Contents

1 Installation and use

This package is available in Bioconductor version 3.15 and later. The following code installs cellxgenedp as well as other packages required for this vignette.

pkgs <- c("cellxgenedp", "zellkonverter", "SingleCellExperiment", "HDF5Array")
required_pkgs <- pkgs[!pkgs %in% rownames(installed.packages())]
BiocManager::install(required_pkgs)

Use the following pkgs vector to install from GitHub (latest, unchecked, development version) instead

pkgs <- c(
    "mtmorgan/cellxgenedp", "zellkonverter", "SingleCellExperiment", "HDF5Array"
)

Load the package into your current R session. We make extensive use of the dplyr packages, and at the end of the vignette use SingleCellExperiment and zellkonverter, so load those as well.

suppressPackageStartupMessages({
    library(zellkonverter)
    library(SingleCellExperiment) # load early to avoid masking dplyr::count()
    library(dplyr)
    library(cellxgenedp)
})

2 cxg() Provides a ‘shiny’ interface

The following sections outline how to use the cellxgenedp package in an R script; most functionality is also available in the cxg() shiny application, providing an easy way to identify, download, and visualize one or several datasets. Start the app

cxg()

choose a project on the first tab, and a dataset for visualization, or one or more datasets for download!

3 Collections, datasets and files

Retrieve metadata about resources available at the cellxgene data portal using db():

db <- db()

Printing the db object provides a brief overview of the available data, as well as hints, in the form of functions like collections(), for further exploration.

db
## cellxgene_db
## number of collections(): 124
## number of datasets(): 732
## number of files(): 2917

The portal organizes data hierarchically, with ‘collections’ (research studies, approximately), ‘datasets’, and ‘files’. Discover data using the corresponding functions.

collections(db)
## # A tibble: 124 × 15
##    collection_id   access_type consortia contact_email contact_name curator_name
##    <chr>           <chr>       <list>    <chr>         <chr>        <chr>       
##  1 43d4bb39-21af-… READ        <lgl [1]> raymond.cho@… Raymond J. … Batuhan Cak…
##  2 d36ca85c-3e8b-… READ        <list>    mengjiechen@… Mengjie Chen Erica Marie…
##  3 2b02dff7-e427-… READ        <lgl [1]> miriam.merad… Miriam Merad Batuhan Cak…
##  4 e9eec7f5-8519-… READ        <list>    mrc38@cam.ac… Menna R Cla… Batuhan Cak…
##  5 a72afd53-ab92-… READ        <lgl [1]> cblish@stanf… Catherine A… Jennifer Yu…
##  6 4796c91c-9d8f-… READ        <lgl [1]> vazquezi@msk… Ignacio Váz… Jason Hilton
##  7 bcb61471-2a44-… READ        <list>    info@kpmp.org KPMP         Jennifer Yu…
##  8 13d1c580-4b17-… READ        <list>    my4@sanger.a… Matthew You… Jason Hilton
##  9 9132fae8-bdfe-… READ        <lgl [1]> zhuang@chemi… Xiaowei Zhu… Jennifer Yu…
## 10 5e469121-c203-… READ        <list>    biren@ucsd.e… Bing Ren     Jennifer Yu…
## # ℹ 114 more rows
## # ℹ 9 more variables: data_submission_policy_version <chr>, description <chr>,
## #   links <list>, name <chr>, publisher_metadata <list>, visibility <chr>,
## #   created_at <date>, published_at <date>, updated_at <date>
datasets(db)
## # A tibble: 732 × 26
##    dataset_id                 collection_id donor_id assay  cell_count cell_type
##    <chr>                      <chr>         <list>   <list>      <int> <list>   
##  1 d62144d1-6e98-4f7c-a36e-f… 43d4bb39-21a… <list>   <list>      68036 <list>   
##  2 b67ac525-77bf-4559-8d03-e… d36ca85c-3e8… <list>   <list>      60574 <list>   
##  3 31641e24-0db8-413f-9c25-3… d36ca85c-3e8… <list>   <list>      18315 <list>   
##  4 8f7d8f2b-0b4c-4f6a-8f11-9… d36ca85c-3e8… <list>   <list>      26134 <list>   
##  5 0f244510-53fe-4037-9fbb-e… d36ca85c-3e8… <list>   <list>      40803 <list>   
##  6 7a637398-175f-4cf2-b06a-6… 2b02dff7-e42… <list>   <list>      32458 <list>   
##  7 14c0c08d-fb2e-4a7d-8b50-7… e9eec7f5-851… <list>   <list>     130908 <list>   
##  8 a0982372-a1cc-4466-8d2b-e… a72afd53-ab9… <list>   <list>      44721 <list>   
##  9 4f8e3657-c1a8-440d-8bc6-a… 4796c91c-9d8… <list>   <list>     211624 <list>   
## 10 48f8ad54-091a-41be-ac40-1… 4796c91c-9d8… <list>   <list>     929690 <list>   
## # ℹ 722 more rows
## # ℹ 20 more variables: dataset_deployments <chr>, development_stage <list>,
## #   disease <list>, is_primary_data <chr>, is_valid <lgl>,
## #   mean_genes_per_cell <dbl>, name <chr>, organism <list>,
## #   processing_status <list>, published <lgl>, revision <int>,
## #   schema_version <chr>, self_reported_ethnicity <list>, sex <list>,
## #   suspension_type <list>, tissue <list>, tombstone <lgl>, …
files(db)
## # A tibble: 2,917 × 8
##    file_id         dataset_id filename filetype s3_uri user_submitted created_at
##    <chr>           <chr>      <chr>    <chr>    <chr>  <lgl>          <date>    
##  1 87ce2086-f2eb-… d62144d1-… "raw.h5… RAW_H5AD s3://… TRUE           1970-01-01
##  2 8eabe485-be71-… d62144d1-… "local.… H5AD     s3://… TRUE           1970-01-01
##  3 33dfa406-132a-… d62144d1-… "local.… RDS      s3://… TRUE           1970-01-01
##  4 454e0b3c-e207-… d62144d1-… ""       CXG      s3://… TRUE           1970-01-01
##  5 99fbc774-538b-… b67ac525-… "local.… RDS      s3://… TRUE           1970-01-01
##  6 00f4be75-8456-… b67ac525-… "local.… H5AD     s3://… TRUE           1970-01-01
##  7 253533d1-3aca-… b67ac525-… "raw.h5… RAW_H5AD s3://… TRUE           1970-01-01
##  8 7ba996fa-cc3d-… b67ac525-… ""       CXG      s3://… TRUE           1970-01-01
##  9 7b5ef79e-25e9-… 31641e24-… "raw.h5… RAW_H5AD s3://… TRUE           1970-01-01
## 10 aff69187-ab59-… 31641e24-… "local.… H5AD     s3://… TRUE           1970-01-01
## # ℹ 2,907 more rows
## # ℹ 1 more variable: updated_at <date>

Each of these resources has a unique primary identifier (e.g., file_id) as well as an identifier describing the relationship of the resource to other components of the database (e.g., dataset_id). These identifiers can be used to ‘join’ information across tables.

3.1 Using dplyr to navigate data

A collection may have several datasets, and datasets may have several files. For instance, here is the collection with the most datasets

collection_with_most_datasets <-
    datasets(db) |>
    count(collection_id, sort = TRUE) |>
    slice(1)

We can find out about this collection by joining with the collections() table.

left_join(
    collection_with_most_datasets |> select(collection_id),
    collections(db),
    by = "collection_id"
) |> glimpse()
## Rows: 1
## Columns: 15
## $ collection_id                  <chr> "283d65eb-dd53-496d-adb7-7570c7caa443"
## $ access_type                    <chr> "READ"
## $ consortia                      <list> ["BRAIN Initiative"]
## $ contact_email                  <chr> "kimberly.siletti@ki.se"
## $ contact_name                   <chr> "Kimberly Siletti"
## $ curator_name                   <chr> "James Chaffer"
## $ data_submission_policy_version <chr> "1.0"
## $ description                    <chr> "The human brain directs a wide range o…
## $ links                          <list> [["", "RAW_DATA", "http://data.nemoarch…
## $ name                           <chr> "Transcriptomic diversity of cell type…
## $ publisher_metadata             <list> [[["Siletti", "Kimberly"], ["Hodge", "R…
## $ visibility                     <chr> "PUBLIC"
## $ created_at                     <date> 2023-02-16
## $ published_at                   <date> 2022-12-09
## $ updated_at                     <date> 2023-02-16

We can take a similar strategy to identify all datasets belonging to this collection

left_join(
    collection_with_most_datasets |> select(collection_id),
    datasets(db),
    by = "collection_id"
)
## # A tibble: 138 × 26
##    collection_id                 dataset_id donor_id assay  cell_count cell_type
##    <chr>                         <chr>      <list>   <list>      <int> <list>   
##  1 283d65eb-dd53-496d-adb7-7570… 262c619b-… <list>   <list>      67533 <list>   
##  2 283d65eb-dd53-496d-adb7-7570… 4fb0cd63-… <list>   <list>      25071 <list>   
##  3 283d65eb-dd53-496d-adb7-7570… d15266a4-… <list>   <list>      35290 <list>   
##  4 283d65eb-dd53-496d-adb7-7570… f4c89e28-… <list>   <list>      38331 <list>   
##  5 283d65eb-dd53-496d-adb7-7570… fb3deebe-… <list>   <list>      21534 <list>   
##  6 283d65eb-dd53-496d-adb7-7570… 7e0f266c-… <list>   <list>       9932 <list>   
##  7 283d65eb-dd53-496d-adb7-7570… 0834c8d1-… <list>   <list>      28724 <list>   
##  8 283d65eb-dd53-496d-adb7-7570… 4cce2e6f-… <list>   <list>      35359 <list>   
##  9 283d65eb-dd53-496d-adb7-7570… 05dd9bf7-… <list>   <list>      27210 <list>   
## 10 283d65eb-dd53-496d-adb7-7570… 5b994dc6-… <list>   <list>      26172 <list>   
## # ℹ 128 more rows
## # ℹ 20 more variables: dataset_deployments <chr>, development_stage <list>,
## #   disease <list>, is_primary_data <chr>, is_valid <lgl>,
## #   mean_genes_per_cell <dbl>, name <chr>, organism <list>,
## #   processing_status <list>, published <lgl>, revision <int>,
## #   schema_version <chr>, self_reported_ethnicity <list>, sex <list>,
## #   suspension_type <list>, tissue <list>, tombstone <lgl>, …

3.2 facets() provides information on ‘levels’ present in specific columns

Notice that some columns are ‘lists’ rather than atomic vectors like ‘character’ or ‘integer’.

datasets(db) |>
    select(where(is.list))
## # A tibble: 732 × 11
##    donor_id    assay      cell_type   development_stage disease    organism  
##    <list>      <list>     <list>      <list>            <list>     <list>    
##  1 <list [9]>  <list [1]> <list [8]>  <list [2]>        <list [1]> <list [1]>
##  2 <list [7]>  <list [2]> <list [11]> <list [6]>        <list [1]> <list [1]>
##  3 <list [3]>  <list [1]> <list [5]>  <list [3]>        <list [1]> <list [1]>
##  4 <list [6]>  <list [2]> <list [6]>  <list [5]>        <list [1]> <list [1]>
##  5 <list [4]>  <list [1]> <list [11]> <list [4]>        <list [1]> <list [1]>
##  6 <list [11]> <list [2]> <list [19]> <list [1]>        <list [1]> <list [1]>
##  7 <list [51]> <list [3]> <list [16]> <list [7]>        <list [1]> <list [1]>
##  8 <list [13]> <list [1]> <list [19]> <list [10]>       <list [2]> <list [1]>
##  9 <list [41]> <list [1]> <list [1]>  <list [24]>       <list [1]> <list [1]>
## 10 <list [41]> <list [1]> <list [10]> <list [24]>       <list [1]> <list [1]>
## # ℹ 722 more rows
## # ℹ 5 more variables: processing_status <list>, self_reported_ethnicity <list>,
## #   sex <list>, suspension_type <list>, tissue <list>

This indicates that at least some of the datasets had more than one type of assay, cell_type, etc. The facets() function provides a convenient way of discovering possible levels of each column, e.g., assay, organism, self_reported_ethnicity, or sex, and the number of datasets with each label.

facets(db, "assay")
## # A tibble: 32 × 4
##    facet label                          ontology_term_id     n
##    <chr> <chr>                          <chr>            <int>
##  1 assay 10x 3' v3                      EFO:0009922        365
##  2 assay 10x 3' v2                      EFO:0009899        189
##  3 assay Slide-seqV2                    EFO:0030062        129
##  4 assay 10x 5' v1                      EFO:0011025         54
##  5 assay Smart-seq2                     EFO:0008931         38
##  6 assay Visium Spatial Gene Expression EFO:0010961         35
##  7 assay 10x multiome                   EFO:0030059         25
##  8 assay Drop-seq                       EFO:0008722         12
##  9 assay 10x 3' transcription profiling EFO:0030003          9
## 10 assay 10x 5' v2                      EFO:0009900          9
## # ℹ 22 more rows
facets(db, "self_reported_ethnicity")
## # A tibble: 18 × 4
##    facet                   label                          ontology_term_id     n
##    <chr>                   <chr>                          <chr>            <int>
##  1 self_reported_ethnicity unknown                        unknown            372
##  2 self_reported_ethnicity European                       HANCESTRO:0005     212
##  3 self_reported_ethnicity na                             na                 184
##  4 self_reported_ethnicity Asian                          HANCESTRO:0008      83
##  5 self_reported_ethnicity African American               HANCESTRO:0568      39
##  6 self_reported_ethnicity multiethnic                    multiethnic         25
##  7 self_reported_ethnicity Greater Middle Eastern  (Midd… HANCESTRO:0015      21
##  8 self_reported_ethnicity Hispanic or Latin American     HANCESTRO:0014      16
##  9 self_reported_ethnicity African American or Afro-Cari… HANCESTRO:0016      10
## 10 self_reported_ethnicity East Asian                     HANCESTRO:0009       4
## 11 self_reported_ethnicity African                        HANCESTRO:0010       3
## 12 self_reported_ethnicity South Asian                    HANCESTRO:0006       2
## 13 self_reported_ethnicity Chinese                        HANCESTRO:0021       1
## 14 self_reported_ethnicity Eskimo                         HANCESTRO:0595       1
## 15 self_reported_ethnicity Finnish                        HANCESTRO:0321       1
## 16 self_reported_ethnicity Han Chinese                    HANCESTRO:0027       1
## 17 self_reported_ethnicity Oceanian                       HANCESTRO:0017       1
## 18 self_reported_ethnicity Pacific Islander               HANCESTRO:0574       1
facets(db, "sex")
## # A tibble: 3 × 4
##   facet label   ontology_term_id     n
##   <chr> <chr>   <chr>            <int>
## 1 sex   male    PATO:0000384       640
## 2 sex   female  PATO:0000383       396
## 3 sex   unknown unknown             57

3.3 Filtering faceted columns

Suppose we were interested in finding datasets from the 10x 3’ v3 assay (ontology_term_id of EFO:0009922) containing individuals of African American ethnicity, and female sex. Use the facets_filter() utility function to filter data sets as needed

african_american_female <-
    datasets(db) |>
    filter(
        facets_filter(assay, "ontology_term_id", "EFO:0009922"),
        facets_filter(self_reported_ethnicity, "label", "African American"),
        facets_filter(sex, "label", "female")
    )

Use nrow(african_american_female) to find the number of datasets satisfying our criteria. It looks like there are up to

african_american_female |>
    summarise(total_cell_count = sum(cell_count))
## # A tibble: 1 × 1
##   total_cell_count
##              <int>
## 1          2657433

cells sequenced (each dataset may contain cells from several ethnicities, as well as males or individuals of unknown gender, so we do not know the actual number of cells available without downloading files). Use left_join to identify the corresponding collections:

## collections
left_join(
    african_american_female |> select(collection_id) |> distinct(),
    collections(db),
    by = "collection_id"
)
## # A tibble: 8 × 15
##   collection_id    access_type consortia contact_email contact_name curator_name
##   <chr>            <chr>       <list>    <chr>         <chr>        <chr>       
## 1 bcb61471-2a44-4… READ        <list>    info@kpmp.org KPMP         Jennifer Yu…
## 2 2f75d249-1bec-4… READ        <list>    rsatija@nyge… Rahul Satija Jennifer Yu…
## 3 b953c942-f5d8-4… READ        <lgl [1]> icobos@stanf… Inma Cobos   Jennifer Yu…
## 4 c9706a92-0e5f-4… READ        <list>    hnakshat@iup… Harikrishna… Jennifer Yu…
## 5 a98b828a-622a-4… READ        <list>    markusbi@med… Markus Bitz… Jennifer Yu…
## 6 625f6bf4-2f33-4… READ        <list>    a5wang@healt… Allen Wang   Jennifer Yu…
## 7 b9fc3d70-5a72-4… READ        <list>    bruce.aronow… Bruce Aronow Jennifer Yu…
## 8 62e8f058-9c37-4… READ        <list>    chanj3@mskcc… Joseph Chan  Jennifer Yu…
## # ℹ 9 more variables: data_submission_policy_version <chr>, description <chr>,
## #   links <list>, name <chr>, publisher_metadata <list>, visibility <chr>,
## #   created_at <date>, published_at <date>, updated_at <date>

4 Visualizing data in cellxgene

Discover files associated with our first selected dataset

selected_files <-
    left_join(
        african_american_female |> select(dataset_id),
        files(db),
        by = "dataset_id"
    )
selected_files
## # A tibble: 88 × 8
##    dataset_id         file_id filename filetype s3_uri user_submitted created_at
##    <chr>              <chr>   <chr>    <chr>    <chr>  <lgl>          <date>    
##  1 8693bc2c-d84f-450… ceabe8… "raw.h5… RAW_H5AD s3://… TRUE           1970-01-01
##  2 8693bc2c-d84f-450… 0a9773… "local.… H5AD     s3://… TRUE           1970-01-01
##  3 8693bc2c-d84f-450… 03225d… "local.… RDS      s3://… TRUE           1970-01-01
##  4 8693bc2c-d84f-450… acd81a… ""       CXG      s3://… TRUE           1970-01-01
##  5 37f30005-8a5c-4bd… 62073a… "raw.h5… RAW_H5AD s3://… TRUE           1970-01-01
##  6 37f30005-8a5c-4bd… 1ada04… "local.… H5AD     s3://… TRUE           1970-01-01
##  7 37f30005-8a5c-4bd… 0808a4… ""       CXG      s3://… TRUE           1970-01-01
##  8 37f30005-8a5c-4bd… cabcb8… "local.… RDS      s3://… TRUE           1970-01-01
##  9 54914718-c0d3-4e1… 92372d… "raw.h5… RAW_H5AD s3://… TRUE           1970-01-01
## 10 54914718-c0d3-4e1… b642b4… "local.… H5AD     s3://… TRUE           1970-01-01
## # ℹ 78 more rows
## # ℹ 1 more variable: updated_at <date>

The filetype column lists the type of each file. The cellxgene service can be used to visualize datasets that have CXG files.

selected_files |>
    filter(filetype == "CXG") |>
    slice(1) |> # visualize a single dataset
    datasets_visualize()

Visualization is an interactive process, so datasets_visualize() will only open up to 5 browser tabs per call.

5 File download and use

Datasets usually contain CXG (cellxgene visualization), H5AD (files produced by the python AnnData module), and Rds (serialized files produced by the R Seurat package). There are no public parsers for CXG, and the Rds files may be unreadable if the version of Seurat used to create the file is different from the version used to read the file. We therefore focus on the H5AD files. For illustration, we download one of our selected files.

local_file <-
    selected_files |>
    filter(
        dataset_id == "24205601-0780-4bf2-b1d9-0e3cacbc2cd6",
        filetype == "H5AD"
    ) |>
    files_download(dry.run = FALSE)
basename(local_file)
## [1] "15e9d9af-60dc-4897-88e4-89842655d6b8.H5AD"

These are downloaded to a local cache (use the internal function cellxgenedp:::.cellxgenedb_cache_path() for the location of the cache), so the process is only time-consuming the first time.

H5AD files can be converted to R / Bioconductor objects using the zellkonverter package.

h5ad <- readH5AD(local_file, use_hdf5 = TRUE)
## Warning: 'X' matrix does not support transposition and has been skipped
h5ad
## class: SingleCellExperiment 
## dim: 33234 31696 
## metadata(3): default_embedding schema_version title
## assays(1): X
## rownames(33234): ENSG00000243485 ENSG00000237613 ... ENSG00000277475
##   ENSG00000268674
## rowData names(4): feature_is_filtered feature_name feature_reference
##   feature_biotype
## colnames(31696): CMGpool_AAACCCAAGGACAACC CMGpool_AAACCCACAATCTCTT ...
##   K109064_TTTGTTGGTTGCATCA K109064_TTTGTTGGTTGGACCC
## colData names(34): donor_id self_reported_ethnicity_ontology_term_id
##   ... self_reported_ethnicity development_stage
## reducedDimNames(3): X_pca X_tsne X_umap
## mainExpName: NULL
## altExpNames(0):

The SingleCellExperiment object is a matrix-like object with rows corresponding to genes and columns to cells. Thus we can easily explore the cells present in the data.

h5ad |>
    colData(h5ad) |>
    as_tibble() |>
    count(sex, donor_id)
## # A tibble: 7 × 3
##   sex    donor_id                     n
##   <fct>  <fct>                    <int>
## 1 female D1                        2303
## 2 female D2                         864
## 3 female D3                        2517
## 4 female D4                        1771
## 5 female D5                        2244
## 6 female D11                       7454
## 7 female pooled [D9,D7,D8,D10,D6] 14543

6 Next steps

The Orchestrating Single-Cell Analysis with Bioconductor online resource provides an excellent introduction to analysis and visualization of single-cell data in R / Bioconductor. Extensive opportunities for working with AnnData objects in R but using the native python interface are briefly described in, e.g., ?AnnData2SCE help page of zellkonverter.

The hca package provides programmatic access to the Human Cell Atlas data portal, allowing retrieval of primary as well as derived single-cell data files.

Session info

## R version 4.2.3 (2023-03-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] cellxgenedp_1.2.2           dplyr_1.1.1                
##  [3] SingleCellExperiment_1.20.1 SummarizedExperiment_1.28.0
##  [5] Biobase_2.58.0              GenomicRanges_1.50.2       
##  [7] GenomeInfoDb_1.34.9         IRanges_2.32.0             
##  [9] S4Vectors_0.36.2            BiocGenerics_0.44.0        
## [11] MatrixGenerics_1.10.0       matrixStats_0.63.0         
## [13] zellkonverter_1.8.0         BiocStyle_2.26.0           
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.10            here_1.0.1             dir.expiry_1.6.0      
##  [4] lattice_0.20-45        png_0.1-8              rprojroot_2.0.3       
##  [7] digest_0.6.31          utf8_1.2.3             mime_0.12             
## [10] R6_2.5.1               evaluate_0.20          httr_1.4.5            
## [13] pillar_1.9.0           basilisk_1.10.2        zlibbioc_1.44.0       
## [16] rlang_1.1.0            curl_5.0.0             jquerylib_0.1.4       
## [19] Matrix_1.5-3           DT_0.27                reticulate_1.28       
## [22] rmarkdown_2.20         htmlwidgets_1.6.2      RCurl_1.98-1.10       
## [25] shiny_1.7.4            DelayedArray_0.24.0    compiler_4.2.3        
## [28] httpuv_1.6.9           xfun_0.37              pkgconfig_2.0.3       
## [31] htmltools_0.5.4        tidyselect_1.2.0       tibble_3.2.1          
## [34] GenomeInfoDbData_1.2.9 bookdown_0.33          fansi_1.0.4           
## [37] withr_2.5.0            later_1.3.0            bitops_1.0-7          
## [40] rjsoncons_1.0.0        basilisk.utils_1.10.0  grid_4.2.3            
## [43] xtable_1.8-4           jsonlite_1.8.4         lifecycle_1.0.3       
## [46] magrittr_2.0.3         cli_3.6.0              cachem_1.0.7          
## [49] XVector_0.38.0         promises_1.2.0.1       bslib_0.4.2           
## [52] ellipsis_0.3.2         filelock_1.0.2         generics_0.1.3        
## [55] vctrs_0.6.1            tools_4.2.3            glue_1.6.2            
## [58] parallel_4.2.3         fastmap_1.1.1          yaml_2.3.7            
## [61] BiocManager_1.30.20    knitr_1.42             sass_0.4.5