFinder.class" "file_id_cellNexus_single_cell" "file_id_cellNexus_pseudobulk"
#> [19] "count_upper_bound" "nfeature_expressed_thresh" "inverse_transform"
#> [22] "cell_annotation_blueprint_singler" "cell_annotation_monaco_singler" "cell_annotation_azimuth_l2"
#> [25] "ethnicity_flagging_score" "low_confidence_ethnicity" ".aggregated_cells"
#> [28] "imputed_ethnicity" "atlas_id"
# How many datasets per tissue group?
metadata |>
dplyr::distinct(dataset_id, tissue_groups) |>
dplyr::count(tissue_groups, sort = TRUE)
#> # 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:]
#> # Ordered by: desc(n)
#> tissue_groups n
#> <chr> <dbl>
#> 1 blood 10
#> 2 respiratory system 7
#> 3 bone marrow 6
#> 4 renal system 4
#> 5 breast 3
#> 6 thymus 3
#> 7 cerebral lobes and cortical areas 2
#> 8 spleen 2
#> 9 female reproductive system 2
#> 10 nasal, oral, and pharyngeal regions 2
#> 11 sensory-related structures 1
#> 12 brainstem and cerebellar structures 1
#> 13 oesophagus 1
#> 14 lymphatic system 1
#> 15 epithelium and mucosal tissues 1
#> 16 stomach 1
#> 17 vasculature 1
#> 18 small intestine 1
#> 19 endocrine system 1
# Typical quality-control filtering
metadata_qc <- metadata |>
dplyr::filter(
empty_droplet == FALSE,
alive == TRUE,
scDblFinder.class != "doublet"
)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
#> [139] BiocFileCache_3.0.0 polyclip_1.10-7 rjsoncons_1.3.2
#> [142] Biostrings_2.78.0 parallelly_1.46.1 aws.s3_0.3.22
#> [145] pkgload_1.5.1 statmod_1.5.1 here_1.0.2
#> [148] RcppHNSW_0.6.0 ScaledMatrix_1.18.0 minqa_1.2.8
#> [151] pbapply_1.7-4 httr2_1.2.2 job_0.3.1
#> [154] spam_2.11-3 dqrng_0.4.1 utf8_1.2.6
#> [157] scDblFinder_1.24.10 basilisk_1.22.0 crew_1.3.0
#> [160] gridExtra_2.3 shiny_1.13.0 R.utils_2.13.0
#> [163] rhdf5filters_1.22.0 RCurl_1.98-1.18 memoise_2.0.1
#> [166] rmarkdown_2.31 nanoarrow_0.8.0 scales_1.4.0
#> [169] R.methodsS3_1.8.2 future_1.70.0 RANN_2.6.2
#> [172] renv_1.2.1 spatstat.data_3.1-9 rstudioapi_0.18.0
#> [175] cluster_2.1.8.2 zellkonverter_1.20.1 spatstat.utils_3.2-2
#> [178] hms_1.1.4 fitdistrplus_1.2-6 cowplot_1.2.0
#> [181] rlang_1.2.0 GenomeInfoDb_1.46.2 crew.cluster_0.4.0
#> [184] DelayedMatrixStats_1.32.0 sparseMatrixStats_1.22.0 shinyWidgets_0.9.1
#> [187] dotCall64_1.2 scuttle_1.20.0 xfun_0.57
#> [190] abind_1.4-8 spdl_0.0.5 tibble_3.3.1
#> [193] EnsDb.Hsapiens.v86_2.99.0 Rhdf5lib_1.32.0 readr_2.2.0
#> [196] bitops_1.0-9 Rdpack_2.6.6 ps_1.9.2
#> [199] promises_1.5.0 RSQLite_2.4.6 cellxgenedp_1.14.0
#> [202] DelayedArray_0.36.1 proxy_0.4-29 compiler_4.5.3
#> [205] prettyunits_1.2.0 boot_1.3-32 beachmat_2.26.0
#> [208] listenv_0.10.1 Rcpp_1.1.1-1 edgeR_4.8.2
#> [211] roxygen2_7.3.3 BiocSingular_1.26.1 tensor_1.5.1
#> [214] usethis_3.2.1 MASS_7.3-65 progress_1.2.3
#> [217] uuid_1.2-2 BiocParallel_1.44.0 ggupset_0.4.1
#> [220] nanotime_0.3.13 spatstat.random_3.4-5 R6_2.6.1
#> [223] fastmap_1.2.0 vipor_0.4.7 ensembldb_2.34.0
#> [226] ROCR_1.0-12 targets_1.12.0 rsvd_1.0.5
#> [229] gtable_0.3.6 KernSmooth_2.23-26 miniUI_0.1.2
#> [232] deldir_2.0-4 htmltools_0.5.9 bit64_4.6.0-1
#> [235] spatstat.explore_3.8-0 lifecycle_1.0.5 S7_0.2.1-1
#> [238] processx_3.8.7 nloptr_2.2.1 callr_3.7.6
#> [241] restfulr_0.0.16 sass_0.4.10 vctrs_0.7.3
#> [244] testthat_3.3.2 rsconnect_1.10.1 spatstat.geom_3.7-3
#> [247] scran_1.38.1 sp_2.2-1 future.apply_1.20.2
#> [250] bslib_0.10.0 pillar_1.11.1 GenomicFeatures_1.62.0
#> [253] DropletUtils_1.30.0 cellxgene.census_1.16.1 collections_0.3.12
#> [256] metapod_1.18.0 locfit_1.5-9.12 otel_0.2.0
#> [259] BiocStyle_2.38.0 jsonlite_2.0.0 cigarillo_1.0.0