diff --git a/DESCRIPTION b/DESCRIPTION index 06178daf..1cf3a8c0 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Type: Package Package: cellNexus Title: Queries the Human Cell Atlas -Version: 0.99.30 +Version: 0.99.31 Authors@R: c( person( "Stefano", diff --git a/R/dev.R b/R/dev.R index 01efbc69..ff5b1ed9 100644 --- a/R/dev.R +++ b/R/dev.R @@ -240,7 +240,7 @@ hdf5_to_anndata <- function(input_directory, output_directory) { #' @param census_version Character scalar. Census LTS release in date format. #' @return NULL downsample_metadata <- function( - cellnexus_output = "sample_hca2024_v2.3.1.parquet", + cellnexus_output = "sample_hca2024_v2.3.2.parquet", census_version = "2024-07-01" ) { census_metadata <- get_census_metadata(census_version) diff --git a/R/metadata.R b/R/metadata.R index e2f24c14..26acd515 100644 --- a/R/metadata.R +++ b/R/metadata.R @@ -14,8 +14,8 @@ cache <- rlang::env( #' @keywords internal #' @noRd metadata_aliases <- c( - hca_2024 = "hca2024_v2.3.1.parquet", - hca_2025 = "hca2025_v0.1.0.parquet" + hca_2024 = "hca2024_v2.3.2.parquet", + hca_2025 = "hca2025_v0.1.1.parquet" ) #' Returns the URLs for all metadata files @@ -62,7 +62,7 @@ get_metadata_url <- function(databases = c("hca_2024")) { SAMPLE_DATABASE_URL <- c( paste0( "https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/", - "cellNexus-metadata/sample_hca2024_v2.3.1.parquet" + "cellNexus-metadata/sample_hca2024_v2.3.2.parquet" ) ) @@ -127,9 +127,8 @@ SAMPLE_DATABASE_URL <- c( #' #' Through harmonisation and curation we introduced custom columns not present #' in the original CELLxGENE metadata: -#' -#' `cell_count`: Number of cells in a dataset. -#' `feature_count`: Number of genes in a dataset. +#' +#' `sample_id`: Sample identifier. #' `age_days`: Donor age in days. #' `tissue_groups`: Coarse tissue grouping for analysis. #' `empty_droplet`: Whether a cell is called an empty droplet from expressed-gene count per sample (default threshold 200; targeted panels may differ). @@ -138,9 +137,15 @@ SAMPLE_DATABASE_URL <- c( #' `cell_type_unified_ensemble`: Consensus immune identity from Azimuth and SingleR (Blueprint, Monaco). #' `cell_annotation_azimuth_l2`: Azimuth cell annotation. #' `cell_annotation_blueprint_singler`: SingleR annotation (Blueprint). -#' `cell_annotation_blueprint_monaco`: SingleR annotation (Monaco). -#' `is_immune`: Whether a cell is an immune cell. -#' `sample_heuristic`: Internal sample subdivision helper. +#' `cell_annotation_monaco_singler`: SingleR annotation (Monaco). +#' `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. +#' `count_upper_bound`: Count capping threshold used in counts transformation. +#' `inverse_transform`: Transformation method used in pre-processing pipeline. +#' `nfeature_expressed_thresh`: Threshold of the number of expressed features per cell. +#' `is_immune`: Curated logical flag for immune-cell context. #' `file_id_cellNexus_single_cell`: Internal file id for single-cell layers. #' `file_id_cellNexus_pseudobulk`: Internal file id for pseudobulk layers. #' `sample_id`: Harmonised sample identifier. diff --git a/README.md b/README.md index eb7dfd94..50b2ae2c 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,6 @@ cellNexus ================ +Mangiola et al. @@ -39,28 +40,50 @@ CELLxGENE releases. +
+ + +
+ +
+ + + +
+ +
+ + + +
+ +
+ - -
-# Repositories +
+ + -### R API: [here](https://github.com/MangiolaLaboratory/cellNexus) -### Python API: [here](https://github.com/MangiolaLaboratory/cellNexusPy/) +
+ +
+ + -### Article code: [here](https://github.com/MangiolaLaboratory/cellNexus_article) +
# Query interface @@ -93,7 +116,13 @@ saved to `get_default_cache_dir()` unless a custom path is provided via the cache_directory argument. The `metadata` variable can then be re-used for all subsequent queries. -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](https://zenodo.org/records/21633607/files/pseudobulk_se.h5ad?download=1). For all versions: [10.5281/zenodo.21633607](https://zenodo.org/records/21633607). +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](https://zenodo.org/records/21633607/files/pseudobulk_se.h5ad?download=1). +For all versions: +[10.5281/zenodo.21633607](https://zenodo.org/records/21633607). The following sections demonstrate the metadata, quality control, generation of raw and normalised counts, and pseudobulk construction for @@ -102,27 +131,27 @@ the specified query. ``` r metadata <- get_metadata() metadata -#> # Source: SQL [?? x 37] +#> # Source: SQL [?? x 31] #> # Database: DuckDB 1.4.3 [unknown@Linux 5.14.0-570.123.1.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 nFeature_expressed_i…¹ nCount_RNA -#> -#> 1 1 `;+Wwc*oS9 574e9f9e-f8… b290d7ef… b290d7… "" BPH556PrGA2_Fco… 25915 prostate 1025 113. -#> 2 1 s<8rT5qe3X 574e9f9e-f8… b290d7ef… b290d7… "" BPH556PrGA2_Fco… 25915 prostate 2586 77.6 -#> 3 2 Se=|eIq*={ 574e9f9e-f8… b290d7ef… b290d7… "" BPH556PrGA2_Fco… 25915 prostate 992 57.6 -#> 4 2 dcNO`ReB5o 574e9f9e-f8… b290d7ef… b290d7… "" BPH556PrGA2_Fco… 25915 prostate 2002 163. -#> 5 3 F_Jf~Pzj BPH556PrGA2_Fco… 25915 prostate 730 93.6 -#> 6 16 i(U>N;cU4_ 574e9f9e-f8… b290d7ef… b290d7… "" BPH556PrGA2_Fco… 25915 prostate 718 342. -#> 7 4 MK~^fbPVCl 574e9f9e-f8… b290d7ef… b290d7… "" BPH556PrGA2_Fco… 25915 prostate 846 99.8 -#> 8 4 J+o&MJmtR5 574e9f9e-f8… b290d7ef… b290d7… "" BPH556PrGA2_Fco… 25915 prostate 829 123. -#> 9 12 $LL!IWeW`F 574e9f9e-f8… b290d7ef… b290d7… "" BPH556PrGA2_Fco… 25915 prostate 2482 61.8 -#> 10 18 $zB;$PErEP 574e9f9e-f8… b290d7ef… b290d7… "" BPH556PrGA2_Fco… 25915 prostate 828 86.6 +#> cell_id observation_joinid dataset_id sample_id donor_id age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA +#> +#> 1 1 `;+Wwc*oS9 574e9f9e-f8b4-41ef-bf… b290d7ef… BPH556 25915 prostate 1025 113. +#> 2 1 s<8rT5qe3X 574e9f9e-f8b4-41ef-bf… b290d7ef… BPH556 25915 prostate 2586 77.6 +#> 3 2 Se=|eIq*={ 574e9f9e-f8b4-41ef-bf… b290d7ef… BPH556 25915 prostate 992 57.6 +#> 4 2 dcNO`ReB5o 574e9f9e-f8b4-41ef-bf… b290d7ef… BPH556 25915 prostate 2002 163. +#> 5 3 F_Jf~Pzj 6 16 i(U>N;cU4_ 574e9f9e-f8b4-41ef-bf… b290d7ef… BPH556 25915 prostate 718 342. +#> 7 4 MK~^fbPVCl 574e9f9e-f8b4-41ef-bf… b290d7ef… BPH556 25915 prostate 846 99.8 +#> 8 4 J+o&MJmtR5 574e9f9e-f8b4-41ef-bf… b290d7ef… BPH556 25915 prostate 829 123. +#> 9 12 $LL!IWeW`F 574e9f9e-f8b4-41ef-bf… b290d7ef… BPH556 25915 prostate 2482 61.8 +#> 10 18 $zB;$PErEP 574e9f9e-f8b4-41ef-bf… b290d7ef… BPH556 25915 prostate 828 86.6 #> # ℹ more rows #> # ℹ abbreviated name: ¹​nFeature_expressed_in_sample -#> # ℹ 25 more variables: empty_droplet , cell_type_unified_ensemble , is_immune , subsets_Mito_percent , subsets_Ribo_percent , -#> # high_mitochondrion , high_ribosome , scDblFinder.class , sample_chunk , cell_chunk , sample_pseudobulk_chunk , -#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , inverse_transform , -#> # alive , cell_annotation_blueprint_singler , cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , -#> # low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , atlas_id +#> # ℹ 22 more variables: empty_droplet , cell_type_unified_ensemble , is_immune , subsets_Mito_percent , +#> # subsets_Ribo_percent , high_mitochondrion , high_ribosome , alive , scDblFinder.class , +#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , +#> # nfeature_expressed_thresh , inverse_transform , cell_annotation_blueprint_singler , +#> # cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , … ``` ## Quality control @@ -177,16 +206,16 @@ metadata |> #> # Database: DuckDB 1.4.3 [unknown@Linux 5.14.0-570.123.1.el9_6.x86_64:R 4.5.3/:memory:] #> tissue cell_type_unified_ensemble #> -#> 1 subcutaneous adipose tissue nkt -#> 2 subcutaneous adipose tissue muscle -#> 3 subcutaneous adipose tissue macrophage -#> 4 subcutaneous adipose tissue cd8 tem -#> 5 subcutaneous adipose tissue cd16 mono -#> 6 subcutaneous adipose tissue cd14 mono -#> 7 subcutaneous adipose tissue b naive -#> 8 subcutaneous adipose tissue cd4 th1/th17 em -#> 9 subcutaneous adipose tissue granulocyte -#> 10 subcutaneous adipose tissue cd4 naive +#> 1 transition zone of prostate cdc +#> 2 transition zone of prostate epithelial +#> 3 transition zone of prostate b memory +#> 4 transition zone of prostate endothelial +#> 5 transition zone of prostate monocytic +#> 6 transition zone of prostate dc +#> 7 transition zone of prostate nk +#> 8 transition zone of prostate other +#> 9 transition zone of prostate stromal +#> 10 transition zone of prostate b #> # ℹ more rows ``` @@ -208,11 +237,9 @@ single_cell_counts <- #> ℹ Synchronising files #> ℹ Reading files. #> -Reading counts ■■■■ 10% | ETA: 11s - -Reading counts ■■■■■■■ 20% | ETA: 7s +Reading counts ■■■■■■■ 20% | ETA: 9s -Reading counts ■■■■■■■■■■ 30% | ETA: 6s +Reading counts ■■■■■■■■■■ 30% | ETA: 7s Reading counts ■■■■■■■■■■■■■ 40% | ETA: 5s @@ -230,27 +257,27 @@ Reading counts ■■■■■■■■■■■■■■■■■■■■■ ℹ Compiling Experiment. single_cell_counts -#> # A SingleCellExperiment-tibble abstraction: 2,806 × 60 -#> # [90mFeatures=33145 | Cells=2806 | Assays=counts[0m -#> .cell observation_joinid dataset_id sample_id sample_ experiment___ run_from_cell_id sample_heuristic age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA -#> -#> 1 80_1 zz-!e5_XAo 842c6f5d-4a94… 1de3f3ba… 1de3f3… "" 7fabaf1c-52fd-4… 14600 breast 1749 10.8 -#> 2 81_1 -mb&DWckf( 842c6f5d-4a94… 1de3f3ba… 1de3f3… "" 7fabaf1c-52fd-4… 14600 breast 1993 12.4 -#> 3 73_1 z_=CTOs4{z 842c6f5d-4a94… 4b5e66fa… 4b5e66… "" 04983012-bb56-4… 14600 breast 2866 10.3 -#> 4 74_1 fNzorxA`Mf 842c6f5d-4a94… 4b5e66fa… 4b5e66… "" 04983012-bb56-4… 14600 breast 1942 7.58 -#> 5 76_1 bTlx!HK=oS 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 1671 9.65 -#> 6 77_1 E4g5+)v;AV 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 2340 11.9 -#> 7 78_1 +q?29B%2nH 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 1714 13.2 -#> 8 79_1 zuJ#MBMWy; 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 1506 12.3 -#> 9 1_1 I8a42<8st4 842c6f5d-4a94… 184fa234… 184fa2… "" c2aa4d8d-e9df-4… 14600 breast 3395 11.8 -#> 10 72_1 8wGs7JgUjj 842c6f5d-4a94… 6b194412… 6b1944… "" b3ff1aad-40fd-4… 14600 breast 2548 13.1 +#> # A SingleCellExperiment-tibble abstraction: 2,806 × 54 +#> # Features=33145 | Cells=2806 | Assays=counts +#> .cell observation_joinid dataset_id sample_id donor_id age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA empty_droplet +#> +#> 1 80_1 zz-!e5_XAo 842c6f5d-… 1de3f3ba… P58 14600 breast 1749 10.8 FALSE +#> 2 81_1 -mb&DWckf( 842c6f5d-… 1de3f3ba… P58 14600 breast 1993 12.4 FALSE +#> 3 73_1 z_=CTOs4{z 842c6f5d-… 4b5e66fa… P39 14600 breast 2866 10.3 FALSE +#> 4 74_1 fNzorxA`Mf 842c6f5d-… 4b5e66fa… P39 14600 breast 1942 7.58 FALSE +#> 5 1_1 I8a42<8st4 842c6f5d-… 184fa234… P65 14600 breast 3395 11.8 FALSE +#> 6 72_1 8wGs7JgUjj 842c6f5d-… 6b194412… P39 14600 breast 2548 13.1 FALSE +#> 7 75_1 F9G7A+GgjA 842c6f5d-… db5a69ed… P40 14600 breast 1291 10.2 FALSE +#> 8 76_1 bTlx!HK=oS 842c6f5d-… 52ab9222… P58 14600 breast 1671 9.65 FALSE +#> 9 77_1 E4g5+)v;AV 842c6f5d-… 52ab9222… P58 14600 breast 2340 11.9 FALSE +#> 10 78_1 +q?29B%2nH 842c6f5d-… 52ab9222… P58 14600 breast 1714 13.2 FALSE #> # ℹ 2,796 more rows #> # ℹ abbreviated name: ¹​nFeature_expressed_in_sample -#> # ℹ 48 more variables: empty_droplet , cell_type_unified_ensemble , is_immune , subsets_Mito_percent , subsets_Ribo_percent , -#> # high_mitochondrion , high_ribosome , scDblFinder.class , sample_chunk , cell_chunk , sample_pseudobulk_chunk , -#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , inverse_transform , -#> # alive , cell_annotation_blueprint_singler , cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , -#> # low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , atlas_id , dataset_version_id , collection_id , … +#> # ℹ 44 more variables: cell_type_unified_ensemble , is_immune , subsets_Mito_percent , +#> # subsets_Ribo_percent , high_mitochondrion , high_ribosome , alive , scDblFinder.class , +#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , +#> # nfeature_expressed_thresh , inverse_transform , cell_annotation_blueprint_singler , +#> # cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , … ``` ### Query counts scaled per million @@ -269,7 +296,7 @@ single_cell_cpm <- #> ℹ Synchronising files #> ℹ Reading files. #> -Reading cpm ■■■■■■■ 20% | ETA: 5s +Reading cpm ■■■■■■■ 20% | ETA: 6s Reading cpm ■■■■■■■■■■ 30% | ETA: 4s @@ -289,27 +316,27 @@ Reading cpm ■■■■■■■■■■■■■■■■■■■■■■ ℹ Compiling Experiment. single_cell_cpm -#> # A SingleCellExperiment-tibble abstraction: 2,806 × 60 -#> # [90mFeatures=33145 | Cells=2806 | 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 nCount_RNA -#> -#> 1 80_1 zz-!e5_XAo 842c6f5d-4a94… 1de3f3ba… 1de3f3… "" 7fabaf1c-52fd-4… 14600 breast 1749 10.8 -#> 2 81_1 -mb&DWckf( 842c6f5d-4a94… 1de3f3ba… 1de3f3… "" 7fabaf1c-52fd-4… 14600 breast 1993 12.4 -#> 3 73_1 z_=CTOs4{z 842c6f5d-4a94… 4b5e66fa… 4b5e66… "" 04983012-bb56-4… 14600 breast 2866 10.3 -#> 4 74_1 fNzorxA`Mf 842c6f5d-4a94… 4b5e66fa… 4b5e66… "" 04983012-bb56-4… 14600 breast 1942 7.58 -#> 5 76_1 bTlx!HK=oS 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 1671 9.65 -#> 6 77_1 E4g5+)v;AV 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 2340 11.9 -#> 7 78_1 +q?29B%2nH 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 1714 13.2 -#> 8 79_1 zuJ#MBMWy; 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 1506 12.3 -#> 9 1_1 I8a42<8st4 842c6f5d-4a94… 184fa234… 184fa2… "" c2aa4d8d-e9df-4… 14600 breast 3395 11.8 -#> 10 72_1 8wGs7JgUjj 842c6f5d-4a94… 6b194412… 6b1944… "" b3ff1aad-40fd-4… 14600 breast 2548 13.1 +#> # A SingleCellExperiment-tibble abstraction: 2,806 × 54 +#> # Features=33145 | Cells=2806 | Assays=cpm +#> .cell observation_joinid dataset_id sample_id donor_id age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA empty_droplet +#> +#> 1 76_1 bTlx!HK=oS 842c6f5d-… 52ab9222… P58 14600 breast 1671 9.65 FALSE +#> 2 77_1 E4g5+)v;AV 842c6f5d-… 52ab9222… P58 14600 breast 2340 11.9 FALSE +#> 3 78_1 +q?29B%2nH 842c6f5d-… 52ab9222… P58 14600 breast 1714 13.2 FALSE +#> 4 79_1 zuJ#MBMWy; 842c6f5d-… 52ab9222… P58 14600 breast 1506 12.3 FALSE +#> 5 1_1 I8a42<8st4 842c6f5d-… 184fa234… P65 14600 breast 3395 11.8 FALSE +#> 6 72_1 8wGs7JgUjj 842c6f5d-… 6b194412… P39 14600 breast 2548 13.1 FALSE +#> 7 75_1 F9G7A+GgjA 842c6f5d-… db5a69ed… P40 14600 breast 1291 10.2 FALSE +#> 8 80_1 zz-!e5_XAo 842c6f5d-… 1de3f3ba… P58 14600 breast 1749 10.8 FALSE +#> 9 81_1 -mb&DWckf( 842c6f5d-… 1de3f3ba… P58 14600 breast 1993 12.4 FALSE +#> 10 73_1 z_=CTOs4{z 842c6f5d-… 4b5e66fa… P39 14600 breast 2866 10.3 FALSE #> # ℹ 2,796 more rows #> # ℹ abbreviated name: ¹​nFeature_expressed_in_sample -#> # ℹ 48 more variables: empty_droplet , cell_type_unified_ensemble , is_immune , subsets_Mito_percent , subsets_Ribo_percent , -#> # high_mitochondrion , high_ribosome , scDblFinder.class , sample_chunk , cell_chunk , sample_pseudobulk_chunk , -#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , inverse_transform , -#> # alive , cell_annotation_blueprint_singler , cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , -#> # low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , atlas_id , dataset_version_id , collection_id , … +#> # ℹ 44 more variables: cell_type_unified_ensemble , is_immune , subsets_Mito_percent , +#> # subsets_Ribo_percent , high_mitochondrion , high_ribosome , alive , scDblFinder.class , +#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , +#> # nfeature_expressed_thresh , inverse_transform , cell_annotation_blueprint_singler , +#> # cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , … ``` ### Query SCT normalised counts @@ -345,7 +372,7 @@ Reading sct ■■■■■■■■■■■■■■■■ 50 ! The number of cells in the SingleCellExperiment will be less than the number of cells you have selected from the metadata. Are cell IDs duplicated? Or, do cell IDs correspond to the counts file? #> Reading sct ■■■■■■■■■■■■■■■■ 50% | ETA: 3s -Reading sct ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 3s +Reading sct ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 2s Reading sct ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 2s @@ -362,27 +389,27 @@ Reading sct ■■■■■■■■■■■■■■■■■■■■■■ #> ℹ Compiling Experiment. single_cell_sct -#> # A SingleCellExperiment-tibble abstraction: 1,193 × 60 -#> # [90mFeatures=33145 | Cells=1193 | Assays=sct[0m -#> .cell observation_joinid dataset_id sample_id sample_ experiment___ run_from_cell_id sample_heuristic age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA -#> -#> 1 80_1 zz-!e5_XAo 842c6f5d-4a94… 1de3f3ba… 1de3f3… "" 7fabaf1c-52fd-4… 14600 breast 1749 10.8 -#> 2 81_1 -mb&DWckf( 842c6f5d-4a94… 1de3f3ba… 1de3f3… "" 7fabaf1c-52fd-4… 14600 breast 1993 12.4 -#> 3 73_1 z_=CTOs4{z 842c6f5d-4a94… 4b5e66fa… 4b5e66… "" 04983012-bb56-4… 14600 breast 2866 10.3 -#> 4 74_1 fNzorxA`Mf 842c6f5d-4a94… 4b5e66fa… 4b5e66… "" 04983012-bb56-4… 14600 breast 1942 7.58 -#> 5 1_1 I8a42<8st4 842c6f5d-4a94… 184fa234… 184fa2… "" c2aa4d8d-e9df-4… 14600 breast 3395 11.8 -#> 6 72_1 8wGs7JgUjj 842c6f5d-4a94… 6b194412… 6b1944… "" b3ff1aad-40fd-4… 14600 breast 2548 13.1 -#> 7 75_1 F9G7A+GgjA 842c6f5d-4a94… db5a69ed… db5a69… "" 49beb83c-66a1-4… 14600 breast 1291 10.2 -#> 8 1_2 >8f0}-gXFY 842c6f5d-4a94… 81d05f17… 81d05f… "" b866c1d4-3dfd-4… 14600 breast 2513 13.3 -#> 9 22_2 2lQ`<&l3-A 842c6f5d-4a94… 30967738… 309677… "" 7d4045ff-3f48-4… 14600 breast 2058 9.91 -#> 10 5_2 +p4uNj_7$S 842c6f5d-4a94… a91e6814… a91e68… "" 700a819c-03f9-4… 14600 breast 1870 11.1 +#> # A SingleCellExperiment-tibble abstraction: 1,193 × 54 +#> # Features=33145 | Cells=1193 | Assays=sct +#> .cell observation_joinid dataset_id sample_id donor_id age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA empty_droplet +#> +#> 1 80_1 zz-!e5_XAo 842c6f5d-… 1de3f3ba… P58 14600 breast 1749 10.8 FALSE +#> 2 81_1 -mb&DWckf( 842c6f5d-… 1de3f3ba… P58 14600 breast 1993 12.4 FALSE +#> 3 72_1 8wGs7JgUjj 842c6f5d-… 6b194412… P39 14600 breast 2548 13.1 FALSE +#> 4 75_1 F9G7A+GgjA 842c6f5d-… db5a69ed… P40 14600 breast 1291 10.2 FALSE +#> 5 73_1 z_=CTOs4{z 842c6f5d-… 4b5e66fa… P39 14600 breast 2866 10.3 FALSE +#> 6 74_1 fNzorxA`Mf 842c6f5d-… 4b5e66fa… P39 14600 breast 1942 7.58 FALSE +#> 7 1_1 I8a42<8st4 842c6f5d-… 184fa234… P65 14600 breast 3395 11.8 FALSE +#> 8 1_2 >8f0}-gXFY 842c6f5d-… 81d05f17… P63 14600 breast 2513 13.3 FALSE +#> 9 22_2 2lQ`<&l3-A 842c6f5d-… 30967738… P57 14600 breast 2058 9.91 FALSE +#> 10 23_2 sqV-|vcI4R 842c6f5d-… d8ecdd92… P41 14600 breast 2375 13.7 FALSE #> # ℹ 1,183 more rows #> # ℹ abbreviated name: ¹​nFeature_expressed_in_sample -#> # ℹ 48 more variables: empty_droplet , cell_type_unified_ensemble , is_immune , subsets_Mito_percent , subsets_Ribo_percent , -#> # high_mitochondrion , high_ribosome , scDblFinder.class , sample_chunk , cell_chunk , sample_pseudobulk_chunk , -#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , inverse_transform , -#> # alive , cell_annotation_blueprint_singler , cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , -#> # low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , atlas_id , dataset_version_id , collection_id , … +#> # ℹ 44 more variables: cell_type_unified_ensemble , is_immune , subsets_Mito_percent , +#> # subsets_Ribo_percent , high_mitochondrion , high_ribosome , alive , scDblFinder.class , +#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , +#> # nfeature_expressed_thresh , inverse_transform , cell_annotation_blueprint_singler , +#> # cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , … ``` ### Query pseudobulk @@ -400,13 +427,13 @@ pseudobulk_counts <- #> ℹ Synchronising files #> ℹ Reading files. #> -Reading counts ■■■■■ 14% | ETA: 8s +Reading counts ■■■■■ 14% | ETA: 9s -Reading counts ■■■■■■■■■■ 29% | ETA: 11s +Reading counts ■■■■■■■■■■ 29% | ETA: 9s -Reading counts ■■■■■■■■■■■■■■ 43% | ETA: 9s +Reading counts ■■■■■■■■■■■■■■ 43% | ETA: 7s -Reading counts ■■■■■■■■■■■■■■■■■■ 57% | ETA: 6s +Reading counts ■■■■■■■■■■■■■■■■■■ 57% | ETA: 5s Reading counts ■■■■■■■■■■■■■■■■■■■■■■ 71% | ETA: 4s @@ -417,27 +444,27 @@ Reading counts ■■■■■■■■■■■■■■■■■■■■■ #> ℹ Compiling Experiment. pseudobulk_counts -#> # A SingleCellExperiment-tibble abstraction: 139 × 43 -#> # [90mFeatures=15888 | Cells=139 | Assays=counts[0m -#> .cell dataset_id sample_id sample_ experiment___ run_from_cell_id sample_heuristic age_days tissue_groups cell_type_unified_en…¹ sample_chunk cell_chunk -#> -#> 1 2e8c9911c9bfbf… 0ba16f4b-… 2e8c9911… 2e8c99… "" HDBR15279,HDBR1… NA respiratory … cd14 mono 1 1 -#> 2 0d874636bc714a… 1e6a6ef9-… 0d874636… 0d8746… "" Leader_Merad_20… 29930 respiratory … monocytic 1 5 -#> 3 0d874636bc714a… 1e6a6ef9-… 0d874636… 0d8746… "" Leader_Merad_20… 29930 respiratory … cd14 mono 1 5 -#> 4 0d874636bc714a… 1e6a6ef9-… 0d874636… 0d8746… "" Leader_Merad_20… 29930 respiratory … cd16 mono 1 5 -#> 5 0d874636bc714a… 1e6a6ef9-… 0d874636… 0d8746… "" Leader_Merad_20… 29930 respiratory … macrophage 1 5 -#> 6 0d874636bc714a… 1e6a6ef9-… 0d874636… 0d8746… "" Leader_Merad_20… 29930 respiratory … other 1 5 -#> 7 11721339cb1dfc… 1e6a6ef9-… 11721339… 117213… "" Leader_Merad_20… 26645 respiratory … monocytic 1 7 -#> 8 11721339cb1dfc… 1e6a6ef9-… 11721339… 117213… "" Leader_Merad_20… 26645 respiratory … cd14 mono 1 7 -#> 9 f71af64a552d45… 1e6a6ef9-… f71af64a… f71af6… "" Leader_Merad_20… 27010 respiratory … monocytic 1 6 -#> 10 f71af64a552d45… 1e6a6ef9-… f71af64a… f71af6… "" Leader_Merad_20… 27010 respiratory … cd14 mono 1 6 -#> # ℹ 129 more rows +#> # A SingleCellExperiment-tibble abstraction: 146 × 46 +#> # Features=15888 | Cells=146 | Assays=counts +#> .cell sample_id cell_type_unified_en…¹ dataset_id donor_id age_days tissue_groups empty_droplet is_immune high_mitochondrion +#> +#> 1 2e8c9… 2e8c9911… cd14 mono 0ba16f4b-… HDBR152… NA respiratory … FALSE TRUE FALSE +#> 2 f71af… f71af64a… monocytic 1e6a6ef9-… Leader_… 27010 respiratory … FALSE TRUE FALSE +#> 3 f71af… f71af64a… cd14 mono 1e6a6ef9-… Leader_… 27010 respiratory … FALSE TRUE FALSE +#> 4 f71af… f71af64a… cd8 tem 1e6a6ef9-… Leader_… 27010 respiratory … FALSE TRUE FALSE +#> 5 11721… 11721339… monocytic 1e6a6ef9-… Leader_… 26645 respiratory … FALSE TRUE FALSE +#> 6 11721… 11721339… cd14 mono 1e6a6ef9-… Leader_… 26645 respiratory … FALSE TRUE FALSE +#> 7 0d874… 0d874636… cd14 mono 1e6a6ef9-… Leader_… 29930 respiratory … FALSE TRUE FALSE +#> 8 0d874… 0d874636… cd16 mono 1e6a6ef9-… Leader_… 29930 respiratory … FALSE TRUE FALSE +#> 9 0d874… 0d874636… macrophage 1e6a6ef9-… Leader_… 29930 respiratory … FALSE TRUE FALSE +#> 10 0d874… 0d874636… monocytic 1e6a6ef9-… Leader_… 29930 respiratory … FALSE TRUE FALSE +#> # ℹ 136 more rows #> # ℹ abbreviated name: ¹​cell_type_unified_ensemble -#> # ℹ 31 more variables: sample_pseudobulk_chunk , file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , -#> # inverse_transform , ethnicity_flagging_score , low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , atlas_id , -#> # dataset_version_id , collection_id , cell_count , citation , default_embedding , explorer_url , feature_count , -#> # mean_genes_per_cell , primary_cell_count , schema_version , title , tombstone , x_approximate_distribution , published_at , -#> # revised_at , tissue , self_reported_ethnicity , assay , disease , sex , sample_identifier +#> # ℹ 36 more variables: alive , scDblFinder.class , file_id_cellNexus_single_cell , +#> # file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , inverse_transform , +#> # cell_annotation_azimuth_l2 , ethnicity_flagging_score , low_confidence_ethnicity , .aggregated_cells , +#> # imputed_ethnicity , atlas_id , dataset_version_id , collection_id , cell_count , citation , +#> # default_embedding , explorer_url , feature_count , mean_genes_per_cell , primary_cell_count , … ``` ## Download cell communication metadata @@ -465,15 +492,16 @@ case. get_cell_communication_strength(cloud_metadata = get_metadata_url("cellNexus_lr_signaling_pathway_strength_DEMO.parquet")) #> # Source: SQL [?? x 16] #> # Database: DuckDB 1.4.3 [unknown@Linux 5.14.0-570.123.1.el9_6.x86_64:R 4.5.3/:memory:] -#> source target ligand receptor lr_prob lr_pval interaction_name interaction_name_2 pathway_name annotation evidence pathway_prob pathway_pval sample_id -#> -#> 1 b b TGFB1 TGFbR1_R2 0.000116 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR2) TGFb Secreted … KEGG: h… 0.000420 1 b290d7ef… -#> 2 b memory b TGFB1 TGFbR1_R2 0.000865 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR2) TGFb Secreted … KEGG: h… 0.00185 1 b290d7ef… -#> 3 b naive b TGFB1 TGFbR1_R2 0.000696 0.99 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR2) TGFb Secreted … KEGG: h… 0.00146 0.994 b290d7ef… -#> 4 cd14 mono b TGFB1 TGFbR1_R2 0.00240 0.81 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR2) TGFb Secreted … KEGG: h… 0.00472 0.924 b290d7ef… -#> 5 cd4 naive b TGFB1 TGFbR1_R2 0.000957 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR2) TGFb Secreted … KEGG: h… 0.00201 0.998 b290d7ef… -#> 6 cd4 tem b TGFB1 TGFbR1_R2 0.00242 0.76 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR2) TGFb Secreted … KEGG: h… 0.00467 0.797 b290d7ef… -#> # ℹ 2 more variables: interaction_count , interaction_weight +#> source target ligand receptor lr_prob lr_pval interaction_name interaction_name_2 pathway_name annotation evidence +#> +#> 1 b b TGFB1 TGFbR1_R2 0.000116 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR… TGFb Secreted … KEGG: h… +#> 2 b memory b TGFB1 TGFbR1_R2 0.000865 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR… TGFb Secreted … KEGG: h… +#> 3 b naive b TGFB1 TGFbR1_R2 0.000696 0.99 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR… TGFb Secreted … KEGG: h… +#> 4 cd14 mono b TGFB1 TGFbR1_R2 0.00240 0.81 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR… TGFb Secreted … KEGG: h… +#> 5 cd4 naive b TGFB1 TGFbR1_R2 0.000957 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR… TGFb Secreted … KEGG: h… +#> 6 cd4 tem b TGFB1 TGFbR1_R2 0.00242 0.76 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR… TGFb Secreted … KEGG: h… +#> # ℹ 5 more variables: pathway_prob , pathway_pval , sample_id , interaction_count , +#> # interaction_weight ``` ### Extract only a subset of genes @@ -496,15 +524,15 @@ single_cell_cpm <- #> ℹ Synchronising files #> ℹ Reading files. #> -Reading cpm ■■■■■■■ 20% | ETA: 5s +Reading cpm ■■■■■■■ 20% | ETA: 4s Reading cpm ■■■■■■■■■■ 30% | ETA: 4s -Reading cpm ■■■■■■■■■■■■■ 40% | ETA: 4s +Reading cpm ■■■■■■■■■■■■■ 40% | ETA: 3s Reading cpm ■■■■■■■■■■■■■■■■ 50% | ETA: 3s -Reading cpm ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 3s +Reading cpm ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 2s Reading cpm ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 2s @@ -516,27 +544,27 @@ Reading cpm ■■■■■■■■■■■■■■■■■■■■■■ ℹ Compiling Experiment. single_cell_cpm -#> # A SingleCellExperiment-tibble abstraction: 2,806 × 60 -#> # [90mFeatures=1 | Cells=2806 | 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 nCount_RNA -#> -#> 1 80_1 zz-!e5_XAo 842c6f5d-4a94… 1de3f3ba… 1de3f3… "" 7fabaf1c-52fd-4… 14600 breast 1749 10.8 -#> 2 81_1 -mb&DWckf( 842c6f5d-4a94… 1de3f3ba… 1de3f3… "" 7fabaf1c-52fd-4… 14600 breast 1993 12.4 -#> 3 72_1 8wGs7JgUjj 842c6f5d-4a94… 6b194412… 6b1944… "" b3ff1aad-40fd-4… 14600 breast 2548 13.1 -#> 4 75_1 F9G7A+GgjA 842c6f5d-4a94… db5a69ed… db5a69… "" 49beb83c-66a1-4… 14600 breast 1291 10.2 -#> 5 73_1 z_=CTOs4{z 842c6f5d-4a94… 4b5e66fa… 4b5e66… "" 04983012-bb56-4… 14600 breast 2866 10.3 -#> 6 74_1 fNzorxA`Mf 842c6f5d-4a94… 4b5e66fa… 4b5e66… "" 04983012-bb56-4… 14600 breast 1942 7.58 -#> 7 76_1 bTlx!HK=oS 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 1671 9.65 -#> 8 77_1 E4g5+)v;AV 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 2340 11.9 -#> 9 78_1 +q?29B%2nH 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 1714 13.2 -#> 10 79_1 zuJ#MBMWy; 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 1506 12.3 +#> # A SingleCellExperiment-tibble abstraction: 2,806 × 54 +#> # Features=1 | Cells=2806 | Assays=cpm +#> .cell observation_joinid dataset_id sample_id donor_id age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA empty_droplet +#> +#> 1 76_1 bTlx!HK=oS 842c6f5d-… 52ab9222… P58 14600 breast 1671 9.65 FALSE +#> 2 77_1 E4g5+)v;AV 842c6f5d-… 52ab9222… P58 14600 breast 2340 11.9 FALSE +#> 3 78_1 +q?29B%2nH 842c6f5d-… 52ab9222… P58 14600 breast 1714 13.2 FALSE +#> 4 79_1 zuJ#MBMWy; 842c6f5d-… 52ab9222… P58 14600 breast 1506 12.3 FALSE +#> 5 72_1 8wGs7JgUjj 842c6f5d-… 6b194412… P39 14600 breast 2548 13.1 FALSE +#> 6 75_1 F9G7A+GgjA 842c6f5d-… db5a69ed… P40 14600 breast 1291 10.2 FALSE +#> 7 80_1 zz-!e5_XAo 842c6f5d-… 1de3f3ba… P58 14600 breast 1749 10.8 FALSE +#> 8 81_1 -mb&DWckf( 842c6f5d-… 1de3f3ba… P58 14600 breast 1993 12.4 FALSE +#> 9 73_1 z_=CTOs4{z 842c6f5d-… 4b5e66fa… P39 14600 breast 2866 10.3 FALSE +#> 10 74_1 fNzorxA`Mf 842c6f5d-… 4b5e66fa… P39 14600 breast 1942 7.58 FALSE #> # ℹ 2,796 more rows #> # ℹ abbreviated name: ¹​nFeature_expressed_in_sample -#> # ℹ 48 more variables: empty_droplet , cell_type_unified_ensemble , is_immune , subsets_Mito_percent , subsets_Ribo_percent , -#> # high_mitochondrion , high_ribosome , scDblFinder.class , sample_chunk , cell_chunk , sample_pseudobulk_chunk , -#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , inverse_transform , -#> # alive , cell_annotation_blueprint_singler , cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , -#> # low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , atlas_id , dataset_version_id , collection_id , … +#> # ℹ 44 more variables: cell_type_unified_ensemble , is_immune , subsets_Mito_percent , +#> # subsets_Ribo_percent , high_mitochondrion , high_ribosome , alive , scDblFinder.class , +#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , +#> # nfeature_expressed_thresh , inverse_transform , cell_annotation_blueprint_singler , +#> # cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , … ``` ### Extract the counts as a Seurat object @@ -559,19 +587,19 @@ seurat_counts <- #> ℹ Synchronising files #> ℹ Reading files. #> -Reading counts ■■■■■■■ 20% | ETA: 5s +Reading counts ■■■■■■■ 20% | ETA: 4s -Reading counts ■■■■■■■■■■ 30% | ETA: 5s +Reading counts ■■■■■■■■■■ 30% | ETA: 4s -Reading counts ■■■■■■■■■■■■■ 40% | ETA: 4s +Reading counts ■■■■■■■■■■■■■ 40% | ETA: 3s Reading counts ■■■■■■■■■■■■■■■■ 50% | ETA: 3s -Reading counts ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 3s +Reading counts ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 2s -Reading counts ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 3s +Reading counts ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 2s -Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■ 80% | ETA: 2s +Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■ 80% | ETA: 1s Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 90% | ETA: 1s @@ -579,10 +607,27 @@ Reading counts ■■■■■■■■■■■■■■■■■■■■■ ℹ Compiling Experiment. seurat_counts -#> An object of class Seurat -#> 33145 features across 2806 samples within 1 assay -#> Active assay: originalexp (33145 features, 0 variable features) -#> 2 layers present: counts, data +#> # A Seurat-tibble abstraction: 2,806 × 59 +#> # Features=33145 | Cells=2806 | Active assay=counts | Assays=counts +#> .cell orig.ident nCount_originalexp nFeature_originalexp observation_joinid dataset_id sample_id donor_id age_days +#> +#> 1 73_1 SeuratProject 14.1 2958 z_=CTOs4{z 842c6f5d-4a94-4ee… 4b5e66fa… P39 14600 +#> 2 74_1 SeuratProject 14.2 2035 fNzorxA`Mf 842c6f5d-4a94-4ee… 4b5e66fa… P39 14600 +#> 3 76_1 SeuratProject 15.5 1759 bTlx!HK=oS 842c6f5d-4a94-4ee… 52ab9222… P58 14600 +#> 4 77_1 SeuratProject 15.4 2434 E4g5+)v;AV 842c6f5d-4a94-4ee… 52ab9222… P58 14600 +#> 5 78_1 SeuratProject 15.3 1798 +q?29B%2nH 842c6f5d-4a94-4ee… 52ab9222… P58 14600 +#> 6 79_1 SeuratProject 15.4 1595 zuJ#MBMWy; 842c6f5d-4a94-4ee… 52ab9222… P58 14600 +#> 7 1_1 SeuratProject 15.3 3493 I8a42<8st4 842c6f5d-4a94-4ee… 184fa234… P65 14600 +#> 8 80_1 SeuratProject 15.5 1837 zz-!e5_XAo 842c6f5d-4a94-4ee… 1de3f3ba… P58 14600 +#> 9 81_1 SeuratProject 15.2 2082 -mb&DWckf( 842c6f5d-4a94-4ee… 1de3f3ba… P58 14600 +#> 10 72_1 SeuratProject 18.0 2642 8wGs7JgUjj 842c6f5d-4a94-4ee… 6b194412… P39 14600 +#> # ℹ 2,796 more rows +#> # ℹ 50 more variables: tissue_groups , nFeature_expressed_in_sample , nCount_RNA , empty_droplet , +#> # cell_type_unified_ensemble , is_immune , subsets_Mito_percent , subsets_Ribo_percent , +#> # high_mitochondrion , high_ribosome , alive , scDblFinder.class , file_id_cellNexus_single_cell , +#> # file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , inverse_transform , +#> # cell_annotation_blueprint_singler , cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , +#> # ethnicity_flagging_score , low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , … ``` By default, data is downloaded to `get_default_cache_dir()` output. If @@ -808,25 +853,28 @@ get_metadata( dplyr::filter(file_id_cellNexus_single_cell %in% c(file_id_from_cloud, file_id_local)) |> dplyr::select(cell_id, sample_id, dataset_id, cell_type_unified_ensemble, atlas_id, file_id_cellNexus_single_cell) |> get_single_cell_experiment(cache_directory = local_cache) +#> 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/tmp/RtmpE4YcCe/sample_hca2024_v2.3.2.parquet #> ℹ Realising metadata. #> ℹ Synchronising files #> ℹ Reading files. #> ℹ Compiling Experiment. #> # A SingleCellExperiment-tibble abstraction: 500 × 7 -#> # [90mFeatures=13132 | Cells=500 | Assays=counts[0m -#> .cell sample_id dataset_id cell_type_unified_ensemble atlas_id file_id_cellNexus_single_cell original_cell_ -#> -#> 1 AAACATACAACCAC_1 pbmc3k pbmc3k Memory CD4 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACATACAACCAC -#> 2 AAACATTGAGCTAC_1 pbmc3k pbmc3k B cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACATTGAGCTAC -#> 3 AAACATTGATCAGC_1 pbmc3k pbmc3k Memory CD4 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACATTGATCAGC -#> 4 AAACCGTGCTTCCG_1 pbmc3k pbmc3k CD14+ Mono cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACCGTGCTTCCG -#> 5 AAACCGTGTATGCG_1 pbmc3k pbmc3k NK cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACCGTGTATGCG -#> 6 AAACGCACTGGTAC_1 pbmc3k pbmc3k Memory CD4 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACGCACTGGTAC -#> 7 AAACGCTGACCAGT_1 pbmc3k pbmc3k CD8 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACGCTGACCAGT -#> 8 AAACGCTGGTTCTT_1 pbmc3k pbmc3k CD8 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACGCTGGTTCTT -#> 9 AAACGCTGTAGCCA_1 pbmc3k pbmc3k Naive CD4 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACGCTGTAGCCA -#> 10 AAACGCTGTTTCTG_1 pbmc3k pbmc3k FCGR3A+ Mono cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACGCTGTTTCTG +#> # Features=13132 | Cells=500 | Assays=counts +#> .cell sample_id dataset_id cell_type_unified_ensemble atlas_id file_id_cellNexus_sing…¹ original_cell_ +#> +#> 1 AAACATACAACCAC_1 pbmc3k pbmc3k Memory CD4 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06… AAACATACAACCAC +#> 2 AAACATTGAGCTAC_1 pbmc3k pbmc3k B cellxgene/03-10-2025 67e196a3c4e145151fc9e06… AAACATTGAGCTAC +#> 3 AAACATTGATCAGC_1 pbmc3k pbmc3k Memory CD4 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06… AAACATTGATCAGC +#> 4 AAACCGTGCTTCCG_1 pbmc3k pbmc3k CD14+ Mono cellxgene/03-10-2025 67e196a3c4e145151fc9e06… AAACCGTGCTTCCG +#> 5 AAACCGTGTATGCG_1 pbmc3k pbmc3k NK cellxgene/03-10-2025 67e196a3c4e145151fc9e06… AAACCGTGTATGCG +#> 6 AAACGCACTGGTAC_1 pbmc3k pbmc3k Memory CD4 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06… AAACGCACTGGTAC +#> 7 AAACGCTGACCAGT_1 pbmc3k pbmc3k CD8 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06… AAACGCTGACCAGT +#> 8 AAACGCTGGTTCTT_1 pbmc3k pbmc3k CD8 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06… AAACGCTGGTTCTT +#> 9 AAACGCTGTAGCCA_1 pbmc3k pbmc3k Naive CD4 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06… AAACGCTGTAGCCA +#> 10 AAACGCTGTTTCTG_1 pbmc3k pbmc3k FCGR3A+ Mono cellxgene/03-10-2025 67e196a3c4e145151fc9e06… AAACGCTGTTTCTG #> # ℹ 490 more rows +#> # ℹ abbreviated name: ¹​file_id_cellNexus_single_cell ``` # Cell metadata @@ -872,9 +920,9 @@ sessionInfo() #> 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 +#> [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) @@ -883,47 +931,102 @@ sessionInfo() #> [1] stats4 stats graphics grDevices utils datasets methods base #> #> other attached packages: -#> [1] BiocStyle_2.38.0 RcppSpdlog_0.0.28 ggplot2_4.0.2 SummarizedExperiment_1.40.0 Biobase_2.70.0 -#> [6] GenomicRanges_1.62.1 Seqinfo_1.0.0 IRanges_2.44.0 S4Vectors_0.49.1-1 BiocGenerics_0.56.0 -#> [11] generics_0.1.4 MatrixGenerics_1.22.0 matrixStats_1.5.0 shiny_1.13.0 anndataR_1.0.2 -#> [16] cellNexus_0.99.27 testthat_3.3.2 dplyr_1.2.1 +#> [1] cellNexus_0.99.30 RcppSpdlog_0.0.28 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] fs_2.0.1 spatstat.sparse_3.1-0 fontawesome_0.5.3 devtools_2.5.0 httr_1.4.8 -#> [6] RColorBrewer_1.1-3 tools_4.5.3 sctransform_0.4.3 backports_1.5.1 utf8_1.2.6 -#> [11] R6_2.6.1 DT_0.34.0 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 gridExtra_2.3 nanoarrow_0.8.0 -#> [21] progressr_0.19.0 cli_3.6.6 spatstat.explore_3.8-0 fastDummies_1.7.5 sass_0.4.10 -#> [26] Seurat_5.5.0.9002 arrow_23.0.1.2 S7_0.2.1-1 spatstat.data_3.1-9 ggridges_0.5.7 -#> [31] pbapply_1.7-4 commonmark_2.0.0 R.utils_2.13.0 parallelly_1.46.1 sessioninfo_1.2.3 -#> [36] rstudioapi_0.18.0 ica_1.0-3 spatstat.random_3.4-5 Matrix_1.7-4 fansi_1.0.7 -#> [41] waldo_0.6.2 rclipboard_0.2.1 abind_1.4-8 R.methodsS3_1.8.2 lifecycle_1.0.5 -#> [46] yaml_2.3.12 rhdf5_2.54.1 SparseArray_1.10.10 Rtsne_0.17 grid_4.5.3 -#> [51] blob_1.3.0 promises_1.5.0 dir.expiry_1.18.0 miniUI_0.1.2 lattice_0.22-9 -#> [56] cowplot_1.2.0 pillar_1.11.1 knitr_1.51 future.apply_1.20.2 codetools_0.2-20 -#> [61] glue_1.8.0 tiledb_0.33.1 spatstat.univar_3.1-7 data.table_1.18.2.1 tidySingleCellExperiment_1.20.1 -#> [66] vctrs_0.7.3 png_0.1-9 spam_2.11-3 gtable_0.3.6 aws.s3_0.3.22 -#> [71] assertthat_0.2.1 cachem_1.1.0 xfun_0.57 S4Arrays_1.10.1 mime_0.13 -#> [76] rsconnect_1.8.0 survival_3.8-6 SingleCellExperiment_1.32.0 ellipsis_0.3.3 fitdistrplus_1.2-6 -#> [81] ROCR_1.0-12 tiledbsoma_2.1.2 nlme_3.1-168 RcppCCTZ_0.2.14 usethis_3.2.1 -#> [86] bit64_4.6.0-1 filelock_1.0.3 RcppAnnoy_0.0.23 GenomeInfoDb_1.46.2 rprojroot_2.1.1 -#> [91] R.cache_0.17.0 bslib_0.10.0 irlba_2.3.7 KernSmooth_2.23-26 otel_0.2.0 -#> [96] DBI_1.3.0 zellkonverter_1.20.1 duckdb_1.4.3 tidyselect_1.2.1 cellxgene.census_1.16.1 -#> [101] bit_4.6.0 compiler_4.5.3 curl_7.0.0 rjsoncons_1.3.2 h5mread_1.2.1 -#> [106] xml2_1.5.2 nanotime_0.3.13 desc_1.4.3 DelayedArray_0.36.1 plotly_4.12.0 -#> [111] bookdown_0.46 checkmate_2.3.4 scales_1.4.0 lmtest_0.9-40 spdl_0.0.5 -#> [116] stringr_1.6.0 digest_0.6.39 goftest_1.2-3 spatstat.utils_3.2-2 rmarkdown_2.31 -#> [121] basilisk_1.22.0 XVector_0.50.0 htmltools_0.5.9 pkgconfig_2.0.3 base64enc_0.1-6 -#> [126] dbplyr_2.5.2 fastmap_1.2.0 rlang_1.2.0 htmlwidgets_1.6.4 UCSC.utils_1.6.1 -#> [131] farver_2.1.2 jquerylib_0.1.4 zoo_1.8-15 jsonlite_2.0.0 R.oo_1.27.1 -#> [136] magrittr_2.0.5 dotCall64_1.2 patchwork_1.3.2 Rhdf5lib_1.32.0 Rcpp_1.1.1-1 -#> [141] reticulate_1.46.0 stringi_1.8.7 brio_1.1.5 MASS_7.3-65 plyr_1.8.9 -#> [146] pkgbuild_1.4.8 parallel_4.5.3 listenv_0.10.1 ggrepel_0.9.8 forcats_1.0.1 -#> [151] deldir_2.0-4 splines_4.5.3 tensor_1.5.1 igraph_2.2.3 cellxgenedp_1.14.0 -#> [156] spatstat.geom_3.7-3 RcppHNSW_0.6.0 reshape2_1.4.5 pkgload_1.5.1 ttservice_0.5.3 -#> [161] evaluate_1.0.5 SeuratObject_5.4.0 BiocManager_1.30.27 httpuv_1.6.17 RANN_2.6.2 -#> [166] tidyr_1.3.2 purrr_1.2.2 polyclip_1.10-7 future_1.70.0 scattermore_1.2 -#> [171] xtable_1.8-8 RSpectra_0.16-2 roxygen2_7.3.3 later_1.4.8 viridisLite_0.4.3 -#> [176] tibble_3.3.1 memoise_2.0.1 aws.signature_0.6.0 cluster_2.1.8.2 shinyWidgets_0.9.1 -#> [181] globals_0.19.1 +#> [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] forcats_1.0.1 prettyunits_1.2.0 boot_1.3-32 +#> [208] beachmat_2.26.0 listenv_0.10.1 Rcpp_1.1.1-1 +#> [211] edgeR_4.8.2 roxygen2_7.3.3 BiocSingular_1.26.1 +#> [214] tensor_1.5.1 usethis_3.2.1 MASS_7.3-65 +#> [217] progress_1.2.3 uuid_1.2-2 BiocParallel_1.44.0 +#> [220] ggupset_0.4.1 nanotime_0.3.13 spatstat.random_3.4-5 +#> [223] R6_2.6.1 fastmap_1.2.0 vipor_0.4.7 +#> [226] ensembldb_2.34.0 ROCR_1.0-12 targets_1.12.0 +#> [229] rsvd_1.0.5 gtable_0.3.6 KernSmooth_2.23-26 +#> [232] miniUI_0.1.2 deldir_2.0-4 htmltools_0.5.9 +#> [235] bit64_4.6.0-1 spatstat.explore_3.8-0 lifecycle_1.0.5 +#> [238] S7_0.2.1-1 processx_3.8.7 nloptr_2.2.1 +#> [241] callr_3.7.6 restfulr_0.0.16 sass_0.4.10 +#> [244] vctrs_0.7.3 testthat_3.3.2 rsconnect_1.10.1 +#> [247] spatstat.geom_3.7-3 scran_1.38.1 sp_2.2-1 +#> [250] future.apply_1.20.2 bslib_0.10.0 pillar_1.11.1 +#> [253] GenomicFeatures_1.62.0 DropletUtils_1.30.0 cellxgene.census_1.16.1 +#> [256] collections_0.3.12 metapod_1.18.0 locfit_1.5-9.12 +#> [259] otel_0.2.0 BiocStyle_2.38.0 jsonlite_2.0.0 +#> [262] cigarillo_1.0.0 ``` diff --git a/index.md b/index.md index 2f7f0839..a9974963 100644 --- a/index.md +++ b/index.md @@ -69,8 +69,7 @@ Through harmonisation and curation, `cellNexus` adds columns that are not presen | `observation_joinid` | Cell ID join key linking metadata. | | `dataset_id` | Primary dataset identifier in the atlas. | | `sample_id` | Harmonised sample identifier. | -| `sample_` | Internal sample subdivision helper. | -| `sample_heuristic` | Internal sample subdivision helper. | +| `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. | @@ -83,9 +82,6 @@ Through harmonisation and curation, `cellNexus` adds columns that are not presen | `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`. | -| `sample_chunk ` | Internal sample subdivision chunks. | -| `cell_chunk ` | Internal cell subdivision chunks. | -| `sample_pseudobulk_chunk ` | Internal pseudobulk subdivision chunks. | | `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. | diff --git a/inst/NEWS.Rd b/inst/NEWS.Rd index 09071b1f..8123887e 100644 --- a/inst/NEWS.Rd +++ b/inst/NEWS.Rd @@ -1,6 +1,17 @@ \name{NEWS} \title{News for Package \pkg{cellNexus}} +\section{News in version 0.99.31}{ +\itemize{ + \item Added \code{donor_id} to harmonised metadata, enabling direct donor-level + queries across datasets. + \item Updated metadata parquet versions to \code{hca2024_v2.3.2} + \item Expanded and corrected documentation for metadata columns on website. + \item Removed internal columns (\code{sample_}, + \code{sample_heuristic}, \code{sample_chunk}, \code{cell_chunk}, + \code{sample_pseudobulk_chunk}) from documentation. +}} + \section{News in version 0.99.30}{ \itemize{ \item \code{get_seurat()} now correctly handles multiple assays and non-default diff --git a/man/get_metadata.Rd b/man/get_metadata.Rd index 99b4df43..2da37016 100644 --- a/man/get_metadata.Rd +++ b/man/get_metadata.Rd @@ -61,8 +61,7 @@ Field definitions for the CELLxGENE schema follow the Through harmonisation and curation we introduced custom columns not present in the original CELLxGENE metadata: -\code{cell_count}: Number of cells in a dataset. -\code{feature_count}: Number of genes in a dataset. +\code{sample_id}: Sample identifier. \code{age_days}: Donor age in days. \code{tissue_groups}: Coarse tissue grouping for analysis. \code{empty_droplet}: Whether a cell is called an empty droplet from expressed-gene count per sample (default threshold 200; targeted panels may differ). @@ -71,9 +70,15 @@ in the original CELLxGENE metadata: \code{cell_type_unified_ensemble}: Consensus immune identity from Azimuth and SingleR (Blueprint, Monaco). \code{cell_annotation_azimuth_l2}: Azimuth cell annotation. \code{cell_annotation_blueprint_singler}: SingleR annotation (Blueprint). -\code{cell_annotation_blueprint_monaco}: SingleR annotation (Monaco). -\code{is_immune}: Whether a cell is an immune cell. -\code{sample_heuristic}: Internal sample subdivision helper. +\code{cell_annotation_monaco_singler}: SingleR annotation (Monaco). +\code{subsets_Mito_percent}: Percent of each cell’s total counts coming from mitochondrial genes in a sample. +\code{subsets_Ribo_percent}: Percent of each cell’s total counts coming from ribosomal genes in a sample. +\code{high_mitochondrion}: TRUE if the cell’s mitochondrial percent exceeds the QC cutoff. +\code{high_ribosome}: TRUE if the cell’s ribosomal percent exceeds the QC cutoff. +\code{count_upper_bound}: Count capping threshold used in counts transformation. +\code{inverse_transform}: Transformation method used in pre-processing pipeline. +\code{nfeature_expressed_thresh}: Threshold of the number of expressed features per cell. +\code{is_immune}: Curated logical flag for immune-cell context. \code{file_id_cellNexus_single_cell}: Internal file id for single-cell layers. \code{file_id_cellNexus_pseudobulk}: Internal file id for pseudobulk layers. \code{sample_id}: Harmonised sample identifier. diff --git a/vignettes/cellNexus.Rmd b/vignettes/cellNexus.Rmd index 136a7f90..becf50ef 100644 --- a/vignettes/cellNexus.Rmd +++ b/vignettes/cellNexus.Rmd @@ -93,27 +93,27 @@ The following sections demonstrate the metadata, quality control, generation of ``` r metadata <- get_metadata() metadata -#> # Source: SQL [?? x 37] +#> # Source: SQL [?? x 31] #> # Database: DuckDB 1.4.3 [unknown@Linux 5.14.0-570.123.1.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 nFeature_expressed_i…¹ nCount_RNA -#> -#> 1 1 `;+Wwc*oS9 574e9f9e-f8… b290d7ef… b290d7… "" BPH556PrGA2_Fco… 25915 prostate 1025 113. -#> 2 1 s<8rT5qe3X 574e9f9e-f8… b290d7ef… b290d7… "" BPH556PrGA2_Fco… 25915 prostate 2586 77.6 -#> 3 2 Se=|eIq*={ 574e9f9e-f8… b290d7ef… b290d7… "" BPH556PrGA2_Fco… 25915 prostate 992 57.6 -#> 4 2 dcNO`ReB5o 574e9f9e-f8… b290d7ef… b290d7… "" BPH556PrGA2_Fco… 25915 prostate 2002 163. -#> 5 3 F_Jf~Pzj BPH556PrGA2_Fco… 25915 prostate 730 93.6 -#> 6 16 i(U>N;cU4_ 574e9f9e-f8… b290d7ef… b290d7… "" BPH556PrGA2_Fco… 25915 prostate 718 342. -#> 7 4 MK~^fbPVCl 574e9f9e-f8… b290d7ef… b290d7… "" BPH556PrGA2_Fco… 25915 prostate 846 99.8 -#> 8 4 J+o&MJmtR5 574e9f9e-f8… b290d7ef… b290d7… "" BPH556PrGA2_Fco… 25915 prostate 829 123. -#> 9 12 $LL!IWeW`F 574e9f9e-f8… b290d7ef… b290d7… "" BPH556PrGA2_Fco… 25915 prostate 2482 61.8 -#> 10 18 $zB;$PErEP 574e9f9e-f8… b290d7ef… b290d7… "" BPH556PrGA2_Fco… 25915 prostate 828 86.6 -#> # ℹ more rows -#> # ℹ abbreviated name: ¹​nFeature_expressed_in_sample -#> # ℹ 25 more variables: empty_droplet , cell_type_unified_ensemble , is_immune , subsets_Mito_percent , subsets_Ribo_percent , -#> # high_mitochondrion , high_ribosome , scDblFinder.class , sample_chunk , cell_chunk , sample_pseudobulk_chunk , -#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , inverse_transform , -#> # alive , cell_annotation_blueprint_singler , cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , -#> # low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , atlas_id +#> cell_id observation_joinid dataset_id sample_id donor_id age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA +#> +#> 1 1 `;+Wwc*oS9 574e9f9e-f8b4-41ef-bf… b290d7ef… BPH556 25915 prostate 1025 113. +#> 2 1 s<8rT5qe3X 574e9f9e-f8b4-41ef-bf… b290d7ef… BPH556 25915 prostate 2586 77.6 +#> 3 2 Se=|eIq*={ 574e9f9e-f8b4-41ef-bf… b290d7ef… BPH556 25915 prostate 992 57.6 +#> 4 2 dcNO`ReB5o 574e9f9e-f8b4-41ef-bf… b290d7ef… BPH556 25915 prostate 2002 163. +#> 5 3 F_Jf~Pzj 6 16 i(U>N;cU4_ 574e9f9e-f8b4-41ef-bf… b290d7ef… BPH556 25915 prostate 718 342. +#> 7 4 MK~^fbPVCl 574e9f9e-f8b4-41ef-bf… b290d7ef… BPH556 25915 prostate 846 99.8 +#> 8 4 J+o&MJmtR5 574e9f9e-f8b4-41ef-bf… b290d7ef… BPH556 25915 prostate 829 123. +#> 9 12 $LL!IWeW`F 574e9f9e-f8b4-41ef-bf… b290d7ef… BPH556 25915 prostate 2482 61.8 +#> 10 18 $zB;$PErEP 574e9f9e-f8b4-41ef-bf… b290d7ef… BPH556 25915 prostate 828 86.6 +#> # ℹ more rows +#> # ℹ abbreviated name: ¹​nFeature_expressed_in_sample +#> # ℹ 22 more variables: empty_droplet , cell_type_unified_ensemble , is_immune , subsets_Mito_percent , +#> # subsets_Ribo_percent , high_mitochondrion , high_ribosome , alive , scDblFinder.class , +#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , +#> # nfeature_expressed_thresh , inverse_transform , cell_annotation_blueprint_singler , +#> # cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , … ``` ## Quality control @@ -140,8 +140,8 @@ Original Census annotations can be retrieved by the function `get_census_metadat ``` r census_metadata <- cellNexus:::get_census_metadata("2024-07-01") -#> ℹ Opening Census version 2024-07-01. -#> ℹ Reading Census obs table. +#> ℹ Opening Census version 2024-07-01. +#> ℹ Reading Census obs table. con <- dbplyr::remote_con(metadata) @@ -165,17 +165,17 @@ metadata |> #> # Database: DuckDB 1.4.3 [unknown@Linux 5.14.0-570.123.1.el9_6.x86_64:R 4.5.3/:memory:] #> tissue cell_type_unified_ensemble #> -#> 1 subcutaneous adipose tissue nkt -#> 2 subcutaneous adipose tissue muscle -#> 3 subcutaneous adipose tissue macrophage -#> 4 subcutaneous adipose tissue cd8 tem -#> 5 subcutaneous adipose tissue cd16 mono -#> 6 subcutaneous adipose tissue cd14 mono -#> 7 subcutaneous adipose tissue b naive -#> 8 subcutaneous adipose tissue cd4 th1/th17 em -#> 9 subcutaneous adipose tissue granulocyte -#> 10 subcutaneous adipose tissue cd4 naive -#> # ℹ more rows +#> 1 transition zone of prostate cdc +#> 2 transition zone of prostate epithelial +#> 3 transition zone of prostate b memory +#> 4 transition zone of prostate endothelial +#> 5 transition zone of prostate monocytic +#> 6 transition zone of prostate dc +#> 7 transition zone of prostate nk +#> 8 transition zone of prostate other +#> 9 transition zone of prostate stromal +#> 10 transition zone of prostate b +#> # ℹ more rows ``` ## Download single-cell RNA sequencing counts @@ -193,53 +193,33 @@ single_cell_counts <- cell_type == "T cell" ) |> get_single_cell_experiment() -#> ℹ Realising metadata. -#> ℹ Synchronising files -#> ℹ Reading files. -#> -Reading counts â– â– â– â–  10% | ETA: 11s - -Reading counts â– â– â– â– â– â– â–  20% | ETA: 7s - -Reading counts â– â– â– â– â– â– â– â– â– â–  30% | ETA: 6s - -Reading counts â– â– â– â– â– â– â– â– â– â– â– â– â–  40% | ETA: 5s - -Reading counts â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  50% | ETA: 4s - -Reading counts â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  60% | ETA: 3s - -Reading counts â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  70% | ETA: 2s - -Reading counts â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  80% | ETA: 1s - -Reading counts â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  90% | ETA: 1s - - -ℹ Compiling Experiment. +#> ℹ Realising metadata. +#> ℹ Synchronising files +#> ℹ Reading files. +#> Reading counts ■■■■■■■ 20% | ETA: 9s Reading counts ■■■■■■■■■■ 30% | ETA: 7s Reading counts ■■■■■■■■■■■■■ 40% | ETA: 5s Reading counts ■■■■■■■■■■■■■■■■ 50% | ETA: 4s Reading counts ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 3s Reading counts ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 2s Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■ 80% | ETA: 1s Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 90% | ETA: 1s ℹ Compiling Experiment. single_cell_counts -#> # A SingleCellExperiment-tibble abstraction: 2,806 × 60 -#> # [90mFeatures=33145 | Cells=2806 | Assays=counts[0m -#> .cell observation_joinid dataset_id sample_id sample_ experiment___ run_from_cell_id sample_heuristic age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA -#> -#> 1 80_1 zz-!e5_XAo 842c6f5d-4a94… 1de3f3ba… 1de3f3… "" 7fabaf1c-52fd-4… 14600 breast 1749 10.8 -#> 2 81_1 -mb&DWckf( 842c6f5d-4a94… 1de3f3ba… 1de3f3… "" 7fabaf1c-52fd-4… 14600 breast 1993 12.4 -#> 3 73_1 z_=CTOs4{z 842c6f5d-4a94… 4b5e66fa… 4b5e66… "" 04983012-bb56-4… 14600 breast 2866 10.3 -#> 4 74_1 fNzorxA`Mf 842c6f5d-4a94… 4b5e66fa… 4b5e66… "" 04983012-bb56-4… 14600 breast 1942 7.58 -#> 5 76_1 bTlx!HK=oS 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 1671 9.65 -#> 6 77_1 E4g5+)v;AV 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 2340 11.9 -#> 7 78_1 +q?29B%2nH 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 1714 13.2 -#> 8 79_1 zuJ#MBMWy; 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 1506 12.3 -#> 9 1_1 I8a42<8st4 842c6f5d-4a94… 184fa234… 184fa2… "" c2aa4d8d-e9df-4… 14600 breast 3395 11.8 -#> 10 72_1 8wGs7JgUjj 842c6f5d-4a94… 6b194412… 6b1944… "" b3ff1aad-40fd-4… 14600 breast 2548 13.1 -#> # ℹ 2,796 more rows -#> # ℹ abbreviated name: ¹​nFeature_expressed_in_sample -#> # ℹ 48 more variables: empty_droplet , cell_type_unified_ensemble , is_immune , subsets_Mito_percent , subsets_Ribo_percent , -#> # high_mitochondrion , high_ribosome , scDblFinder.class , sample_chunk , cell_chunk , sample_pseudobulk_chunk , -#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , inverse_transform , -#> # alive , cell_annotation_blueprint_singler , cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , -#> # low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , atlas_id , dataset_version_id , collection_id , … +#> # A SingleCellExperiment-tibble abstraction: 2,806 × 54 +#> # Features=33145 | Cells=2806 | Assays=counts +#> .cell observation_joinid dataset_id sample_id donor_id age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA empty_droplet +#> +#> 1 80_1 zz-!e5_XAo 842c6f5d-… 1de3f3ba… P58 14600 breast 1749 10.8 FALSE +#> 2 81_1 -mb&DWckf( 842c6f5d-… 1de3f3ba… P58 14600 breast 1993 12.4 FALSE +#> 3 73_1 z_=CTOs4{z 842c6f5d-… 4b5e66fa… P39 14600 breast 2866 10.3 FALSE +#> 4 74_1 fNzorxA`Mf 842c6f5d-… 4b5e66fa… P39 14600 breast 1942 7.58 FALSE +#> 5 1_1 I8a42<8st4 842c6f5d-… 184fa234… P65 14600 breast 3395 11.8 FALSE +#> 6 72_1 8wGs7JgUjj 842c6f5d-… 6b194412… P39 14600 breast 2548 13.1 FALSE +#> 7 75_1 F9G7A+GgjA 842c6f5d-… db5a69ed… P40 14600 breast 1291 10.2 FALSE +#> 8 76_1 bTlx!HK=oS 842c6f5d-… 52ab9222… P58 14600 breast 1671 9.65 FALSE +#> 9 77_1 E4g5+)v;AV 842c6f5d-… 52ab9222… P58 14600 breast 2340 11.9 FALSE +#> 10 78_1 +q?29B%2nH 842c6f5d-… 52ab9222… P58 14600 breast 1714 13.2 FALSE +#> # ℹ 2,796 more rows +#> # ℹ abbreviated name: ¹​nFeature_expressed_in_sample +#> # ℹ 44 more variables: cell_type_unified_ensemble , is_immune , subsets_Mito_percent , +#> # subsets_Ribo_percent , high_mitochondrion , high_ribosome , alive , scDblFinder.class , +#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , +#> # nfeature_expressed_thresh , inverse_transform , cell_annotation_blueprint_singler , +#> # cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , … ``` ### Query counts scaled per million @@ -255,51 +235,33 @@ single_cell_cpm <- cell_type == "T cell" ) |> get_single_cell_experiment(assays = "cpm") -#> ℹ Realising metadata. -#> ℹ Synchronising files -#> ℹ Reading files. -#> -Reading cpm â– â– â– â– â– â– â–  20% | ETA: 5s - -Reading cpm â– â– â– â– â– â– â– â– â– â–  30% | ETA: 4s - -Reading cpm â– â– â– â– â– â– â– â– â– â– â– â– â–  40% | ETA: 4s - -Reading cpm â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  50% | ETA: 3s - -Reading cpm â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  60% | ETA: 3s - -Reading cpm â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  70% | ETA: 2s - -Reading cpm â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  80% | ETA: 1s - -Reading cpm â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  90% | ETA: 1s - - -ℹ Compiling Experiment. +#> ℹ Realising metadata. +#> ℹ Synchronising files +#> ℹ Reading files. +#> Reading cpm ■■■■■■■ 20% | ETA: 6s Reading cpm ■■■■■■■■■■ 30% | ETA: 4s Reading cpm ■■■■■■■■■■■■■ 40% | ETA: 4s Reading cpm ■■■■■■■■■■■■■■■■ 50% | ETA: 3s Reading cpm ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 3s Reading cpm ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 2s Reading cpm ■■■■■■■■■■■■■■■■■■■■■■■■■ 80% | ETA: 1s Reading cpm ■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 90% | ETA: 1s ℹ Compiling Experiment. single_cell_cpm -#> # A SingleCellExperiment-tibble abstraction: 2,806 × 60 -#> # [90mFeatures=33145 | Cells=2806 | 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 nCount_RNA -#> -#> 1 80_1 zz-!e5_XAo 842c6f5d-4a94… 1de3f3ba… 1de3f3… "" 7fabaf1c-52fd-4… 14600 breast 1749 10.8 -#> 2 81_1 -mb&DWckf( 842c6f5d-4a94… 1de3f3ba… 1de3f3… "" 7fabaf1c-52fd-4… 14600 breast 1993 12.4 -#> 3 73_1 z_=CTOs4{z 842c6f5d-4a94… 4b5e66fa… 4b5e66… "" 04983012-bb56-4… 14600 breast 2866 10.3 -#> 4 74_1 fNzorxA`Mf 842c6f5d-4a94… 4b5e66fa… 4b5e66… "" 04983012-bb56-4… 14600 breast 1942 7.58 -#> 5 76_1 bTlx!HK=oS 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 1671 9.65 -#> 6 77_1 E4g5+)v;AV 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 2340 11.9 -#> 7 78_1 +q?29B%2nH 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 1714 13.2 -#> 8 79_1 zuJ#MBMWy; 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 1506 12.3 -#> 9 1_1 I8a42<8st4 842c6f5d-4a94… 184fa234… 184fa2… "" c2aa4d8d-e9df-4… 14600 breast 3395 11.8 -#> 10 72_1 8wGs7JgUjj 842c6f5d-4a94… 6b194412… 6b1944… "" b3ff1aad-40fd-4… 14600 breast 2548 13.1 -#> # ℹ 2,796 more rows -#> # ℹ abbreviated name: ¹​nFeature_expressed_in_sample -#> # ℹ 48 more variables: empty_droplet , cell_type_unified_ensemble , is_immune , subsets_Mito_percent , subsets_Ribo_percent , -#> # high_mitochondrion , high_ribosome , scDblFinder.class , sample_chunk , cell_chunk , sample_pseudobulk_chunk , -#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , inverse_transform , -#> # alive , cell_annotation_blueprint_singler , cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , -#> # low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , atlas_id , dataset_version_id , collection_id , … +#> # A SingleCellExperiment-tibble abstraction: 2,806 × 54 +#> # Features=33145 | Cells=2806 | Assays=cpm +#> .cell observation_joinid dataset_id sample_id donor_id age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA empty_droplet +#> +#> 1 76_1 bTlx!HK=oS 842c6f5d-… 52ab9222… P58 14600 breast 1671 9.65 FALSE +#> 2 77_1 E4g5+)v;AV 842c6f5d-… 52ab9222… P58 14600 breast 2340 11.9 FALSE +#> 3 78_1 +q?29B%2nH 842c6f5d-… 52ab9222… P58 14600 breast 1714 13.2 FALSE +#> 4 79_1 zuJ#MBMWy; 842c6f5d-… 52ab9222… P58 14600 breast 1506 12.3 FALSE +#> 5 1_1 I8a42<8st4 842c6f5d-… 184fa234… P65 14600 breast 3395 11.8 FALSE +#> 6 72_1 8wGs7JgUjj 842c6f5d-… 6b194412… P39 14600 breast 2548 13.1 FALSE +#> 7 75_1 F9G7A+GgjA 842c6f5d-… db5a69ed… P40 14600 breast 1291 10.2 FALSE +#> 8 80_1 zz-!e5_XAo 842c6f5d-… 1de3f3ba… P58 14600 breast 1749 10.8 FALSE +#> 9 81_1 -mb&DWckf( 842c6f5d-… 1de3f3ba… P58 14600 breast 1993 12.4 FALSE +#> 10 73_1 z_=CTOs4{z 842c6f5d-… 4b5e66fa… P39 14600 breast 2866 10.3 FALSE +#> # ℹ 2,796 more rows +#> # ℹ abbreviated name: ¹​nFeature_expressed_in_sample +#> # ℹ 44 more variables: cell_type_unified_ensemble , is_immune , subsets_Mito_percent , +#> # subsets_Ribo_percent , high_mitochondrion , high_ribosome , alive , scDblFinder.class , +#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , +#> # nfeature_expressed_thresh , inverse_transform , cell_annotation_blueprint_singler , +#> # cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , … ``` ### Query SCT normalised counts @@ -314,65 +276,38 @@ single_cell_sct <- cell_type == "T cell" ) |> get_single_cell_experiment(assays = "sct") -#> ℹ Realising metadata. -#> ℹ Synchronising files -#> ℹ Reading files. +#> ℹ Realising metadata. +#> ℹ Synchronising files +#> ℹ Reading files. #> ! The number of cells in the SingleCellExperiment will be less than the number of cells you have selected from the metadata. Are cell IDs duplicated? Or, do cell IDs correspond to the counts file? -#> -Reading sct â– â– â– â– â– â– â–  20% | ETA: 5s - -Reading sct â– â– â– â– â– â– â– â– â– â–  30% | ETA: 4s - -Reading sct â– â– â– â– â– â– â– â– â– â– â– â– â–  40% | ETA: 4s - - -! The number of cells in the SingleCellExperiment will be less than the number of cells you have selected from the metadata. Are cell IDs duplicated? Or, do cell IDs correspond to the counts file? -#> Reading sct â– â– â– â– â– â– â– â– â– â– â– â– â–  40% | ETA: 4s - -Reading sct â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  50% | ETA: 3s - - -! The number of cells in the SingleCellExperiment will be less than the number of cells you have selected from the metadata. Are cell IDs duplicated? Or, do cell IDs correspond to the counts file? -#> Reading sct â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  50% | ETA: 3s - -Reading sct â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  60% | ETA: 3s - -Reading sct â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  70% | ETA: 2s - - -! The number of cells in the SingleCellExperiment will be less than the number of cells you have selected from the metadata. Are cell IDs duplicated? Or, do cell IDs correspond to the counts file? -#> Reading sct â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  70% | ETA: 2s - -Reading sct â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  80% | ETA: 1s - -Reading sct â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  90% | ETA: 1s - - -! cellNexus says: 1613 cell(s) from your metadata are absent from the SCT assay across 4 file(s). This is expected: SCT normalisation is run per sample and may fail for samples with very few cells or extreme count distributions. The returned object contains only cells from samples where SCT succeeded. Affected sample_id(s): 52ab92226337d36c306466eefe67f9c1, 765554078ca8d1eaf2712000c0df0d6f, 8940e0767e7eca1b72d37b4138be2276, a79912cb9aaa8d8c0b1a3cdcc9294f8c, 5e641a2218d1d8b91f638989626c89e0. -#> ℹ Compiling Experiment. +#> Reading sct ■■■■■■■ 20% | ETA: 5s Reading sct ■■■■■■■■■■ 30% | ETA: 4s Reading sct ■■■■■■■■■■■■■ 40% | ETA: 4s ! The number of cells in the SingleCellExperiment will be less than the number of cells you have selected from the metadata. Are cell IDs duplicated? Or, do cell IDs correspond to the counts file? +#> Reading sct ■■■■■■■■■■■■■ 40% | ETA: 4s Reading sct ■■■■■■■■■■■■■■■■ 50% | ETA: 3s ! The number of cells in the SingleCellExperiment will be less than the number of cells you have selected from the metadata. Are cell IDs duplicated? Or, do cell IDs correspond to the counts file? +#> Reading sct ■■■■■■■■■■■■■■■■ 50% | ETA: 3s Reading sct ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 2s Reading sct ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 2s ! The number of cells in the SingleCellExperiment will be less than the number of cells you have selected from the metadata. Are cell IDs duplicated? Or, do cell IDs correspond to the counts file? +#> Reading sct ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 2s Reading sct ■■■■■■■■■■■■■■■■■■■■■■■■■ 80% | ETA: 1s Reading sct ■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 90% | ETA: 1s ! cellNexus says: 1613 cell(s) from your metadata are absent from the SCT assay across 4 file(s). This is expected: SCT normalisation is run per sample and may fail for samples with very few cells or extreme count distributions. The returned object contains only cells from samples where SCT succeeded. Affected sample_id(s): 52ab92226337d36c306466eefe67f9c1, 765554078ca8d1eaf2712000c0df0d6f, 8940e0767e7eca1b72d37b4138be2276, a79912cb9aaa8d8c0b1a3cdcc9294f8c, 5e641a2218d1d8b91f638989626c89e0. +#> ℹ Compiling Experiment. single_cell_sct -#> # A SingleCellExperiment-tibble abstraction: 1,193 × 60 -#> # [90mFeatures=33145 | Cells=1193 | Assays=sct[0m -#> .cell observation_joinid dataset_id sample_id sample_ experiment___ run_from_cell_id sample_heuristic age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA -#> -#> 1 80_1 zz-!e5_XAo 842c6f5d-4a94… 1de3f3ba… 1de3f3… "" 7fabaf1c-52fd-4… 14600 breast 1749 10.8 -#> 2 81_1 -mb&DWckf( 842c6f5d-4a94… 1de3f3ba… 1de3f3… "" 7fabaf1c-52fd-4… 14600 breast 1993 12.4 -#> 3 73_1 z_=CTOs4{z 842c6f5d-4a94… 4b5e66fa… 4b5e66… "" 04983012-bb56-4… 14600 breast 2866 10.3 -#> 4 74_1 fNzorxA`Mf 842c6f5d-4a94… 4b5e66fa… 4b5e66… "" 04983012-bb56-4… 14600 breast 1942 7.58 -#> 5 1_1 I8a42<8st4 842c6f5d-4a94… 184fa234… 184fa2… "" c2aa4d8d-e9df-4… 14600 breast 3395 11.8 -#> 6 72_1 8wGs7JgUjj 842c6f5d-4a94… 6b194412… 6b1944… "" b3ff1aad-40fd-4… 14600 breast 2548 13.1 -#> 7 75_1 F9G7A+GgjA 842c6f5d-4a94… db5a69ed… db5a69… "" 49beb83c-66a1-4… 14600 breast 1291 10.2 -#> 8 1_2 >8f0}-gXFY 842c6f5d-4a94… 81d05f17… 81d05f… "" b866c1d4-3dfd-4… 14600 breast 2513 13.3 -#> 9 22_2 2lQ`<&l3-A 842c6f5d-4a94… 30967738… 309677… "" 7d4045ff-3f48-4… 14600 breast 2058 9.91 -#> 10 5_2 +p4uNj_7$S 842c6f5d-4a94… a91e6814… a91e68… "" 700a819c-03f9-4… 14600 breast 1870 11.1 -#> # ℹ 1,183 more rows -#> # ℹ abbreviated name: ¹​nFeature_expressed_in_sample -#> # ℹ 48 more variables: empty_droplet , cell_type_unified_ensemble , is_immune , subsets_Mito_percent , subsets_Ribo_percent , -#> # high_mitochondrion , high_ribosome , scDblFinder.class , sample_chunk , cell_chunk , sample_pseudobulk_chunk , -#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , inverse_transform , -#> # alive , cell_annotation_blueprint_singler , cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , -#> # low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , atlas_id , dataset_version_id , collection_id , … +#> # A SingleCellExperiment-tibble abstraction: 1,193 × 54 +#> # Features=33145 | Cells=1193 | Assays=sct +#> .cell observation_joinid dataset_id sample_id donor_id age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA empty_droplet +#> +#> 1 80_1 zz-!e5_XAo 842c6f5d-… 1de3f3ba… P58 14600 breast 1749 10.8 FALSE +#> 2 81_1 -mb&DWckf( 842c6f5d-… 1de3f3ba… P58 14600 breast 1993 12.4 FALSE +#> 3 72_1 8wGs7JgUjj 842c6f5d-… 6b194412… P39 14600 breast 2548 13.1 FALSE +#> 4 75_1 F9G7A+GgjA 842c6f5d-… db5a69ed… P40 14600 breast 1291 10.2 FALSE +#> 5 73_1 z_=CTOs4{z 842c6f5d-… 4b5e66fa… P39 14600 breast 2866 10.3 FALSE +#> 6 74_1 fNzorxA`Mf 842c6f5d-… 4b5e66fa… P39 14600 breast 1942 7.58 FALSE +#> 7 1_1 I8a42<8st4 842c6f5d-… 184fa234… P65 14600 breast 3395 11.8 FALSE +#> 8 1_2 >8f0}-gXFY 842c6f5d-… 81d05f17… P63 14600 breast 2513 13.3 FALSE +#> 9 22_2 2lQ`<&l3-A 842c6f5d-… 30967738… P57 14600 breast 2058 9.91 FALSE +#> 10 23_2 sqV-|vcI4R 842c6f5d-… d8ecdd92… P41 14600 breast 2375 13.7 FALSE +#> # ℹ 1,183 more rows +#> # ℹ abbreviated name: ¹​nFeature_expressed_in_sample +#> # ℹ 44 more variables: cell_type_unified_ensemble , is_immune , subsets_Mito_percent , +#> # subsets_Ribo_percent , high_mitochondrion , high_ribosome , alive , scDblFinder.class , +#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , +#> # nfeature_expressed_thresh , inverse_transform , cell_annotation_blueprint_singler , +#> # cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , … ``` ### Query pseudobulk @@ -386,53 +321,39 @@ pseudobulk_counts <- cell_type == "classical monocyte" ) |> get_pseudobulk() -#> ℹ Realising metadata. -#> ℹ Synchronising files -#> ℹ Reading files. -#> -Reading counts â– â– â– â– â–  14% | ETA: 8s - -Reading counts â– â– â– â– â– â– â– â– â– â–  29% | ETA: 11s - -Reading counts â– â– â– â– â– â– â– â– â– â– â– â– â– â–  43% | ETA: 9s - -Reading counts â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  57% | ETA: 6s - -Reading counts â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  71% | ETA: 4s - -Reading counts â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  86% | ETA: 2s - - -! cellNexus says: Not all genes completely overlap across the provided objects. Counts are generated by genes intersection. -#> ℹ Compiling Experiment. +#> ℹ Realising metadata. +#> ℹ Synchronising files +#> ℹ Reading files. +#> Reading counts ■■■■■ 14% | ETA: 9s Reading counts ■■■■■■■■■■ 29% | ETA: 9s Reading counts ■■■■■■■■■■■■■■ 43% | ETA: 7s Reading counts ■■■■■■■■■■■■■■■■■■ 57% | ETA: 5s Reading counts ■■■■■■■■■■■■■■■■■■■■■■ 71% | ETA: 4s Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■■■ 86% | ETA: 2s ! cellNexus says: Not all genes completely overlap across the provided objects. Counts are generated by genes intersection. +#> ℹ Compiling Experiment. pseudobulk_counts -#> # A SingleCellExperiment-tibble abstraction: 139 × 43 -#> # [90mFeatures=15888 | Cells=139 | Assays=counts[0m -#> .cell dataset_id sample_id sample_ experiment___ run_from_cell_id sample_heuristic age_days tissue_groups cell_type_unified_en…¹ sample_chunk cell_chunk -#> -#> 1 2e8c9911c9bfbf… 0ba16f4b-… 2e8c9911… 2e8c99… "" HDBR15279,HDBR1… NA respiratory … cd14 mono 1 1 -#> 2 0d874636bc714a… 1e6a6ef9-… 0d874636… 0d8746… "" Leader_Merad_20… 29930 respiratory … monocytic 1 5 -#> 3 0d874636bc714a… 1e6a6ef9-… 0d874636… 0d8746… "" Leader_Merad_20… 29930 respiratory … cd14 mono 1 5 -#> 4 0d874636bc714a… 1e6a6ef9-… 0d874636… 0d8746… "" Leader_Merad_20… 29930 respiratory … cd16 mono 1 5 -#> 5 0d874636bc714a… 1e6a6ef9-… 0d874636… 0d8746… "" Leader_Merad_20… 29930 respiratory … macrophage 1 5 -#> 6 0d874636bc714a… 1e6a6ef9-… 0d874636… 0d8746… "" Leader_Merad_20… 29930 respiratory … other 1 5 -#> 7 11721339cb1dfc… 1e6a6ef9-… 11721339… 117213… "" Leader_Merad_20… 26645 respiratory … monocytic 1 7 -#> 8 11721339cb1dfc… 1e6a6ef9-… 11721339… 117213… "" Leader_Merad_20… 26645 respiratory … cd14 mono 1 7 -#> 9 f71af64a552d45… 1e6a6ef9-… f71af64a… f71af6… "" Leader_Merad_20… 27010 respiratory … monocytic 1 6 -#> 10 f71af64a552d45… 1e6a6ef9-… f71af64a… f71af6… "" Leader_Merad_20… 27010 respiratory … cd14 mono 1 6 -#> # ℹ 129 more rows -#> # ℹ abbreviated name: ¹​cell_type_unified_ensemble -#> # ℹ 31 more variables: sample_pseudobulk_chunk , file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , -#> # inverse_transform , ethnicity_flagging_score , low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , atlas_id , -#> # dataset_version_id , collection_id , cell_count , citation , default_embedding , explorer_url , feature_count , -#> # mean_genes_per_cell , primary_cell_count , schema_version , title , tombstone , x_approximate_distribution , published_at , -#> # revised_at , tissue , self_reported_ethnicity , assay , disease , sex , sample_identifier +#> # A SingleCellExperiment-tibble abstraction: 146 × 46 +#> # Features=15888 | Cells=146 | Assays=counts +#> .cell sample_id cell_type_unified_en…¹ dataset_id donor_id age_days tissue_groups empty_droplet is_immune high_mitochondrion +#> +#> 1 2e8c9… 2e8c9911… cd14 mono 0ba16f4b-… HDBR152… NA respiratory … FALSE TRUE FALSE +#> 2 f71af… f71af64a… monocytic 1e6a6ef9-… Leader_… 27010 respiratory … FALSE TRUE FALSE +#> 3 f71af… f71af64a… cd14 mono 1e6a6ef9-… Leader_… 27010 respiratory … FALSE TRUE FALSE +#> 4 f71af… f71af64a… cd8 tem 1e6a6ef9-… Leader_… 27010 respiratory … FALSE TRUE FALSE +#> 5 11721… 11721339… monocytic 1e6a6ef9-… Leader_… 26645 respiratory … FALSE TRUE FALSE +#> 6 11721… 11721339… cd14 mono 1e6a6ef9-… Leader_… 26645 respiratory … FALSE TRUE FALSE +#> 7 0d874… 0d874636… cd14 mono 1e6a6ef9-… Leader_… 29930 respiratory … FALSE TRUE FALSE +#> 8 0d874… 0d874636… cd16 mono 1e6a6ef9-… Leader_… 29930 respiratory … FALSE TRUE FALSE +#> 9 0d874… 0d874636… macrophage 1e6a6ef9-… Leader_… 29930 respiratory … FALSE TRUE FALSE +#> 10 0d874… 0d874636… monocytic 1e6a6ef9-… Leader_… 29930 respiratory … FALSE TRUE FALSE +#> # ℹ 136 more rows +#> # ℹ abbreviated name: ¹​cell_type_unified_ensemble +#> # ℹ 36 more variables: alive , scDblFinder.class , file_id_cellNexus_single_cell , +#> # file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , inverse_transform , +#> # cell_annotation_azimuth_l2 , ethnicity_flagging_score , low_confidence_ethnicity , .aggregated_cells , +#> # imputed_ethnicity , atlas_id , dataset_version_id , collection_id , cell_count , citation , +#> # default_embedding , explorer_url , feature_count , mean_genes_per_cell , primary_cell_count , … ``` ## Download cell communication metadata -Cell communication metadata was generated based on post-QC cells per sample using `CellChat v2` method. It uses our harmonised cell type annotation (cell_type_unified_ensemble) to infer the communication. It captures inferred communication at both the ligand–receptor pair level and the signalling pathway level. +Cell communication metadata was generated based on post-QC cells per sample using `CellChat v2` method. It uses our harmonised cell type annotation (cell_type_unified_ensemble) to infer the communication. It captures inferred communication at both the ligand–receptor pair level and the signalling pathway level. - interaction_count: The number of inferred interactions between each pair of cell groups. @@ -446,15 +367,16 @@ For demonstration purpose, read cell communication metadata from a demo file her get_cell_communication_strength(cloud_metadata = get_metadata_url("cellNexus_lr_signaling_pathway_strength_DEMO.parquet")) #> # Source: SQL [?? x 16] #> # Database: DuckDB 1.4.3 [unknown@Linux 5.14.0-570.123.1.el9_6.x86_64:R 4.5.3/:memory:] -#> source target ligand receptor lr_prob lr_pval interaction_name interaction_name_2 pathway_name annotation evidence pathway_prob pathway_pval sample_id -#> -#> 1 b b TGFB1 TGFbR1_R2 0.000116 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR2) TGFb Secreted … KEGG: h… 0.000420 1 b290d7ef… -#> 2 b memory b TGFB1 TGFbR1_R2 0.000865 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR2) TGFb Secreted … KEGG: h… 0.00185 1 b290d7ef… -#> 3 b naive b TGFB1 TGFbR1_R2 0.000696 0.99 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR2) TGFb Secreted … KEGG: h… 0.00146 0.994 b290d7ef… -#> 4 cd14 mono b TGFB1 TGFbR1_R2 0.00240 0.81 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR2) TGFb Secreted … KEGG: h… 0.00472 0.924 b290d7ef… -#> 5 cd4 naive b TGFB1 TGFbR1_R2 0.000957 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR2) TGFb Secreted … KEGG: h… 0.00201 0.998 b290d7ef… -#> 6 cd4 tem b TGFB1 TGFbR1_R2 0.00242 0.76 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR2) TGFb Secreted … KEGG: h… 0.00467 0.797 b290d7ef… -#> # ℹ 2 more variables: interaction_count , interaction_weight +#> source target ligand receptor lr_prob lr_pval interaction_name interaction_name_2 pathway_name annotation evidence +#> +#> 1 b b TGFB1 TGFbR1_R2 0.000116 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR… TGFb Secreted … KEGG: h… +#> 2 b memory b TGFB1 TGFbR1_R2 0.000865 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR… TGFb Secreted … KEGG: h… +#> 3 b naive b TGFB1 TGFbR1_R2 0.000696 0.99 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR… TGFb Secreted … KEGG: h… +#> 4 cd14 mono b TGFB1 TGFbR1_R2 0.00240 0.81 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR… TGFb Secreted … KEGG: h… +#> 5 cd4 naive b TGFB1 TGFbR1_R2 0.000957 1 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR… TGFb Secreted … KEGG: h… +#> 6 cd4 tem b TGFB1 TGFbR1_R2 0.00242 0.76 TGFB1_TGFBR1_TGFBR2 TGFB1 - (TGFBR1+TGFBR… TGFb Secreted … KEGG: h… +#> # ℹ 5 more variables: pathway_prob , pathway_pval , sample_id , interaction_count , +#> # interaction_weight ``` ### Extract only a subset of genes @@ -472,51 +394,33 @@ single_cell_cpm <- cell_type == "T cell" ) |> get_single_cell_experiment(assays = "cpm", features = "ENSG00000134644") -#> ℹ Realising metadata. -#> ℹ Synchronising files -#> ℹ Reading files. -#> -Reading cpm â– â– â– â– â– â– â–  20% | ETA: 5s - -Reading cpm â– â– â– â– â– â– â– â– â– â–  30% | ETA: 4s - -Reading cpm â– â– â– â– â– â– â– â– â– â– â– â– â–  40% | ETA: 4s - -Reading cpm â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  50% | ETA: 3s - -Reading cpm â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  60% | ETA: 3s - -Reading cpm â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  70% | ETA: 2s - -Reading cpm â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  80% | ETA: 1s - -Reading cpm â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  90% | ETA: 1s - - -ℹ Compiling Experiment. +#> ℹ Realising metadata. +#> ℹ Synchronising files +#> ℹ Reading files. +#> Reading cpm ■■■■■■■ 20% | ETA: 4s Reading cpm ■■■■■■■■■■ 30% | ETA: 4s Reading cpm ■■■■■■■■■■■■■ 40% | ETA: 3s Reading cpm ■■■■■■■■■■■■■■■■ 50% | ETA: 3s Reading cpm ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 2s Reading cpm ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 2s Reading cpm ■■■■■■■■■■■■■■■■■■■■■■■■■ 80% | ETA: 1s Reading cpm ■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 90% | ETA: 1s ℹ Compiling Experiment. single_cell_cpm -#> # A SingleCellExperiment-tibble abstraction: 2,806 × 60 -#> # [90mFeatures=1 | Cells=2806 | 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 nCount_RNA -#> -#> 1 80_1 zz-!e5_XAo 842c6f5d-4a94… 1de3f3ba… 1de3f3… "" 7fabaf1c-52fd-4… 14600 breast 1749 10.8 -#> 2 81_1 -mb&DWckf( 842c6f5d-4a94… 1de3f3ba… 1de3f3… "" 7fabaf1c-52fd-4… 14600 breast 1993 12.4 -#> 3 72_1 8wGs7JgUjj 842c6f5d-4a94… 6b194412… 6b1944… "" b3ff1aad-40fd-4… 14600 breast 2548 13.1 -#> 4 75_1 F9G7A+GgjA 842c6f5d-4a94… db5a69ed… db5a69… "" 49beb83c-66a1-4… 14600 breast 1291 10.2 -#> 5 73_1 z_=CTOs4{z 842c6f5d-4a94… 4b5e66fa… 4b5e66… "" 04983012-bb56-4… 14600 breast 2866 10.3 -#> 6 74_1 fNzorxA`Mf 842c6f5d-4a94… 4b5e66fa… 4b5e66… "" 04983012-bb56-4… 14600 breast 1942 7.58 -#> 7 76_1 bTlx!HK=oS 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 1671 9.65 -#> 8 77_1 E4g5+)v;AV 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 2340 11.9 -#> 9 78_1 +q?29B%2nH 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 1714 13.2 -#> 10 79_1 zuJ#MBMWy; 842c6f5d-4a94… 52ab9222… 52ab92… "" 7ce86149-8906-4… 14600 breast 1506 12.3 -#> # ℹ 2,796 more rows -#> # ℹ abbreviated name: ¹​nFeature_expressed_in_sample -#> # ℹ 48 more variables: empty_droplet , cell_type_unified_ensemble , is_immune , subsets_Mito_percent , subsets_Ribo_percent , -#> # high_mitochondrion , high_ribosome , scDblFinder.class , sample_chunk , cell_chunk , sample_pseudobulk_chunk , -#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , inverse_transform , -#> # alive , cell_annotation_blueprint_singler , cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , -#> # low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , atlas_id , dataset_version_id , collection_id , … +#> # A SingleCellExperiment-tibble abstraction: 2,806 × 54 +#> # Features=1 | Cells=2806 | Assays=cpm +#> .cell observation_joinid dataset_id sample_id donor_id age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA empty_droplet +#> +#> 1 76_1 bTlx!HK=oS 842c6f5d-… 52ab9222… P58 14600 breast 1671 9.65 FALSE +#> 2 77_1 E4g5+)v;AV 842c6f5d-… 52ab9222… P58 14600 breast 2340 11.9 FALSE +#> 3 78_1 +q?29B%2nH 842c6f5d-… 52ab9222… P58 14600 breast 1714 13.2 FALSE +#> 4 79_1 zuJ#MBMWy; 842c6f5d-… 52ab9222… P58 14600 breast 1506 12.3 FALSE +#> 5 72_1 8wGs7JgUjj 842c6f5d-… 6b194412… P39 14600 breast 2548 13.1 FALSE +#> 6 75_1 F9G7A+GgjA 842c6f5d-… db5a69ed… P40 14600 breast 1291 10.2 FALSE +#> 7 80_1 zz-!e5_XAo 842c6f5d-… 1de3f3ba… P58 14600 breast 1749 10.8 FALSE +#> 8 81_1 -mb&DWckf( 842c6f5d-… 1de3f3ba… P58 14600 breast 1993 12.4 FALSE +#> 9 73_1 z_=CTOs4{z 842c6f5d-… 4b5e66fa… P39 14600 breast 2866 10.3 FALSE +#> 10 74_1 fNzorxA`Mf 842c6f5d-… 4b5e66fa… P39 14600 breast 1942 7.58 FALSE +#> # ℹ 2,796 more rows +#> # ℹ abbreviated name: ¹​nFeature_expressed_in_sample +#> # ℹ 44 more variables: cell_type_unified_ensemble , is_immune , subsets_Mito_percent , +#> # subsets_Ribo_percent , high_mitochondrion , high_ribosome , alive , scDblFinder.class , +#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , +#> # nfeature_expressed_thresh , inverse_transform , cell_annotation_blueprint_singler , +#> # cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , … ``` ### Extract the counts as a Seurat object @@ -534,34 +438,33 @@ seurat_counts <- cell_type == "T cell" ) |> get_seurat() -#> ℹ Realising metadata. -#> ℹ Synchronising files -#> ℹ Reading files. -#> -Reading counts â– â– â– â– â– â– â–  20% | ETA: 5s - -Reading counts â– â– â– â– â– â– â– â– â– â–  30% | ETA: 5s - -Reading counts â– â– â– â– â– â– â– â– â– â– â– â– â–  40% | ETA: 4s - -Reading counts â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  50% | ETA: 3s - -Reading counts â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  60% | ETA: 3s - -Reading counts â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  70% | ETA: 3s - -Reading counts â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  80% | ETA: 2s - -Reading counts â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â– â–  90% | ETA: 1s - - -ℹ Compiling Experiment. +#> ℹ Realising metadata. +#> ℹ Synchronising files +#> ℹ Reading files. +#> Reading counts ■■■■■■■ 20% | ETA: 4s Reading counts ■■■■■■■■■■ 30% | ETA: 4s Reading counts ■■■■■■■■■■■■■ 40% | ETA: 3s Reading counts ■■■■■■■■■■■■■■■■ 50% | ETA: 3s Reading counts ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 2s Reading counts ■■■■■■■■■■■■■■■■■■■■■■ 70% | ETA: 2s Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■ 80% | ETA: 1s Reading counts ■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 90% | ETA: 1s ℹ Compiling Experiment. seurat_counts -#> An object of class Seurat -#> 33145 features across 2806 samples within 1 assay -#> Active assay: originalexp (33145 features, 0 variable features) -#> 2 layers present: counts, data +#> # A Seurat-tibble abstraction: 2,806 × 59 +#> # Features=33145 | Cells=2806 | Active assay=counts | Assays=counts +#> .cell orig.ident nCount_originalexp nFeature_originalexp observation_joinid dataset_id sample_id donor_id age_days +#> +#> 1 73_1 SeuratProject 14.1 2958 z_=CTOs4{z 842c6f5d-4a94-4ee… 4b5e66fa… P39 14600 +#> 2 74_1 SeuratProject 14.2 2035 fNzorxA`Mf 842c6f5d-4a94-4ee… 4b5e66fa… P39 14600 +#> 3 76_1 SeuratProject 15.5 1759 bTlx!HK=oS 842c6f5d-4a94-4ee… 52ab9222… P58 14600 +#> 4 77_1 SeuratProject 15.4 2434 E4g5+)v;AV 842c6f5d-4a94-4ee… 52ab9222… P58 14600 +#> 5 78_1 SeuratProject 15.3 1798 +q?29B%2nH 842c6f5d-4a94-4ee… 52ab9222… P58 14600 +#> 6 79_1 SeuratProject 15.4 1595 zuJ#MBMWy; 842c6f5d-4a94-4ee… 52ab9222… P58 14600 +#> 7 1_1 SeuratProject 15.3 3493 I8a42<8st4 842c6f5d-4a94-4ee… 184fa234… P65 14600 +#> 8 80_1 SeuratProject 15.5 1837 zz-!e5_XAo 842c6f5d-4a94-4ee… 1de3f3ba… P58 14600 +#> 9 81_1 SeuratProject 15.2 2082 -mb&DWckf( 842c6f5d-4a94-4ee… 1de3f3ba… P58 14600 +#> 10 72_1 SeuratProject 18.0 2642 8wGs7JgUjj 842c6f5d-4a94-4ee… 6b194412… P39 14600 +#> # ℹ 2,796 more rows +#> # ℹ 50 more variables: tissue_groups , nFeature_expressed_in_sample , nCount_RNA , empty_droplet , +#> # cell_type_unified_ensemble , is_immune , subsets_Mito_percent , subsets_Ribo_percent , +#> # high_mitochondrion , high_ribosome , alive , scDblFinder.class , file_id_cellNexus_single_cell , +#> # file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , inverse_transform , +#> # cell_annotation_blueprint_singler , cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , +#> # ethnicity_flagging_score , low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , … ``` By default, data is downloaded to `get_default_cache_dir()` output. If memory is a concern, users can specify a custom path to metadata and counts `cache_directory` argument. @@ -764,25 +667,28 @@ get_metadata( dplyr::filter(file_id_cellNexus_single_cell %in% c(file_id_from_cloud, file_id_local)) |> dplyr::select(cell_id, sample_id, dataset_id, cell_type_unified_ensemble, atlas_id, file_id_cellNexus_single_cell) |> get_single_cell_experiment(cache_directory = local_cache) -#> ℹ Realising metadata. -#> ℹ Synchronising files -#> ℹ Reading files. -#> ℹ Compiling Experiment. -#> # A SingleCellExperiment-tibble abstraction: 500 × 7 -#> # [90mFeatures=13132 | Cells=500 | Assays=counts[0m -#> .cell sample_id dataset_id cell_type_unified_ensemble atlas_id file_id_cellNexus_single_cell original_cell_ -#> -#> 1 AAACATACAACCAC_1 pbmc3k pbmc3k Memory CD4 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACATACAACCAC -#> 2 AAACATTGAGCTAC_1 pbmc3k pbmc3k B cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACATTGAGCTAC -#> 3 AAACATTGATCAGC_1 pbmc3k pbmc3k Memory CD4 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACATTGATCAGC -#> 4 AAACCGTGCTTCCG_1 pbmc3k pbmc3k CD14+ Mono cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACCGTGCTTCCG -#> 5 AAACCGTGTATGCG_1 pbmc3k pbmc3k NK cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACCGTGTATGCG -#> 6 AAACGCACTGGTAC_1 pbmc3k pbmc3k Memory CD4 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACGCACTGGTAC -#> 7 AAACGCTGACCAGT_1 pbmc3k pbmc3k CD8 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACGCTGACCAGT -#> 8 AAACGCTGGTTCTT_1 pbmc3k pbmc3k CD8 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACGCTGGTTCTT -#> 9 AAACGCTGTAGCCA_1 pbmc3k pbmc3k Naive CD4 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACGCTGTAGCCA -#> 10 AAACGCTGTTTCTG_1 pbmc3k pbmc3k FCGR3A+ Mono cellxgene/03-10-2025 67e196a3c4e145151fc9e06c200e2f7f.h5ad AAACGCTGTTTCTG -#> # ℹ 490 more rows +#> 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/tmp/RtmpE4YcCe/sample_hca2024_v2.3.2.parquet +#> ℹ Realising metadata. +#> ℹ Synchronising files +#> ℹ Reading files. +#> ℹ Compiling Experiment. +#> # A SingleCellExperiment-tibble abstraction: 500 × 7 +#> # Features=13132 | Cells=500 | Assays=counts +#> .cell sample_id dataset_id cell_type_unified_ensemble atlas_id file_id_cellNexus_sing…¹ original_cell_ +#> +#> 1 AAACATACAACCAC_1 pbmc3k pbmc3k Memory CD4 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06… AAACATACAACCAC +#> 2 AAACATTGAGCTAC_1 pbmc3k pbmc3k B cellxgene/03-10-2025 67e196a3c4e145151fc9e06… AAACATTGAGCTAC +#> 3 AAACATTGATCAGC_1 pbmc3k pbmc3k Memory CD4 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06… AAACATTGATCAGC +#> 4 AAACCGTGCTTCCG_1 pbmc3k pbmc3k CD14+ Mono cellxgene/03-10-2025 67e196a3c4e145151fc9e06… AAACCGTGCTTCCG +#> 5 AAACCGTGTATGCG_1 pbmc3k pbmc3k NK cellxgene/03-10-2025 67e196a3c4e145151fc9e06… AAACCGTGTATGCG +#> 6 AAACGCACTGGTAC_1 pbmc3k pbmc3k Memory CD4 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06… AAACGCACTGGTAC +#> 7 AAACGCTGACCAGT_1 pbmc3k pbmc3k CD8 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06… AAACGCTGACCAGT +#> 8 AAACGCTGGTTCTT_1 pbmc3k pbmc3k CD8 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06… AAACGCTGGTTCTT +#> 9 AAACGCTGTAGCCA_1 pbmc3k pbmc3k Naive CD4 T cellxgene/03-10-2025 67e196a3c4e145151fc9e06… AAACGCTGTAGCCA +#> 10 AAACGCTGTTTCTG_1 pbmc3k pbmc3k FCGR3A+ Mono cellxgene/03-10-2025 67e196a3c4e145151fc9e06… AAACGCTGTTTCTG +#> # ℹ 490 more rows +#> # ℹ abbreviated name: ¹​file_id_cellNexus_single_cell ``` # Cell metadata @@ -818,9 +724,9 @@ sessionInfo() #> 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 +#> [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) @@ -829,47 +735,102 @@ sessionInfo() #> [1] stats4 stats graphics grDevices utils datasets methods base #> #> other attached packages: -#> [1] BiocStyle_2.38.0 RcppSpdlog_0.0.28 ggplot2_4.0.2 SummarizedExperiment_1.40.0 Biobase_2.70.0 -#> [6] GenomicRanges_1.62.1 Seqinfo_1.0.0 IRanges_2.44.0 S4Vectors_0.49.1-1 BiocGenerics_0.56.0 -#> [11] generics_0.1.4 MatrixGenerics_1.22.0 matrixStats_1.5.0 shiny_1.13.0 anndataR_1.0.2 -#> [16] cellNexus_0.99.27 testthat_3.3.2 dplyr_1.2.1 +#> [1] cellNexus_0.99.30 RcppSpdlog_0.0.28 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] fs_2.0.1 spatstat.sparse_3.1-0 fontawesome_0.5.3 devtools_2.5.0 httr_1.4.8 -#> [6] RColorBrewer_1.1-3 tools_4.5.3 sctransform_0.4.3 backports_1.5.1 utf8_1.2.6 -#> [11] R6_2.6.1 DT_0.34.0 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 gridExtra_2.3 nanoarrow_0.8.0 -#> [21] progressr_0.19.0 cli_3.6.6 spatstat.explore_3.8-0 fastDummies_1.7.5 sass_0.4.10 -#> [26] Seurat_5.5.0.9002 arrow_23.0.1.2 S7_0.2.1-1 spatstat.data_3.1-9 ggridges_0.5.7 -#> [31] pbapply_1.7-4 commonmark_2.0.0 R.utils_2.13.0 parallelly_1.46.1 sessioninfo_1.2.3 -#> [36] rstudioapi_0.18.0 ica_1.0-3 spatstat.random_3.4-5 Matrix_1.7-4 fansi_1.0.7 -#> [41] waldo_0.6.2 rclipboard_0.2.1 abind_1.4-8 R.methodsS3_1.8.2 lifecycle_1.0.5 -#> [46] yaml_2.3.12 rhdf5_2.54.1 SparseArray_1.10.10 Rtsne_0.17 grid_4.5.3 -#> [51] blob_1.3.0 promises_1.5.0 dir.expiry_1.18.0 miniUI_0.1.2 lattice_0.22-9 -#> [56] cowplot_1.2.0 pillar_1.11.1 knitr_1.51 future.apply_1.20.2 codetools_0.2-20 -#> [61] glue_1.8.0 tiledb_0.33.1 spatstat.univar_3.1-7 data.table_1.18.2.1 tidySingleCellExperiment_1.20.1 -#> [66] vctrs_0.7.3 png_0.1-9 spam_2.11-3 gtable_0.3.6 aws.s3_0.3.22 -#> [71] assertthat_0.2.1 cachem_1.1.0 xfun_0.57 S4Arrays_1.10.1 mime_0.13 -#> [76] rsconnect_1.8.0 survival_3.8-6 SingleCellExperiment_1.32.0 ellipsis_0.3.3 fitdistrplus_1.2-6 -#> [81] ROCR_1.0-12 tiledbsoma_2.1.2 nlme_3.1-168 RcppCCTZ_0.2.14 usethis_3.2.1 -#> [86] bit64_4.6.0-1 filelock_1.0.3 RcppAnnoy_0.0.23 GenomeInfoDb_1.46.2 rprojroot_2.1.1 -#> [91] R.cache_0.17.0 bslib_0.10.0 irlba_2.3.7 KernSmooth_2.23-26 otel_0.2.0 -#> [96] DBI_1.3.0 zellkonverter_1.20.1 duckdb_1.4.3 tidyselect_1.2.1 cellxgene.census_1.16.1 -#> [101] bit_4.6.0 compiler_4.5.3 curl_7.0.0 rjsoncons_1.3.2 h5mread_1.2.1 -#> [106] xml2_1.5.2 nanotime_0.3.13 desc_1.4.3 DelayedArray_0.36.1 plotly_4.12.0 -#> [111] bookdown_0.46 checkmate_2.3.4 scales_1.4.0 lmtest_0.9-40 spdl_0.0.5 -#> [116] stringr_1.6.0 digest_0.6.39 goftest_1.2-3 spatstat.utils_3.2-2 rmarkdown_2.31 -#> [121] basilisk_1.22.0 XVector_0.50.0 htmltools_0.5.9 pkgconfig_2.0.3 base64enc_0.1-6 -#> [126] dbplyr_2.5.2 fastmap_1.2.0 rlang_1.2.0 htmlwidgets_1.6.4 UCSC.utils_1.6.1 -#> [131] farver_2.1.2 jquerylib_0.1.4 zoo_1.8-15 jsonlite_2.0.0 R.oo_1.27.1 -#> [136] magrittr_2.0.5 dotCall64_1.2 patchwork_1.3.2 Rhdf5lib_1.32.0 Rcpp_1.1.1-1 -#> [141] reticulate_1.46.0 stringi_1.8.7 brio_1.1.5 MASS_7.3-65 plyr_1.8.9 -#> [146] pkgbuild_1.4.8 parallel_4.5.3 listenv_0.10.1 ggrepel_0.9.8 forcats_1.0.1 -#> [151] 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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] forcats_1.0.1 prettyunits_1.2.0 boot_1.3-32 +#> [208] beachmat_2.26.0 listenv_0.10.1 Rcpp_1.1.1-1 +#> [211] edgeR_4.8.2 roxygen2_7.3.3 BiocSingular_1.26.1 +#> [214] tensor_1.5.1 usethis_3.2.1 MASS_7.3-65 +#> [217] progress_1.2.3 uuid_1.2-2 BiocParallel_1.44.0 +#> [220] ggupset_0.4.1 nanotime_0.3.13 spatstat.random_3.4-5 +#> [223] R6_2.6.1 fastmap_1.2.0 vipor_0.4.7 +#> [226] ensembldb_2.34.0 ROCR_1.0-12 targets_1.12.0 +#> [229] rsvd_1.0.5 gtable_0.3.6 KernSmooth_2.23-26 +#> [232] miniUI_0.1.2 deldir_2.0-4 htmltools_0.5.9 +#> [235] bit64_4.6.0-1 spatstat.explore_3.8-0 lifecycle_1.0.5 +#> [238] S7_0.2.1-1 processx_3.8.7 nloptr_2.2.1 +#> [241] callr_3.7.6 restfulr_0.0.16 sass_0.4.10 +#> [244] vctrs_0.7.3 testthat_3.3.2 rsconnect_1.10.1 +#> [247] spatstat.geom_3.7-3 scran_1.38.1 sp_2.2-1 +#> [250] future.apply_1.20.2 bslib_0.10.0 pillar_1.11.1 +#> [253] GenomicFeatures_1.62.0 DropletUtils_1.30.0 cellxgene.census_1.16.1 +#> [256] collections_0.3.12 metapod_1.18.0 locfit_1.5-9.12 +#> [259] otel_0.2.0 BiocStyle_2.38.0 jsonlite_2.0.0 +#> [262] cigarillo_1.0.0 ``` diff --git a/vignettes/gene-expression-explore.Rmd b/vignettes/gene-expression-explore.Rmd index a5108ead..0114eba2 100644 --- a/vignettes/gene-expression-explore.Rmd +++ b/vignettes/gene-expression-explore.Rmd @@ -18,12 +18,11 @@ This page focuses on expression-layer retrieval workflows after metadata filteri ``` r library(cellNexus) -#> Registered S3 method overwritten by 'zellkonverter': -#> method from -#> py_to_r.pandas.core.arrays.categorical.Categorical reticulate 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() ``` @@ -40,30 +39,30 @@ query_metadata <- metadata |> imputed_ethnicity == "African American" ) query_metadata -#> # Source: SQL [?? x 76] -#> # Database: DuckDB v1.2.2 [shen.m@Darwin 23.6.0:R 4.5.0/:memory:] -#> cell_id observation_joinid dataset_id sample_id sample_ experiment___ -#> -#> 1 16 j}0a#X~ 842c6f5d-4a94-4ee… 1119f482… 1119f4… "" -#> 2 19 lNmuO5xs~3 842c6f5d-4a94-4ee… 1119f482… 1119f4… "" -#> 3 14 qxl7HJjL$L 842c6f5d-4a94-4ee… 1119f482… 1119f4… "" -#> 4 2 $jvBt8wHSK 842c6f5d-4a94-4ee… 1f755b9b… 1f755b… "" -#> 5 21 Mq^|(c<-#3 842c6f5d-4a94-4ee… b0d0c16e… b0d0c1… "" -#> 6 24 I`4{4__f#J 842c6f5d-4a94-4ee… b0d0c16e… b0d0c1… "" -#> 7 22 %vkLP;!cqY 842c6f5d-4a94-4ee… b0d0c16e… b0d0c1… "" -#> 8 11 gncTL3)pV~ 842c6f5d-4a94-4ee… bd5f6876… bd5f68… "" -#> 9 25 rfOnkhfWl8 842c6f5d-4a94-4ee… 04e410cb… 04e410… "" -#> 10 24 =tj7A 11 13 Py{Fqs?~!! 842c6f5d-4a94-4ee… 30ea4b4f… 30ea4b… "" -#> 12 9 s$u5u14ye$ 842c6f5d-4a94-4ee… 49ef9551… 49ef95… "" -#> 13 6 ?y4kdGGQ!^ 842c6f5d-4a94-4ee… 49ef9551… 49ef95… "" -#> # ℹ 70 more variables: run_from_cell_id , sample_heuristic , -#> # age_days , tissue_groups , nFeature_expressed_in_sample , -#> # nCount_RNA , empty_droplet , cell_type_unified_ensemble , -#> # is_immune , subsets_Mito_percent , subsets_Ribo_percent , -#> # high_mitochondrion , high_ribosome , scDblFinder.class , -#> # sample_chunk , cell_chunk , sample_pseudobulk_chunk , -#> # file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , … +#> # 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 +#> +#> 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: ¹​nFeature_expressed_in_sample, ²​cell_type_unified_ensemble +#> # ℹ 20 more variables: is_immune , subsets_Mito_percent , subsets_Ribo_percent , high_mitochondrion , +#> # high_ribosome , alive , scDblFinder.class , file_id_cellNexus_single_cell , +#> # file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , inverse_transform , +#> # cell_annotation_blueprint_singler , cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , +#> # ethnicity_flagging_score , low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , +#> # atlas_id ``` # Retrieve expression by representation @@ -77,26 +76,32 @@ sce_counts <- query_metadata |> #> ℹ Realising metadata. #> ℹ Synchronising files #> ℹ Reading files. -#> For native R and reading and writing of H5AD files, an R object, and conversion to or objects, check out the anndataR package: -#> ℹ Install it from Bioconductor with `BiocManager::install("anndataR")` -#> ℹ See more at -#> ℹ Compiling Experiment. -#> -#> This message is displayed once per session. +#> Reading counts ■■■■■■■■■■■■■■■■ 50% | ETA: 1s ℹ Compiling Experiment. sce_counts -#> class: SingleCellExperiment -#> dim: 33145 13 -#> metadata(0): -#> assays(1): counts -#> rownames(33145): ENSG00000243485 ENSG00000237613 ... ENSG00000277475 -#> ENSG00000268674 -#> rowData names(0): -#> colnames(13): 16_1 19_1 ... 9_2 6_2 -#> colData names(76): observation_joinid dataset_id ... -#> tissue_ontology_term_id original_cell_ -#> reducedDimNames(0): -#> mainExpName: NULL -#> altExpNames(0): +#> # A SingleCellExperiment-tibble abstraction: 13 × 30 +#> # Features=33145 | Cells=13 | Assays=counts +#> .cell dataset_id sample_id age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA empty_droplet cell_type_unified_en…² +#> +#> 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: ¹​nFeature_expressed_in_sample, ²​cell_type_unified_ensemble +#> # ℹ 21 more variables: is_immune , subsets_Mito_percent , subsets_Ribo_percent , high_mitochondrion , +#> # high_ribosome , alive , scDblFinder.class , file_id_cellNexus_single_cell , +#> # file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , inverse_transform , +#> # cell_annotation_blueprint_singler , cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , +#> # ethnicity_flagging_score , low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , +#> # atlas_id , original_cell_ ``` ## Counts per million @@ -107,24 +112,33 @@ sce_cpm <- query_metadata |> get_single_cell_experiment(assays = "cpm") #> ℹ Realising metadata. #> ℹ Synchronising files -#> ℹ Downloading 1 file, totalling 0 GB -#> ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellNexus-anndata/cellxgene_2024/0.2.1/cpm/001f82656d61ccb98f0ae26a2eb9e5ba___1.h5ad to /Users/shen.m/Library/Caches/org.R-project.R/R/cellNexus/cellxgene_2024/0.2.1//cpm/001f82656d61ccb98f0ae26a2eb9e5ba___1.h5ad #> ℹ Reading files. #> ℹ Compiling Experiment. sce_cpm -#> class: SingleCellExperiment -#> dim: 33145 13 -#> metadata(0): -#> assays(1): cpm -#> rownames(33145): ENSG00000243485 ENSG00000237613 ... ENSG00000277475 -#> ENSG00000268674 -#> rowData names(0): -#> colnames(13): 16_1 19_1 ... 9_2 6_2 -#> colData names(76): observation_joinid dataset_id ... -#> tissue_ontology_term_id original_cell_ -#> reducedDimNames(0): -#> mainExpName: NULL -#> altExpNames(0): +#> # A SingleCellExperiment-tibble abstraction: 13 × 30 +#> # Features=33145 | Cells=13 | Assays=cpm +#> .cell dataset_id sample_id age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA empty_droplet cell_type_unified_en…² +#> +#> 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: ¹​nFeature_expressed_in_sample, ²​cell_type_unified_ensemble +#> # ℹ 21 more variables: is_immune , subsets_Mito_percent , subsets_Ribo_percent , high_mitochondrion , +#> # high_ribosome , alive , scDblFinder.class , file_id_cellNexus_single_cell , +#> # file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , inverse_transform , +#> # cell_annotation_blueprint_singler , cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , +#> # ethnicity_flagging_score , low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , +#> # atlas_id , original_cell_ ``` ## Pseudobulk @@ -135,27 +149,25 @@ pb_counts <- query_metadata |> get_pseudobulk() #> ℹ Realising metadata. #> ℹ Synchronising files -#> ℹ Downloading 1 file, totalling 0.26 GB -#> ℹ Downloading https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/cellNexus-anndata/cellxgene_2024/0.2.1/pseudobulk/counts/91acbf94110b95b1e994fdb1e1322fd1___1.h5ad to /Users/shen.m/Library/Caches/org.R-project.R/R/cellNexus/cellxgene_2024/0.2.1/pseudobulk/counts/91acbf94110b95b1e994fdb1e1322fd1___1.h5ad #> ℹ Reading files. #> ℹ Compiling Experiment. pb_counts -#> class: SingleCellExperiment -#> dim: 33145 7 -#> metadata(0): -#> assays(1): counts -#> rownames(33145): ENSG00000243485 ENSG00000237613 ... ENSG00000277475 -#> ENSG00000268674 -#> rowData names(0): -#> colnames(7): 1119f4825edbcfb74341b89d9dec4ac8___cd16 mono -#> 1f755b9b59313f6c5e80caa696799ac2___cd16 mono ... -#> 30ea4b4f8922b4a6b35bf1a12be2d5e9___cd16 mono -#> 49ef9551c2ddf79b01c74f863ea6f556___cd16 mono -#> colData names(59): dataset_id sample_id ... tissue_ontology_term_id -#> sample_identifier -#> reducedDimNames(0): -#> mainExpName: NULL -#> altExpNames(0): +#> # A SingleCellExperiment-tibble abstraction: 7 × 25 +#> # Features=33145 | Cells=7 | Assays=counts +#> .cell sample_id cell_type_unified_en…¹ dataset_id age_days tissue_groups empty_droplet is_immune high_mitochondrion alive +#> +#> 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: ¹​cell_type_unified_ensemble +#> # ℹ 15 more variables: scDblFinder.class , file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , +#> # count_upper_bound , nfeature_expressed_thresh , inverse_transform , cell_annotation_blueprint_singler , +#> # cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , +#> # low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , atlas_id , sample_identifier ``` # Targeted gene queries @@ -173,18 +185,30 @@ sce_gene <- query_metadata |> #> ℹ Reading files. #> ℹ Compiling Experiment. sce_gene -#> class: SingleCellExperiment -#> dim: 1 13 -#> metadata(0): -#> assays(1): cpm -#> rownames(1): ENSG00000134644 -#> rowData names(0): -#> colnames(13): 16_1 19_1 ... 9_2 6_2 -#> colData names(76): observation_joinid dataset_id ... -#> tissue_ontology_term_id original_cell_ -#> reducedDimNames(0): -#> mainExpName: NULL -#> altExpNames(0): +#> # A SingleCellExperiment-tibble abstraction: 13 × 30 +#> # Features=1 | Cells=13 | Assays=cpm +#> .cell dataset_id sample_id age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA empty_droplet cell_type_unified_en…² +#> +#> 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: ¹​nFeature_expressed_in_sample, ²​cell_type_unified_ensemble +#> # ℹ 21 more variables: is_immune , subsets_Mito_percent , subsets_Ribo_percent , high_mitochondrion , +#> # high_ribosome , alive , scDblFinder.class , file_id_cellNexus_single_cell , +#> # file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , inverse_transform , +#> # cell_annotation_blueprint_singler , cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , +#> # ethnicity_flagging_score , low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , +#> # atlas_id , original_cell_ ``` # Seurat @@ -199,10 +223,36 @@ seurat_obj <- query_metadata |> #> ℹ Reading files. #> 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 +#> 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 . +#> 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 +#> # Features=33145 | Cells=13 | Active assay=counts | Assays=counts +#> .cell orig.ident nCount_originalexp nFeature_originalexp dataset_id sample_id age_days tissue_groups nFeature_expressed_i…¹ +#> +#> 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: ¹​nFeature_expressed_in_sample +#> # ℹ 26 more variables: nCount_RNA , empty_droplet , cell_type_unified_ensemble , is_immune , +#> # subsets_Mito_percent , subsets_Ribo_percent , high_mitochondrion , high_ribosome , alive , +#> # scDblFinder.class , file_id_cellNexus_single_cell , file_id_cellNexus_pseudobulk , count_upper_bound , +#> # nfeature_expressed_thresh , inverse_transform , cell_annotation_blueprint_singler , +#> # cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , +#> # low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , atlas_id , original_cell_ , … ``` # Portable output examples @@ -229,99 +279,121 @@ anndataR::write_h5ad(sce_counts, "single_cell_counts.h5ad") ``` r sessionInfo() -#> R version 4.5.0 (2025-04-11) -#> Platform: aarch64-apple-darwin20 -#> Running under: macOS Sonoma 14.8.7 +#> 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: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib -#> LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1 +#> 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] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 +#> [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: internal +#> tzcode source: system (glibc) #> #> attached base packages: -#> [1] stats graphics grDevices utils datasets methods base +#> [1] stats4 stats graphics grDevices utils datasets methods base #> #> other attached packages: -#> [1] dplyr_1.2.1 cellNexus_0.99.23 +#> [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] RColorBrewer_1.1-3 jsonlite_2.0.0 -#> [3] magrittr_2.0.5 spatstat.utils_3.2-2 -#> [5] farver_2.1.2 vctrs_0.7.3 -#> [7] ROCR_1.0-12 spatstat.explore_3.8-0 -#> [9] htmltools_0.5.9 S4Arrays_1.10.1 -#> [11] curl_7.1.0 Rhdf5lib_1.32.0 -#> [13] rhdf5_2.54.1 SparseArray_1.10.10 -#> [15] sass_0.4.10 sctransform_0.4.3 -#> [17] parallelly_1.47.0 bslib_0.10.0 -#> [19] KernSmooth_2.23-26 basilisk_1.20.0 -#> [21] htmlwidgets_1.6.4 ica_1.0-3 -#> [23] plyr_1.8.9 cachem_1.1.0 -#> [25] plotly_4.12.0 zoo_1.8-15 -#> [27] igraph_2.3.0 mime_0.13 -#> [29] lifecycle_1.0.5 pkgconfig_2.0.3 -#> [31] Matrix_1.7-3 R6_2.6.1 -#> [33] fastmap_1.2.0 anndataR_1.0.1 -#> [35] MatrixGenerics_1.22.0 fitdistrplus_1.2-6 -#> [37] future_1.70.0 shiny_1.13.0 -#> [39] digest_0.6.39 patchwork_1.3.2 -#> [41] S4Vectors_0.48.1 Seurat_5.5.0.9003 -#> [43] tensor_1.5.1 RSpectra_0.16-2 -#> [45] irlba_2.3.7 GenomicRanges_1.62.1 -#> [47] filelock_1.0.3 progressr_0.19.0 -#> [49] spatstat.sparse_3.1-0 httr_1.4.8 -#> [51] polyclip_1.10-7 abind_1.4-8 -#> [53] compiler_4.5.0 withr_3.0.2 -#> [55] backports_1.5.1 S7_0.2.2 -#> [57] DBI_1.3.0 fastDummies_1.7.6 -#> [59] HDF5Array_1.38.0 duckdb_1.2.2 -#> [61] MASS_7.3-65 DelayedArray_0.36.1 -#> [63] tools_4.5.0 lmtest_0.9-40 -#> [65] otel_0.2.0 httpuv_1.6.17 -#> [67] future.apply_1.20.2 goftest_1.2-3 -#> [69] glue_1.8.1 h5mread_1.2.1 -#> [71] rhdf5filters_1.22.0 nlme_3.1-168 -#> [73] promises_1.5.0 grid_4.5.0 -#> [75] checkmate_2.3.2 Rtsne_0.17 -#> [77] cluster_2.1.8.1 reshape2_1.4.5 -#> [79] generics_0.1.4 gtable_0.3.6 -#> [81] spatstat.data_3.1-9 tidyr_1.3.2 -#> [83] data.table_1.18.2.1 utf8_1.2.6 -#> [85] sp_2.2-1 XVector_0.50.0 -#> [87] BiocGenerics_0.56.0 spatstat.geom_3.7-3 -#> [89] RcppAnnoy_0.0.23 ggrepel_0.9.8 -#> [91] RANN_2.6.2 pillar_1.11.1 -#> [93] stringr_1.6.0 spam_2.11-3 -#> [95] RcppHNSW_0.6.0 later_1.4.8 -#> [97] splines_4.5.0 lattice_0.22-6 -#> [99] survival_3.8-3 deldir_2.0-4 -#> [101] tidyselect_1.2.1 SingleCellExperiment_1.32.0 -#> [103] miniUI_0.1.2 pbapply_1.7-4 -#> [105] knitr_1.51 gridExtra_2.3 -#> [107] IRanges_2.44.0 Seqinfo_1.0.0 -#> [109] SummarizedExperiment_1.40.0 scattermore_1.2 -#> [111] stats4_4.5.0 xfun_0.57 -#> [113] Biobase_2.70.0 matrixStats_1.5.0 -#> [115] stringi_1.8.7 lazyeval_0.2.3 -#> [117] shinyWidgets_0.9.0 evaluate_1.0.5 -#> [119] codetools_0.2-20 tibble_3.3.1 -#> [121] cli_3.6.6 uwot_0.2.4 -#> [123] xtable_1.8-8 reticulate_1.46.0 -#> [125] jquerylib_0.1.4 zellkonverter_1.20.1 -#> [127] Rcpp_1.1.1-1.1 dir.expiry_1.16.0 -#> [129] globals_0.19.1 spatstat.random_3.4-5 -#> [131] dbplyr_2.5.2 png_0.1-9 -#> [133] spatstat.univar_3.1-7 parallel_4.5.0 -#> [135] blob_1.3.0 rclipboard_0.2.1 -#> [137] ggplot2_4.0.3 basilisk.utils_1.20.0 -#> [139] dotCall64_1.2 listenv_0.10.1 -#> [141] viridisLite_0.4.3 scales_1.4.0 -#> [143] ggridges_0.5.7 SeuratObject_5.4.0 -#> [145] purrr_1.2.2 rlang_1.2.0 -#> [147] cowplot_1.2.0 +#> [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 ``` diff --git a/vignettes/metadata-explore.Rmd b/vignettes/metadata-explore.Rmd index 56086476..81d82bf4 100644 --- a/vignettes/metadata-explore.Rmd +++ b/vignettes/metadata-explore.Rmd @@ -18,27 +18,27 @@ This page is a standalone metadata guide for `cellNexus` and documents the key f library(cellNexus) metadata <- get_metadata(cloud_metadata = SAMPLE_DATABASE_URL) metadata -#> # Source: SQL [?? x 36] +#> # 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 sample_ experiment___ run_from_cell_id sample_heuristic age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA empty_droplet -#> -#> 1 15 842c6f5d-4a94-4e… 1119f482… 1119f4… "" 182a61cc-b041-4… 14600 breast 1701 8.99 FALSE -#> 2 16 842c6f5d-4a94-4e… 1119f482… 1119f4… "" 182a61cc-b041-4… 14600 breast 2438 9.80 FALSE -#> 3 17 842c6f5d-4a94-4e… 1119f482… 1119f4… "" 182a61cc-b041-4… 14600 breast 2122 9.46 FALSE -#> 4 18 842c6f5d-4a94-4e… 1119f482… 1119f4… "" 182a61cc-b041-4… 14600 breast 1894 10.3 FALSE -#> 5 19 842c6f5d-4a94-4e… 1119f482… 1119f4… "" 182a61cc-b041-4… 14600 breast 1876 9.15 FALSE -#> 6 20 842c6f5d-4a94-4e… 1119f482… 1119f4… "" 182a61cc-b041-4… 14600 breast 1441 10.3 FALSE -#> 7 14 842c6f5d-4a94-4e… 1119f482… 1119f4… "" 182a61cc-b041-4… 14600 breast 1547 10.5 FALSE -#> 8 2 842c6f5d-4a94-4e… 1f755b9b… 1f755b… "" 9ca47fe5-873e-4… 14600 breast 1342 9.40 FALSE -#> 9 3 842c6f5d-4a94-4e… 1f755b9b… 1f755b… "" 9ca47fe5-873e-4… 14600 breast 1808 9.80 FALSE -#> 10 4 842c6f5d-4a94-4e… 1f755b9b… 1f755b… "" 9ca47fe5-873e-4… 14600 breast 1514 9.30 FALSE +#> cell_id dataset_id sample_id age_days tissue_groups nFeature_expressed_i…¹ nCount_RNA empty_droplet cell_type_unified_en…² +#> +#> 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 name: ¹​nFeature_expressed_in_sample -#> # ℹ 24 more variables: cell_type_unified_ensemble , is_immune , subsets_Mito_percent , subsets_Ribo_percent , high_mitochondrion , -#> # high_ribosome , scDblFinder.class , sample_chunk , cell_chunk , sample_pseudobulk_chunk , file_id_cellNexus_single_cell , -#> # file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , inverse_transform , alive , -#> # cell_annotation_blueprint_singler , cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , ethnicity_flagging_score , -#> # low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , atlas_id +#> # ℹ abbreviated names: ¹​nFeature_expressed_in_sample, ²​cell_type_unified_ensemble +#> # ℹ 20 more variables: is_immune , subsets_Mito_percent , subsets_Ribo_percent , high_mitochondrion , +#> # high_ribosome , alive , scDblFinder.class , file_id_cellNexus_single_cell , +#> # file_id_cellNexus_pseudobulk , count_upper_bound , nfeature_expressed_thresh , inverse_transform , +#> # cell_annotation_blueprint_singler , cell_annotation_monaco_singler , cell_annotation_azimuth_l2 , +#> # ethnicity_flagging_score , low_confidence_ethnicity , .aggregated_cells , imputed_ethnicity , … ``` # Data-processing context @@ -57,9 +57,7 @@ metadata | `observation_joinid` | Cell ID join key linking metadata. | | `dataset_id` | Primary dataset identifier in the atlas. | | `sample_id` | Harmonised sample identifier. | - | `sample_` | Internal sample subdivision helper. | - | `experiment___` | Upstream experiment grouping variable. | - | `sample_heuristic` | Internal sample subdivision helper. | + | `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. | @@ -72,9 +70,6 @@ metadata | `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`. | - | `sample_chunk ` | Internal sample subdivision chunks. | - | `cell_chunk ` | Internal cell subdivision chunks. | - | `sample_pseudobulk_chunk ` | Internal pseudobulk subdivision chunks. | | `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. | @@ -97,15 +92,16 @@ metadata ``` r # Which columns are available? colnames(metadata) -#> [1] "cell_id" "dataset_id" "sample_id" "sample_" -#> [5] "experiment___" "run_from_cell_id" "sample_heuristic" "age_days" -#> [9] "tissue_groups" "nFeature_expressed_in_sample" "nCount_RNA" "empty_droplet" -#> [13] "cell_type_unified_ensemble" "is_immune" "subsets_Mito_percent" "subsets_Ribo_percent" -#> [17] "high_mitochondrion" "high_ribosome" "scDblFinder.class" "sample_chunk" -#> [21] "cell_chunk" "sample_pseudobulk_chunk" "file_id_cellNexus_single_cell" "file_id_cellNexus_pseudobulk" -#> [25] "count_upper_bound" "nfeature_expressed_thresh" "inverse_transform" "alive" -#> [29] "cell_annotation_blueprint_singler" "cell_annotation_monaco_singler" "cell_annotation_azimuth_l2" "ethnicity_flagging_score" -#> [33] "low_confidence_ethnicity" ".aggregated_cells" "imputed_ethnicity" "atlas_id" +#> [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 |> @@ -120,21 +116,21 @@ metadata |> #> 2 respiratory system 7 #> 3 bone marrow 6 #> 4 renal system 4 -#> 5 thymus 3 -#> 6 breast 3 +#> 5 breast 3 +#> 6 thymus 3 #> 7 cerebral lobes and cortical areas 2 -#> 8 female reproductive system 2 -#> 9 spleen 2 +#> 8 spleen 2 +#> 9 female reproductive system 2 #> 10 nasal, oral, and pharyngeal regions 2 -#> 11 oesophagus 1 -#> 12 lymphatic system 1 -#> 13 epithelium and mucosal tissues 1 -#> 14 stomach 1 -#> 15 vasculature 1 -#> 16 small intestine 1 -#> 17 endocrine system 1 -#> 18 brainstem and cerebellar structures 1 -#> 19 sensory-related structures 1 +#> 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 |> @@ -157,9 +153,9 @@ sessionInfo() #> 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 +#> [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) @@ -168,47 +164,101 @@ sessionInfo() #> [1] stats4 stats graphics grDevices utils datasets methods base #> #> other attached packages: -#> [1] BiocStyle_2.38.0 RcppSpdlog_0.0.28 ggplot2_4.0.2 SummarizedExperiment_1.40.0 Biobase_2.70.0 -#> [6] GenomicRanges_1.62.1 Seqinfo_1.0.0 IRanges_2.44.0 S4Vectors_0.49.1-1 BiocGenerics_0.56.0 -#> [11] generics_0.1.4 MatrixGenerics_1.22.0 matrixStats_1.5.0 shiny_1.13.0 anndataR_1.0.2 -#> [16] cellNexus_0.99.27 testthat_3.3.2 dplyr_1.2.1 +#> [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] fs_2.0.1 spatstat.sparse_3.1-0 fontawesome_0.5.3 devtools_2.5.0 httr_1.4.8 -#> [6] RColorBrewer_1.1-3 tools_4.5.3 sctransform_0.4.3 backports_1.5.1 utf8_1.2.6 -#> [11] R6_2.6.1 DT_0.34.0 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 gridExtra_2.3 nanoarrow_0.8.0 -#> [21] progressr_0.19.0 cli_3.6.6 spatstat.explore_3.8-0 fastDummies_1.7.5 sass_0.4.10 -#> [26] Seurat_5.5.0.9002 arrow_23.0.1.2 S7_0.2.1-1 spatstat.data_3.1-9 ggridges_0.5.7 -#> [31] pbapply_1.7-4 commonmark_2.0.0 R.utils_2.13.0 parallelly_1.46.1 sessioninfo_1.2.3 -#> [36] rstudioapi_0.18.0 ica_1.0-3 spatstat.random_3.4-5 Matrix_1.7-4 fansi_1.0.7 -#> [41] waldo_0.6.2 rclipboard_0.2.1 abind_1.4-8 R.methodsS3_1.8.2 lifecycle_1.0.5 -#> [46] yaml_2.3.12 rhdf5_2.54.1 SparseArray_1.10.10 Rtsne_0.17 grid_4.5.3 -#> [51] blob_1.3.0 promises_1.5.0 dir.expiry_1.18.0 miniUI_0.1.2 lattice_0.22-9 -#> [56] cowplot_1.2.0 pillar_1.11.1 knitr_1.51 future.apply_1.20.2 codetools_0.2-20 -#> [61] glue_1.8.0 tiledb_0.33.1 spatstat.univar_3.1-7 data.table_1.18.2.1 tidySingleCellExperiment_1.20.1 -#> [66] vctrs_0.7.3 png_0.1-9 spam_2.11-3 gtable_0.3.6 aws.s3_0.3.22 -#> [71] assertthat_0.2.1 cachem_1.1.0 xfun_0.57 S4Arrays_1.10.1 mime_0.13 -#> [76] rsconnect_1.8.0 survival_3.8-6 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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 ``` diff --git a/vignettes/metadata-explore.Rmd.orig b/vignettes/metadata-explore.Rmd.orig index 5b159e42..689e8f01 100644 --- a/vignettes/metadata-explore.Rmd.orig +++ b/vignettes/metadata-explore.Rmd.orig @@ -37,9 +37,7 @@ metadata | `observation_joinid` | Cell ID join key linking metadata. | | `dataset_id` | Primary dataset identifier in the atlas. | | `sample_id` | Harmonised sample identifier. | - | `sample_` | Internal sample subdivision helper. | - | `experiment___` | Upstream experiment grouping variable. | - | `sample_heuristic` | Internal sample subdivision helper. | + | `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. | @@ -52,9 +50,6 @@ metadata | `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`. | - | `sample_chunk ` | Internal sample subdivision chunks. | - | `cell_chunk ` | Internal cell subdivision chunks. | - | `sample_pseudobulk_chunk ` | Internal pseudobulk subdivision chunks. | | `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. |