CuratedAtlasQueryR

CuratedAtlasQuery is a query interface that allow the programmatic exploration and retrieval of the harmonised, curated and reannotated CELLxGENE single-cell human cell atlas. Data can be retrieved at cell, sample, or dataset levels based on filtering criteria.

Harmonised data is stored in the ARDC Nectar Research Cloud, and most CuratedAtlasQuery functions interact with Nectar via web requests, so a network connection is required for most functionality.

Usage

The API has delivered more than 15Tb of data to the community in the first year. Thanks!

Query interface

Installation

devtools::install_github("stemangiola/CuratedAtlasQueryR")

Load the package

library(CuratedAtlasQueryR)

Load and explore the metadata

Load the metadata

# Note: in real applications you should use the default value of remote_url 
metadata <- get_metadata(remote_url = METADATA_URL)
metadata
#> # Source:   table</vast/scratch/users/milton.m/cache/R/CuratedAtlasQueryR/metadata.0.2.3.parquet> [?? x 56]
#> # Database: DuckDB 0.7.1 [unknown@Linux 3.10.0-1160.88.1.el7.x86_64:R 4.2.1/:memory:]
#>    cell_ sample_ cell_…¹ cell_…² confi…³ cell_…⁴ cell_…⁵ cell_…⁶ sampl…⁷ _samp…⁸
#>    <chr> <chr>   <chr>   <chr>     <dbl> <chr>   <chr>   <chr>   <chr>   <chr>  
#>  1 8387… 7bd7b8… natura… immune…       5 cd8 tem gmp     natura… 842ce7… Q59___…
#>  2 1768… 7bd7b8… natura… immune…       5 cd8 tem cd8 tcm natura… 842ce7… Q59___…
#>  3 6329… 7bd7b8… natura… immune…       5 cd8 tem clp     termin… 842ce7… Q59___…
#>  4 5027… 7bd7b8… natura… immune…       5 cd8 tem clp     natura… 842ce7… Q59___…
#>  5 7956… 7bd7b8… natura… immune…       5 cd8 tem clp     natura… 842ce7… Q59___…
#>  6 4305… 7bd7b8… natura… immune…       5 cd8 tem clp     termin… 842ce7… Q59___…
#>  7 2126… 933f96… natura… ilc           1 nk      nk      natura… c250bf… AML3__…
#>  8 3114… 933f96… natura… immune…       5 mait    nk      natura… c250bf… AML3__…
#>  9 1407… 933f96… natura… immune…       5 mait    clp     natura… c250bf… AML3__…
#> 10 2911… 933f96… natura… nk            5 nk      clp     natura… c250bf… AML3__…
#> # … with more rows, 46 more variables: assay <chr>,
#> #   assay_ontology_term_id <chr>, file_id_db <chr>,
#> #   cell_type_ontology_term_id <chr>, development_stage <chr>,
#> #   development_stage_ontology_term_id <chr>, disease <chr>,
#> #   disease_ontology_term_id <chr>, ethnicity <chr>,
#> #   ethnicity_ontology_term_id <chr>, experiment___ <chr>, file_id <chr>,
#> #   is_primary_data_x <chr>, organism <chr>, organism_ontology_term_id <chr>, …

The metadata variable can then be re-used for all subsequent queries.

Explore the tissue

metadata |>
    dplyr::distinct(tissue, file_id) 
#> # Source:   SQL [10 x 2]
#> # Database: DuckDB 0.7.1 [unknown@Linux 3.10.0-1160.88.1.el7.x86_64:R 4.2.1/:memory:]
#>    tissue              file_id                             
#>    <chr>               <chr>                               
#>  1 bone marrow         1ff5cbda-4d41-4f50-8c7e-cbe4a90e38db
#>  2 lung parenchyma     6661ab3a-792a-4682-b58c-4afb98b2c016
#>  3 respiratory airway  6661ab3a-792a-4682-b58c-4afb98b2c016
#>  4 nose                6661ab3a-792a-4682-b58c-4afb98b2c016
#>  5 renal pelvis        dc9d8cdd-29ee-4c44-830c-6559cb3d0af6
#>  6 kidney              dc9d8cdd-29ee-4c44-830c-6559cb3d0af6
#>  7 renal medulla       dc9d8cdd-29ee-4c44-830c-6559cb3d0af6
#>  8 cortex of kidney    dc9d8cdd-29ee-4c44-830c-6559cb3d0af6
#>  9 kidney blood vessel dc9d8cdd-29ee-4c44-830c-6559cb3d0af6
#> 10 lung                a2796032-d015-40c4-b9db-835207e5bd5b

Download single-cell RNA sequencing counts

Query raw counts

single_cell_counts = 
    metadata |>
    dplyr::filter(
        ethnicity == "African" &
        stringr::str_like(assay, "%10x%") &
        tissue == "lung parenchyma" &
        stringr::str_like(cell_type, "%CD4%")
    ) |>
    get_single_cell_experiment()
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Downloading 0 files, totalling 0 GB
#> ℹ Reading files.
#> ℹ Compiling Single Cell Experiment.

single_cell_counts
#> # A SingleCellExperiment-tibble abstraction: 1,571 × 57
#> # [90mFeatures=36229 | Cells=1571 | Assays=counts[0m
#>    .cell sample_ cell_…¹ cell_…² confi…³ cell_…⁴ cell_…⁵ cell_…⁶ sampl…⁷ X_sam…⁸
#>    <chr> <chr>   <chr>   <chr>     <dbl> <chr>   <chr>   <chr>   <chr>   <chr>  
#>  1 AGCG… 11a7dc… CD4-po… cd4 th1       3 cd4 tcm cd8 t   th1     10b339… Donor_…
#>  2 TCAG… 11a7dc… CD4-po… cd4 th…       3 cd4 tcm cd4 tem th1/th… 10b339… Donor_…
#>  3 TTTA… 11a7dc… CD4-po… cd4 th…       3 cd4 tcm cd4 tcm th17    10b339… Donor_…
#>  4 ACAC… 11a7dc… CD4-po… immune…       5 cd4 tcm plasma  th1/th… 10b339… Donor_…
#>  5 CAAG… 11a7dc… CD4-po… immune…       1 cd4 tcm cd4 tcm mait    10b339… Donor_…
#>  6 CTGT… 14a078… CD4-po… cd4 th…       3 cd4 tcm cd4 tem th1/th… 8f71c5… VUHD85…
#>  7 ACGT… 14a078… CD4-po… treg          5 cd4 tcm tregs   t regu… 8f71c5… VUHD85…
#>  8 CATA… 14a078… CD4-po… immune…       5 nk      cd8 tem mait    8f71c5… VUHD85…
#>  9 ACTT… 14a078… CD4-po… mait          5 mait    cd8 tem mait    8f71c5… VUHD85…
#> 10 TGCG… 14a078… CD4-po… cd4 th1       3 cd4 tcm cd4 tem th1     8f71c5… VUHD85…
#> # … with 1,561 more rows, 47 more variables: assay <chr>,
#> #   assay_ontology_term_id <chr>, file_id_db <chr>,
#> #   cell_type_ontology_term_id <chr>, development_stage <chr>,
#> #   development_stage_ontology_term_id <chr>, disease <chr>,
#> #   disease_ontology_term_id <chr>, ethnicity <chr>,
#> #   ethnicity_ontology_term_id <chr>, experiment___ <chr>, file_id <chr>,
#> #   is_primary_data_x <chr>, organism <chr>, organism_ontology_term_id <chr>, …

Query counts scaled per million

This is helpful if just few genes are of interest, as they can be compared across samples.

single_cell_counts = 
    metadata |>
    dplyr::filter(
        ethnicity == "African" &
        stringr::str_like(assay, "%10x%") &
        tissue == "lung parenchyma" &
        stringr::str_like(cell_type, "%CD4%")
    ) |>
    get_single_cell_experiment(assays = "cpm")
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Downloading 0 files, totalling 0 GB
#> ℹ Reading files.
#> ℹ Compiling Single Cell Experiment.

single_cell_counts
#> # A SingleCellExperiment-tibble abstraction: 1,571 × 57
#> # [90mFeatures=36229 | Cells=1571 | Assays=cpm[0m
#>    .cell sample_ cell_…¹ cell_…² confi…³ cell_…⁴ cell_…⁵ cell_…⁶ sampl…⁷ X_sam…⁸
#>    <chr> <chr>   <chr>   <chr>     <dbl> <chr>   <chr>   <chr>   <chr>   <chr>  
#>  1 AGCG… 11a7dc… CD4-po… cd4 th1       3 cd4 tcm cd8 t   th1     10b339… Donor_…
#>  2 TCAG… 11a7dc… CD4-po… cd4 th…       3 cd4 tcm cd4 tem th1/th… 10b339… Donor_…
#>  3 TTTA… 11a7dc… CD4-po… cd4 th…       3 cd4 tcm cd4 tcm th17    10b339… Donor_…
#>  4 ACAC… 11a7dc… CD4-po… immune…       5 cd4 tcm plasma  th1/th… 10b339… Donor_…
#>  5 CAAG… 11a7dc… CD4-po… immune…       1 cd4 tcm cd4 tcm mait    10b339… Donor_…
#>  6 CTGT… 14a078… CD4-po… cd4 th…       3 cd4 tcm cd4 tem th1/th… 8f71c5… VUHD85…
#>  7 ACGT… 14a078… CD4-po… treg          5 cd4 tcm tregs   t regu… 8f71c5… VUHD85…
#>  8 CATA… 14a078… CD4-po… immune…       5 nk      cd8 tem mait    8f71c5… VUHD85…
#>  9 ACTT… 14a078… CD4-po… mait          5 mait    cd8 tem mait    8f71c5… VUHD85…
#> 10 TGCG… 14a078… CD4-po… cd4 th1       3 cd4 tcm cd4 tem th1     8f71c5… VUHD85…
#> # … with 1,561 more rows, 47 more variables: assay <chr>,
#> #   assay_ontology_term_id <chr>, file_id_db <chr>,
#> #   cell_type_ontology_term_id <chr>, development_stage <chr>,
#> #   development_stage_ontology_term_id <chr>, disease <chr>,
#> #   disease_ontology_term_id <chr>, ethnicity <chr>,
#> #   ethnicity_ontology_term_id <chr>, experiment___ <chr>, file_id <chr>,
#> #   is_primary_data_x <chr>, organism <chr>, organism_ontology_term_id <chr>, …

Extract only a subset of genes

single_cell_counts = 
    metadata |>
    dplyr::filter(
        ethnicity == "African" &
        stringr::str_like(assay, "%10x%") &
        tissue == "lung parenchyma" &
        stringr::str_like(cell_type, "%CD4%")
    ) |>
    get_single_cell_experiment(assays = "cpm", features = "PUM1")
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Downloading 0 files, totalling 0 GB
#> ℹ Reading files.
#> ℹ Compiling Single Cell Experiment.

single_cell_counts
#> # A SingleCellExperiment-tibble abstraction: 1,571 × 57
#> # [90mFeatures=1 | Cells=1571 | Assays=cpm[0m
#>    .cell sample_ cell_…¹ cell_…² confi…³ cell_…⁴ cell_…⁵ cell_…⁶ sampl…⁷ X_sam…⁸
#>    <chr> <chr>   <chr>   <chr>     <dbl> <chr>   <chr>   <chr>   <chr>   <chr>  
#>  1 AGCG… 11a7dc… CD4-po… cd4 th1       3 cd4 tcm cd8 t   th1     10b339… Donor_…
#>  2 TCAG… 11a7dc… CD4-po… cd4 th…       3 cd4 tcm cd4 tem th1/th… 10b339… Donor_…
#>  3 TTTA… 11a7dc… CD4-po… cd4 th…       3 cd4 tcm cd4 tcm th17    10b339… Donor_…
#>  4 ACAC… 11a7dc… CD4-po… immune…       5 cd4 tcm plasma  th1/th… 10b339… Donor_…
#>  5 CAAG… 11a7dc… CD4-po… immune…       1 cd4 tcm cd4 tcm mait    10b339… Donor_…
#>  6 CTGT… 14a078… CD4-po… cd4 th…       3 cd4 tcm cd4 tem th1/th… 8f71c5… VUHD85…
#>  7 ACGT… 14a078… CD4-po… treg          5 cd4 tcm tregs   t regu… 8f71c5… VUHD85…
#>  8 CATA… 14a078… CD4-po… immune…       5 nk      cd8 tem mait    8f71c5… VUHD85…
#>  9 ACTT… 14a078… CD4-po… mait          5 mait    cd8 tem mait    8f71c5… VUHD85…
#> 10 TGCG… 14a078… CD4-po… cd4 th1       3 cd4 tcm cd4 tem th1     8f71c5… VUHD85…
#> # … with 1,561 more rows, 47 more variables: assay <chr>,
#> #   assay_ontology_term_id <chr>, file_id_db <chr>,
#> #   cell_type_ontology_term_id <chr>, development_stage <chr>,
#> #   development_stage_ontology_term_id <chr>, disease <chr>,
#> #   disease_ontology_term_id <chr>, ethnicity <chr>,
#> #   ethnicity_ontology_term_id <chr>, experiment___ <chr>, file_id <chr>,
#> #   is_primary_data_x <chr>, organism <chr>, organism_ontology_term_id <chr>, …

Extract the counts as a Seurat object

This convert the H5 SingleCellExperiment to Seurat so it might take long time and occupy a lot of memory depending on how many cells you are requesting.

single_cell_counts_seurat = 
    metadata |>
    dplyr::filter(
        ethnicity == "African" &
        stringr::str_like(assay, "%10x%") &
        tissue == "lung parenchyma" &
        stringr::str_like(cell_type, "%CD4%")
    ) |>
    get_seurat()
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Downloading 0 files, totalling 0 GB
#> ℹ Reading files.
#> ℹ Compiling Single Cell Experiment.

single_cell_counts_seurat
#> An object of class Seurat 
#> 36229 features across 1571 samples within 1 assay 
#> Active assay: originalexp (36229 features, 0 variable features)

Save your `SingleCellExperiment`

The returned SingleCellExperiment can be saved with two modalities, as .rds or as HDF5.

Saving as RDS (fast saving, slow reading)

Saving as .rds has the advantage of being fast, andd the .rds file occupies very little disk space as it only stores the links to the files in your cache.

However it has the disadvantage that for big SingleCellExperiment objects, which merge a lot of HDF5 from your get_single_cell_experiment, the display and manipulation is going to be slow. In addition, an .rds saved in this way is not portable: you will not be able to share it with other users.

single_cell_counts |> saveRDS("single_cell_counts.rds")

Saving as HDF5 (slow saving, fast reading)

Saving as .hdf5 executes any computation on the SingleCellExperiment and writes it to disk as a monolithic HDF5. Once this is done, operations on the SingleCellExperiment will be comparatively very fast. The resulting .hdf5 file will also be totally portable and sharable.

However this .hdf5 has the disadvantage of being larger than the corresponding .rds as it includes a copy of the count information, and the saving process is going to be slow for large objects.

single_cell_counts |> HDF5Array::saveHDF5SummarizedExperiment("single_cell_counts", replace = TRUE)

Visualise gene transcription

We can gather all CD14 monocytes cells and plot the distribution of HLA-A across all tissues

#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Downloading 0 files, totalling 0 GB
#> ℹ Reading files.
#> ℹ Compiling Single Cell Experiment.

library(tidySingleCellExperiment)
library(ggplot2)

counts |> 
  ggplot(aes( disease, `HLA.A`,color = file_id)) +
  geom_jitter(shape=".")

metadata |> 
    
  # Filter and subset
  dplyr::filter(cell_type_harmonised=="nk") |> 

  # Get counts per million for HCA-A gene 
  get_single_cell_experiment(assays = "cpm", features = "HLA-A") |> 

  # Plot (styling code have been omitted)
  tidySingleCellExperiment::join_features("HLA-A", shape = "wide") |> 
  ggplot(aes(tissue_harmonised, `HLA.A`,color = file_id)) +
  geom_jitter(shape=".")
#> ℹ Realising metadata.
#> ℹ Synchronising files
#> ℹ Downloading 0 files, totalling 0 GB
#> ℹ Reading files.
#> ℹ Compiling Single Cell Experiment.

Obtain Unharmonised Metadata

Various metadata fields are not common between datasets, so it does not make sense for these to live in the main metadata table. However, we can obtain it using the get_unharmonised_metadata() function. This function returns a data frame with one row per dataset, including the unharmonised column which contains unharmnised metadata as a nested data frame.

harmonised <- metadata |> dplyr::filter(tissue == "kidney blood vessel")
unharmonised <- get_unharmonised_metadata(harmonised)
unharmonised
#> # A tibble: 1 × 2
#>   file_id                              unharmonised   
#>   <chr>                                <list>         
#> 1 dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 <tbl_dck_[,14]>

Notice that the columns differ between each dataset’s data frame:

dplyr::pull(unharmonised) |> head(2)
#> [[1]]
#> # Source:   SQL [?? x 14]
#> # Database: DuckDB 0.7.1 [unknown@Linux 3.10.0-1160.88.1.el7.x86_64:R 4.2.1/:memory:]
#>    cell_ file_id donor…¹ donor…² libra…³ mappe…⁴ sampl…⁵ suspe…⁶ suspe…⁷ autho…⁸
#>    <chr> <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>  
#>  1 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell    d8a44f… Pelvic…
#>  2 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell    d8a44f… Pelvic…
#>  3 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell    d8a44f… Pelvic…
#>  4 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell    d8a44f… Pelvic…
#>  5 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell    d8a44f… Pelvic…
#>  6 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell    d8a44f… Pelvic…
#>  7 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell    d8a44f… Pelvic…
#>  8 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell    d8a44f… Pelvic…
#>  9 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell    d8a44f… Pelvic…
#> 10 4602… dc9d8c… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell    d8a44f… Pelvic…
#> # … with more rows, 4 more variables: reported_diseases <chr>,
#> #   Experiment <chr>, Project <chr>, broad_celltype <chr>, and abbreviated
#> #   variable names ¹donor_age, ²donor_uuid, ³library_uuid,
#> #   ⁴mapped_reference_annotation, ⁵sample_uuid, ⁶suspension_type,
#> #   ⁷suspension_uuid, ⁸author_cell_type

Cell metadata

Dataset-specific columns (definitions available at cellxgene.cziscience.com)

cell_count, collection_id, created_at.x, created_at.y, dataset_deployments, dataset_id, file_id, filename, filetype, is_primary_data.y, is_valid, linked_genesets, mean_genes_per_cell, name, published, published_at, revised_at, revision, s3_uri, schema_version, tombstone, updated_at.x, updated_at.y, user_submitted, x_normalization

Sample-specific columns (definitions available at cellxgene.cziscience.com)

sample_, sample_name, age_days, assay, assay_ontology_term_id, development_stage, development_stage_ontology_term_id, ethnicity, ethnicity_ontology_term_id, experiment___, organism, organism_ontology_term_id, sample_placeholder, sex, sex_ontology_term_id, tissue, tissue_harmonised, tissue_ontology_term_id, disease, disease_ontology_term_id, is_primary_data.x

Cell-specific columns (definitions available at cellxgene.cziscience.com)

cell_, cell_type, cell_type_ontology_term_idm, cell_type_harmonised, confidence_class, cell_annotation_azimuth_l2, cell_annotation_blueprint_singler

Through harmonisation and curation we introduced custom column, not present in the original CELLxGENE metadata

tissue_harmonised: a coarser tissue name for better filtering
age_days: the number of days corresponding to the age
cell_type_harmonised: the consensus call identity (for immune cells) using the original and three novel annotations using Seurat Azimuth and SingleR
confidence_class: an ordinal class of how confident cell_type_harmonised is. 1 is complete consensus, 2 is 3 out of four and so on.
cell_annotation_azimuth_l2: Azimuth cell annotation
cell_annotation_blueprint_singler: SingleR cell annotation using Blueprint reference
cell_annotation_blueprint_monaco: SingleR cell annotation using Monaco reference
sample_id_db: Sample subdivision for internal use
file_id_db: File subdivision for internal use
sample_: Sample ID
.sample_name: How samples were defined

RNA abundance

The raw assay includes RNA abundance in the positive real scale (not transformed with non-linear functions, e.g. log sqrt). Originally CELLxGENE include a mix of scales and transformations specified in the x_normalization column.

The cpm assay includes counts per million.

Session Info

sessionInfo()
#> R version 4.2.1 (2022-06-23)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#> 
#> Matrix products: default
#> BLAS:   /stornext/System/data/apps/R/R-4.2.1/lib64/R/lib/libRblas.so
#> LAPACK: /stornext/System/data/apps/R/R-4.2.1/lib64/R/lib/libRlapack.so
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] 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   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] tidySingleCellExperiment_1.6.3 SingleCellExperiment_1.18.1   
#>  [3] SummarizedExperiment_1.26.1    Biobase_2.56.0                
#>  [5] GenomicRanges_1.48.0           GenomeInfoDb_1.32.4           
#>  [7] IRanges_2.30.1                 S4Vectors_0.34.0              
#>  [9] BiocGenerics_0.42.0            MatrixGenerics_1.8.1          
#> [11] matrixStats_0.63.0             ttservice_0.2.2               
#> [13] ggplot2_3.4.1                  CuratedAtlasQueryR_0.99.1     
#> 
#> loaded via a namespace (and not attached):
#>   [1] plyr_1.8.8             igraph_1.4.1           lazyeval_0.2.2        
#>   [4] sp_1.5-1               splines_4.2.1          listenv_0.9.0         
#>   [7] scattermore_0.8        digest_0.6.31          htmltools_0.5.4       
#>  [10] fansi_1.0.3            magrittr_2.0.3         tensor_1.5            
#>  [13] cluster_2.1.3          ROCR_1.0-11            globals_0.16.2        
#>  [16] duckdb_0.7.1-1         spatstat.sparse_3.0-0  colorspace_2.0-3      
#>  [19] blob_1.2.3             ggrepel_0.9.2          xfun_0.36             
#>  [22] dplyr_1.1.0            RCurl_1.98-1.9         jsonlite_1.8.4        
#>  [25] progressr_0.13.0       spatstat.data_3.0-0    survival_3.3-1        
#>  [28] zoo_1.8-11             glue_1.6.2             polyclip_1.10-4       
#>  [31] gtable_0.3.1           zlibbioc_1.42.0        XVector_0.36.0        
#>  [34] leiden_0.4.3           DelayedArray_0.22.0    Rhdf5lib_1.18.2       
#>  [37] future.apply_1.10.0    HDF5Array_1.24.2       abind_1.4-5           
#>  [40] scales_1.2.1           DBI_1.1.3              spatstat.random_3.0-1 
#>  [43] miniUI_0.1.1.1         Rcpp_1.0.10            viridisLite_0.4.1     
#>  [46] xtable_1.8-4           reticulate_1.26        htmlwidgets_1.6.0     
#>  [49] httr_1.4.4             RColorBrewer_1.1-3     ellipsis_0.3.2        
#>  [52] Seurat_4.3.0           ica_1.0-3              farver_2.1.1          
#>  [55] pkgconfig_2.0.3        dbplyr_2.3.0           sass_0.4.4            
#>  [58] uwot_0.1.14            deldir_1.0-6           utf8_1.2.2            
#>  [61] labeling_0.4.2         tidyselect_1.2.0       rlang_1.0.6           
#>  [64] reshape2_1.4.4         later_1.3.0            munsell_0.5.0         
#>  [67] tools_4.2.1            cachem_1.0.6           cli_3.6.0             
#>  [70] generics_0.1.3         ggridges_0.5.4         evaluate_0.19         
#>  [73] stringr_1.5.0          fastmap_1.1.0          yaml_2.3.6            
#>  [76] goftest_1.2-3          knitr_1.42             fitdistrplus_1.1-8    
#>  [79] purrr_1.0.1            RANN_2.6.1             pbapply_1.6-0         
#>  [82] future_1.30.0          nlme_3.1-157           mime_0.12             
#>  [85] compiler_4.2.1         rstudioapi_0.14        curl_4.3.3            
#>  [88] plotly_4.10.1          png_0.1-8              spatstat.utils_3.0-1  
#>  [91] tibble_3.1.8           bslib_0.4.2            stringi_1.7.12        
#>  [94] highr_0.10             forcats_1.0.0          lattice_0.20-45       
#>  [97] Matrix_1.5-3           vctrs_0.5.2            pillar_1.8.1          
#> [100] lifecycle_1.0.3        rhdf5filters_1.8.0     spatstat.geom_3.0-3   
#> [103] lmtest_0.9-40          jquerylib_0.1.4        RcppAnnoy_0.0.20      
#> [106] data.table_1.14.6      cowplot_1.1.1          bitops_1.0-7          
#> [109] irlba_2.3.5.1          httpuv_1.6.7           patchwork_1.1.2       
#> [112] R6_2.5.1               promises_1.2.0.1       KernSmooth_2.23-20    
#> [115] gridExtra_2.3          parallelly_1.33.0      codetools_0.2-18      
#> [118] assertthat_0.2.1       MASS_7.3-57            rhdf5_2.40.0          
#> [121] rprojroot_2.0.3        withr_2.5.0            SeuratObject_4.1.3    
#> [124] sctransform_0.3.5      GenomeInfoDbData_1.2.8 parallel_4.2.1        
#> [127] grid_4.2.1             tidyr_1.3.0            rmarkdown_2.20        
#> [130] Rtsne_0.16             spatstat.explore_3.0-5 shiny_1.7.4

Name		Name	Last commit message	Last commit date
Latest commit History 549 Commits
.github		.github
R		R
data		data
dev		dev
inst		inst
man		man
tests		tests
vignettes		vignettes
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
DESCRIPTION		DESCRIPTION
LICENSE.md		LICENSE.md
NAMESPACE		NAMESPACE
NEWS.md		NEWS.md
README.md		README.md

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CuratedAtlasQueryR

Usage

Query interface

Installation

Load the package

Load and explore the metadata

Load the metadata

Explore the tissue

Download single-cell RNA sequencing counts

Query raw counts

Query counts scaled per million

Extract only a subset of genes

Extract the counts as a Seurat object

Save your `SingleCellExperiment`

Saving as RDS (fast saving, slow reading)

Saving as HDF5 (slow saving, fast reading)

Visualise gene transcription

Obtain Unharmonised Metadata

Cell metadata

RNA abundance

Session Info

About

Releases 7

Packages

Contributors 5

Languages

License

stemangiola/CuratedAtlasQueryR

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CuratedAtlasQueryR

Usage

Query interface

Installation

Load the package

Load and explore the metadata

Load the metadata

Explore the tissue

Download single-cell RNA sequencing counts

Query raw counts

Query counts scaled per million

Extract only a subset of genes

Extract the counts as a Seurat object

Save your SingleCellExperiment

Saving as RDS (fast saving, slow reading)

Saving as HDF5 (slow saving, fast reading)

Visualise gene transcription

Obtain Unharmonised Metadata

Cell metadata

RNA abundance

Session Info

About

Topics

Resources

License

Stars

Watchers

Forks

Releases 7

Packages 0

Contributors 5

Languages

Save your `SingleCellExperiment`

Packages