The CELLxGENE data portal (https://cellxgene.cziscience.com/) provides a graphical user interface to collections of single-cell sequence data processed in standard ways to ‘count matrix’ summaries. The cellxgenedp package provides an alternative, R-based inteface, allowing flexible data discovery, viewing, and retrieval.
cellxgenedp 1.13.1
NOTE: The interface to CELLxGENE has changed; versions of cellxgenedp prior to 1.4.1 / 1.5.2 will cease to work when CELLxGENE removes the previous interface. See the vignette section ‘API changes’ for additional details.
This package is available in Bioconductor version 3.15 and later. The following code installs cellxgenedp from Bioconductor
if (!"BiocManager" %in% rownames(installed.packages()))
install.packages("BiocManager", repos = "https://CRAN.R-project.org")
BiocManager::install("cellxgenedp")Alternatively, install the ‘development’ version from GitHub (see GitHub.io for current documentation)
if (!"remotes" %in% rownames(installed.packages()))
install.packages("remotes", repos = "https://CRAN.R-project.org")
remotes::install_github("mtmorgan/cellxgenedp")To also install additional packages required for this vignette, use
pkgs <- c("tidyr", "zellkonverter", "SingleCellExperiment", "HDF5Array")
required_pkgs <- pkgs[!pkgs %in% rownames(installed.packages())]
BiocManager::install(required_pkgs)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.
library(zellkonverter)
library(SingleCellExperiment) # load early to avoid masking dplyr::count()
library(dplyr)
library(cellxgenedp)cxg() Provides a ‘shiny’ interfaceThe 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!
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(): 302
## number of datasets(): 1831
## number of files(): 1859The portal organizes data hierarchically, with ‘collections’ (research studies, approximately), ‘datasets’, and ‘files’. Discover data using the corresponding functions.
collections(db)## # A tibble: 302 × 18
## collection_id collection_version_id collection_url consortia contact_email
## <chr> <chr> <chr> <list> <chr>
## 1 e02201d7-f49f-4… f540732d-8fe8-4af1-8… https://cellx… <chr [1]> richard.smit…
## 2 33d19f34-87f5-4… 46f90bbb-80e9-49da-b… https://cellx… <chr [1]> fabian.theis…
## 3 5900dda8-2dc3-4… 673d6b45-e5dd-4c34-8… https://cellx… <chr [1]> ruichen@bcm.…
## 4 28e9d721-6816-4… 18e6e9fc-46d4-4c17-9… https://cellx… <lgl [1]> Tom_Mariani@…
## 5 e9cf4e8d-05ed-4… 6ebe6c8e-34d7-471b-8… https://cellx… <chr [1]> lungdrcho@sn…
## 6 7d7cabfd-1d1f-4… 0cf01a7c-c64f-4747-8… https://cellx… <chr [2]> jimmie.ye@uc…
## 7 367d95c0-0eb0-4… 5ce83dbf-14d5-4636-b… https://cellx… <chr [2]> edl@allenins…
## 8 b52eb423-5d0d-4… 81c94ceb-9410-43ab-9… https://cellx… <chr [2]> st9@sanger.a…
## 9 77446b76-1c2d-4… 07ea4287-932d-4873-a… https://cellx… <chr [1]> hnakshat@iu.…
## 10 74e10dc4-cbb2-4… 07dbcc54-9d4b-4609-9… https://cellx… <chr [1]> charlotte.sc…
## # ℹ 292 more rows
## # ℹ 13 more variables: contact_name <chr>, curator_name <chr>,
## # description <chr>, doi <chr>, links <list>, name <chr>,
## # publisher_metadata <list>, revising_in <lgl>, revision_of <lgl>,
## # visibility <chr>, created_at <date>, published_at <date>, revised_at <date>datasets(db)## # A tibble: 1,831 × 33
## dataset_id dataset_version_id collection_id donor_id assay batch_condition
## <chr> <chr> <chr> <list> <list> <list>
## 1 82f6af6d-53… 6649d5f0-3226-441… e02201d7-f49… <chr> <list> <lgl [1]>
## 2 39ed7d98-67… 1b43bc92-9c17-4b9… e02201d7-f49… <chr> <list> <lgl [1]>
## 3 4dd00779-7f… b37b1d6d-2f95-4b0… 33d19f34-87f… <chr> <list> <lgl [1]>
## 4 963a08cb-d9… 9fd6a2ab-c2f0-450… 5900dda8-2dc… <chr> <list> <lgl [1]>
## 5 6e00ccf7-07… a77fc250-40b8-442… 28e9d721-681… <chr> <list> <lgl [1]>
## 6 ef1a4b05-54… 993c6e92-2ae9-4d7… e9cf4e8d-05e… <chr> <list> <lgl [1]>
## 7 01ad3cd7-39… 1ee5d935-ec19-4b5… 7d7cabfd-1d1… <chr> <list> <lgl [1]>
## 8 f7a068f1-0f… b933380f-233c-473… 367d95c0-0eb… <chr> <list> <lgl [1]>
## 9 cb119d3c-07… 6874f0a0-79b7-4cc… 367d95c0-0eb… <chr> <list> <lgl [1]>
## 10 b6203114-e1… ee136403-047a-404… 367d95c0-0eb… <chr> <list> <lgl [1]>
## # ℹ 1,821 more rows
## # ℹ 27 more variables: cell_count <int>, cell_type <list>, citation <chr>,
## # default_embedding <chr>, development_stage <list>, disease <list>,
## # embeddings <list>, explorer_url <chr>, feature_biotype <list>,
## # feature_count <int>, feature_reference <list>, is_primary_data <list>,
## # mean_genes_per_cell <dbl>, organism <list>, primary_cell_count <int>,
## # raw_data_location <chr>, schema_version <chr>, …files(db)## # A tibble: 1,859 × 4
## dataset_id filesize filetype url
## <chr> <dbl> <chr> <chr>
## 1 82f6af6d-5313-439a-9936-5e844be49a70 158537379 H5AD https://datas…
## 2 39ed7d98-676d-4b8d-9d0a-0f3b60914ead 238784948 H5AD https://datas…
## 3 4dd00779-7f73-4f50-89bb-e2d3c6b71b18 224364348 H5AD https://datas…
## 4 963a08cb-d995-4862-9614-f9d38a7d7540 39907225440 ATAC_FRAGMENT https://datas…
## 5 963a08cb-d995-4862-9614-f9d38a7d7540 5843958 ATAC_INDEX https://datas…
## 6 963a08cb-d995-4862-9614-f9d38a7d7540 3090854453 H5AD https://datas…
## 7 6e00ccf7-0749-46ef-a999-dba785630d52 48266501 H5AD https://datas…
## 8 ef1a4b05-540c-4f73-8ceb-49dfa800645d 640370875 H5AD https://datas…
## 9 01ad3cd7-3929-4654-84c0-6db05bd5fd59 6312866445 H5AD https://datas…
## 10 f7a068f1-0fdb-48e8-8029-db870ff11d9e 116028071 H5AD https://datas…
## # ℹ 1,849 more rowsEach 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.
facets() provides information on ‘levels’ present in specific columnsNotice that some columns are ‘lists’ rather than atomic vectors like ‘character’ or ‘integer’.
datasets(db) |>
select(where(is.list))## # A tibble: 1,831 × 16
## donor_id assay batch_condition cell_type development_stage disease
## <list> <list> <list> <list> <list> <list>
## 1 <chr [1]> <list [1]> <lgl [1]> <list [3]> <list [1]> <list>
## 2 <chr [1]> <list [1]> <lgl [1]> <list [4]> <list [1]> <list>
## 3 <chr [12]> <list [2]> <lgl [1]> <list [23]> <list [1]> <list>
## 4 <chr [14]> <list [1]> <lgl [1]> <list [28]> <list [10]> <list>
## 5 <chr [2]> <list [1]> <lgl [1]> <list [11]> <list [1]> <list>
## 6 <chr [2]> <list [1]> <lgl [1]> <list [3]> <list [2]> <list>
## 7 <chr [80]> <list [1]> <lgl [1]> <list [11]> <list [43]> <list>
## 8 <chr [2]> <list [1]> <lgl [1]> <list [1]> <list [2]> <list>
## 9 <chr [2]> <list [1]> <lgl [1]> <list [1]> <list [1]> <list>
## 10 <chr [2]> <list [1]> <lgl [1]> <list [1]> <list [2]> <list>
## # ℹ 1,821 more rows
## # ℹ 10 more variables: embeddings <list>, feature_biotype <list>,
## # feature_reference <list>, is_primary_data <list>, organism <list>,
## # self_reported_ethnicity <list>, sex <list>, spatial <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: 45 × 4
## facet label ontology_term_id n
## <chr> <chr> <chr> <int>
## 1 assay 10x 3' v3 EFO:0009922 865
## 2 assay 10x 3' v2 EFO:0009899 379
## 3 assay Visium Spatial Gene Expression V1 EFO:0022857 317
## 4 assay Slide-seqV2 EFO:0030062 240
## 5 assay 10x 5' v1 EFO:0011025 119
## 6 assay sci-RNA-seq3 EFO:0030028 81
## 7 assay Smart-seq2 EFO:0008931 78
## 8 assay 10x multiome EFO:0030059 76
## 9 assay 10x 5' v2 EFO:0009900 66
## 10 assay Drop-seq EFO:0008722 27
## # ℹ 35 more rowsfacets(db, "self_reported_ethnicity")## # A tibble: 40 × 4
## facet label ontology_term_id n
## <chr> <chr> <chr> <int>
## 1 self_reported_ethnicity unknown unknown 761
## 2 self_reported_ethnicity European HANCESTRO:0005 707
## 3 self_reported_ethnicity na na 466
## 4 self_reported_ethnicity Asian HANCESTRO:0008 227
## 5 self_reported_ethnicity African American HANCESTRO:0568 124
## 6 self_reported_ethnicity Hispanic or Latin American HANCESTRO:0014 124
## 7 self_reported_ethnicity Native American,Hispanic or L… HANCESTRO:0013,… 50
## 8 self_reported_ethnicity African American or Afro-Cari… HANCESTRO:0016 43
## 9 self_reported_ethnicity South Asian HANCESTRO:0006 31
## 10 self_reported_ethnicity Greater Middle Eastern (Midd… HANCESTRO:0015 29
## # ℹ 30 more rowsfacets(db, "sex")## # A tibble: 3 × 4
## facet label ontology_term_id n
## <chr> <chr> <chr> <int>
## 1 sex male PATO:0000384 1294
## 2 sex female PATO:0000383 1100
## 3 sex unknown unknown 330Suppose 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 26987758cells 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: 36 × 18
## collection_id collection_version_id collection_url consortia contact_email
## <chr> <chr> <chr> <list> <chr>
## 1 21bbfaec-6958-4… 87e9c5ee-a0d0-4c1e-9… https://cellx… <lgl [1]> Thomas.pranz…
## 2 cee845e3-ec04-4… 1f502a5f-c16a-453c-9… https://cellx… <chr [1]> yuw1@chop.edu
## 3 c9706a92-0e5f-4… 3367da8a-774d-4bb1-b… https://cellx… <chr [1]> hnakshat@iup…
## 4 ec691f5f-0aac-4… 89fad7a4-9a83-4c7c-a… https://cellx… <chr [1]> df2396@cumc.…
## 5 72d37bc9-76cc-4… 554c82b5-8c37-416b-b… https://cellx… <chr [1]> m.sepp@zmbh.…
## 6 6b701826-37bb-4… 9d92543e-3093-4962-9… https://cellx… <chr [1]> astreets@ber…
## 7 f17b9205-f61f-4… d72779ea-069d-462b-a… https://cellx… <chr [1]> genevieve.ko…
## 8 61e422dd-c9cd-4… fe0b55e0-1496-439e-a… https://cellx… <lgl [1]> arul@med.umi…
## 9 de379e5f-52d0-4… 58cccb76-e600-48c7-9… https://cellx… <chr [1]> barbara.treu…
## 10 58e85c2f-d52e-4… e7d46cb4-1f8e-4fb2-b… https://cellx… <lgl [1]> efthymios.mo…
## # ℹ 26 more rows
## # ℹ 13 more variables: contact_name <chr>, curator_name <chr>,
## # description <chr>, doi <chr>, links <list>, name <chr>,
## # publisher_metadata <list>, revising_in <lgl>, revision_of <lgl>,
## # visibility <chr>, created_at <date>, published_at <date>, revised_at <date>Many collections include publication information and other external
data. This information is available in the return value of
collections(), but the helper function publisher_metadata(),
authors(), and links() may facilitate access.
Suppose one is interested in the publication “A single-cell atlas of the healthy breast tissues reveals clinically relevant clusters of breast epithelial cells”. Discover it in the collections
title_of_interest <- paste(
"A single-cell atlas of the healthy breast tissues reveals clinically",
"relevant clusters of breast epithelial cells"
)
collection_of_interest <-
collections(db) |>
dplyr::filter(startsWith(name, title_of_interest))
collection_of_interest |>
glimpse()## Rows: 1
## Columns: 18
## $ collection_id <chr> "c9706a92-0e5f-46c1-96d8-20e42467f287"
## $ collection_version_id <chr> "3367da8a-774d-4bb1-b5b8-5dc3e5dbbed7"
## $ collection_url <chr> "https://cellxgene.cziscience.com/collections/c9…
## $ consortia <list> "CZI Cell Science"
## $ contact_email <chr> "hnakshat@iupui.edu"
## $ contact_name <chr> "Harikrishna Nakshatri"
## $ curator_name <chr> "Jennifer Yu-Sheng Chien"
## $ description <chr> "Single-cell RNA sequencing (scRNA-seq) is an ev…
## $ doi <chr> "10.1016/j.xcrm.2021.100219"
## $ links <list> [["", "RAW_DATA", "https://explore.data.humancel…
## $ name <chr> "A single-cell atlas of the healthy breast tiss…
## $ publisher_metadata <list> [[["Bhat-Nakshatri", "Poornima"], ["Gao", "Hongy…
## $ revising_in <lgl> NA
## $ revision_of <lgl> NA
## $ visibility <chr> "PUBLIC"
## $ created_at <date> 2025-03-30
## $ published_at <date> 2021-03-25
## $ revised_at <date> 2025-04-14Use the collection_id to extract publisher metadata (including a DOI
if available) and author information
collection_id_of_interest <- pull(collection_of_interest, "collection_id")
publisher_metadata(db) |>
filter(collection_id == collection_id_of_interest) |>
glimpse()## Rows: 1
## Columns: 9
## $ collection_id <chr> "c9706a92-0e5f-46c1-96d8-20e42467f287"
## $ name <chr> "A single-cell atlas of the healthy breast tissues rev…
## $ is_preprint <lgl> FALSE
## $ journal <chr> "Cell Reports Medicine"
## $ published_year <int> 2021
## $ published_month <int> 3
## $ published_day <int> 1
## $ published_at <date> 2021-03-01
## $ doi <chr> NAauthors(db) |>
filter(collection_id == collection_id_of_interest)## # A tibble: 12 × 4
## collection_id family given consortium
## <chr> <chr> <chr> <chr>
## 1 c9706a92-0e5f-46c1-96d8-20e42467f287 Bhat-Nakshatri Poornima <NA>
## 2 c9706a92-0e5f-46c1-96d8-20e42467f287 Gao Hongyu <NA>
## 3 c9706a92-0e5f-46c1-96d8-20e42467f287 Sheng Liu <NA>
## 4 c9706a92-0e5f-46c1-96d8-20e42467f287 McGuire Patrick C. <NA>
## 5 c9706a92-0e5f-46c1-96d8-20e42467f287 Xuei Xiaoling <NA>
## 6 c9706a92-0e5f-46c1-96d8-20e42467f287 Wan Jun <NA>
## 7 c9706a92-0e5f-46c1-96d8-20e42467f287 Liu Yunlong <NA>
## 8 c9706a92-0e5f-46c1-96d8-20e42467f287 Althouse Sandra K. <NA>
## 9 c9706a92-0e5f-46c1-96d8-20e42467f287 Colter Austyn <NA>
## 10 c9706a92-0e5f-46c1-96d8-20e42467f287 Sandusky George <NA>
## 11 c9706a92-0e5f-46c1-96d8-20e42467f287 Storniolo Anna Maria <NA>
## 12 c9706a92-0e5f-46c1-96d8-20e42467f287 Nakshatri Harikrishna <NA>Collections may have links to additional external data, in this case a
DOI and two links to RAW_DATA.
external_links <- links(db)
external_links## # A tibble: 1,009 × 4
## collection_id link_name link_type link_url
## <chr> <chr> <chr> <chr>
## 1 e02201d7-f49f-401f-baf0-1eb1406546c0 phs001272 RAW_DATA https://www.ncbi.…
## 2 33d19f34-87f5-455b-8ca5-9023a2e5453d <NA> OTHER https://explore.d…
## 3 33d19f34-87f5-455b-8ca5-9023a2e5453d <NA> OTHER https://singlecel…
## 4 33d19f34-87f5-455b-8ca5-9023a2e5453d <NA> OTHER https://cells.ucs…
## 5 5900dda8-2dc3-4770-b604-084eac1c2c82 <NA> RAW_DATA https://explore.d…
## 6 5900dda8-2dc3-4770-b604-084eac1c2c82 GSE268630 RAW_DATA https://www.ncbi.…
## 7 28e9d721-6816-48a2-8d0b-43bf0b0c0ebc <NA> PROTOCOL https://www.proto…
## 8 28e9d721-6816-48a2-8d0b-43bf0b0c0ebc <NA> DATA_SOURCE https://lungmap.n…
## 9 e9cf4e8d-05ed-4d95-b550-af35ca219390 GSE280502 RAW_DATA https://www.ncbi.…
## 10 7d7cabfd-1d1f-40af-96b7-26a0825a306d <NA> PROTOCOL http://www.protoc…
## # ℹ 999 more rowsexternal_links |>
count(link_type)## # A tibble: 5 × 2
## link_type n
## <chr> <int>
## 1 DATA_SOURCE 71
## 2 LAB_WEBSITE 49
## 3 OTHER 433
## 4 PROTOCOL 55
## 5 RAW_DATA 401external_links |>
filter(collection_id == collection_id_of_interest)## # A tibble: 2 × 4
## collection_id link_name link_type link_url
## <chr> <chr> <chr> <chr>
## 1 c9706a92-0e5f-46c1-96d8-20e42467f287 <NA> RAW_DATA https://explore.data…
## 2 c9706a92-0e5f-46c1-96d8-20e42467f287 <NA> RAW_DATA https://www.ncbi.nlm…Conversely, knowledge of a DOI, etc., can be used to discover details of the corresponding collection.
doi_of_interest <- "https://doi.org/10.1016/j.stem.2018.12.011"
links(db) |>
filter(link_url == doi_of_interest) |>
left_join(collections(db), by = "collection_id") |>
glimpse()## Rows: 1
## Columns: 21
## $ collection_id <chr> "b1a879f6-5638-48d3-8f64-f6592c1b1561"
## $ link_name <chr> "PSC-ATO protocol"
## $ link_type <chr> "PROTOCOL"
## $ link_url <chr> "https://doi.org/10.1016/j.stem.2018.12.011"
## $ collection_version_id <chr> "81a0bf32-b224-40c7-bbdd-5026abd00ab8"
## $ collection_url <chr> "https://cellxgene.cziscience.com/collections/b1…
## $ consortia <list> <"CZI Cell Science", "Wellcome HCA Strategic Sci…
## $ contact_email <chr> "st9@sanger.ac.uk"
## $ contact_name <chr> "Sarah Teichmann"
## $ curator_name <chr> "Jennifer Yu-Sheng Chien"
## $ description <chr> "Single-cell genomics studies have decoded the i…
## $ doi <chr> "10.1126/science.abo0510"
## $ links <list> [["scVI Models", "DATA_SOURCE", "https://develop…
## $ name <chr> "Mapping the developing human immune system acro…
## $ publisher_metadata <list> [[["Suo", "Chenqu"], ["Dann", "Emma"], ["Goh", "…
## $ revising_in <lgl> NA
## $ revision_of <lgl> NA
## $ visibility <chr> "PUBLIC"
## $ created_at <date> 2025-06-26
## $ published_at <date> 2022-10-04
## $ revised_at <date> 2025-06-27cellxgeneVisualization is straight-forward once dataset_id is available. For
example, to visualize the first dataset in african_american_female,
use
african_american_female |>
## use criteria to identify a single dataset (here just the
## 'first' dataset), then visualize
slice(1) |>
datasets_visualize()Visualization is an interactive process, so datasets_visualize()
will only open up to 5 browser tabs per call.
Datasets usually contain H5AD (files produced by the python AnnData
module), and Rds (serialized files produced by the R Seurat
package). 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 find all files associated with studies with African American females
download one of our selected files.
selected_files <-
left_join(
african_american_female |> select(dataset_id),
files(db),
by = "dataset_id"
)And then choose a single dataset and its H5AD file for download
local_file <-
selected_files |>
filter(
dataset_id == "de985818-285f-4f59-9dbd-d74968fddba3",
filetype == "H5AD"
) |>
files_download(dry.run = FALSE)
basename(local_file)## [1] "51ba9fdd-3b84-404b-91df-ee630823d716.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, reader = "R")
h5ad## class: SingleCellExperiment
## dim: 33159 31696
## metadata(5): citation default_embedding schema_reference schema_version
## title
## assays(1): X
## rownames(33159): ENSG00000243485 ENSG00000237613 ... ENSG00000277475
## ENSG00000268674
## rowData names(6): feature_is_filtered feature_name ... feature_length
## feature_type
## colnames(31696): CMGpool_AAACCCAAGGACAACC CMGpool_AAACCCACAATCTCTT ...
## K109064_TTTGTTGGTTGCATCA K109064_TTTGTTGGTTGGACCC
## colData names(45): mapped_reference_annotation donor_id ...
## development_stage observation_joinid
## 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] 14543The 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.
Data access provided by CELLxGENE has changed to a new ‘Discover’ API. The main functionality of the cellxgenedp package has not changed, but specific columns have been removed, replaced or added, as follows:
collections()
access_type, data_submission_policy_versionupdated_at replaced with revised_atcollection_version_id, collection_url, doi,
revising_in, revision_ofdatasets()
is_valid, processing_status, published, revision,
created_atdataset_deployments replaced with explorer_url, name
replaced with title, updated_at replaced with revised_atdataset_version_id, batch_condition,
x_approximate_distributionfiles()
file_id, filename, s3_uri, user_submitted,
created_at, updated_atfilesize, url## R version 4.5.1 (2025-06-13)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [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
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] cellxgenedp_1.13.1 dplyr_1.1.4
## [3] SingleCellExperiment_1.31.1 SummarizedExperiment_1.39.1
## [5] Biobase_2.69.0 GenomicRanges_1.61.1
## [7] Seqinfo_0.99.1 IRanges_2.43.0
## [9] S4Vectors_0.47.0 BiocGenerics_0.55.0
## [11] generics_0.1.4 MatrixGenerics_1.21.0
## [13] matrixStats_1.5.0 zellkonverter_1.19.2
## [15] BiocStyle_2.37.0
##
## loaded via a namespace (and not attached):
## [1] dir.expiry_1.17.0 xfun_0.52 bslib_0.9.0
## [4] htmlwidgets_1.6.4 rhdf5_2.53.1 lattice_0.22-7
## [7] rhdf5filters_1.21.0 vctrs_0.6.5 rjsoncons_1.3.2
## [10] tools_4.5.1 curl_6.4.0 parallel_4.5.1
## [13] tibble_3.3.0 pkgconfig_2.0.3 Matrix_1.7-3
## [16] lifecycle_1.0.4 compiler_4.5.1 httpuv_1.6.16
## [19] htmltools_0.5.8.1 sass_0.4.10 yaml_2.3.10
## [22] pillar_1.11.0 later_1.4.2 crayon_1.5.3
## [25] jquerylib_0.1.4 DT_0.33 DelayedArray_0.35.2
## [28] cachem_1.1.0 abind_1.4-8 mime_0.13
## [31] basilisk_1.21.5 tidyselect_1.2.1 digest_0.6.37
## [34] bookdown_0.43 fastmap_1.2.0 grid_4.5.1
## [37] cli_3.6.5 SparseArray_1.9.0 magrittr_2.0.3
## [40] S4Arrays_1.9.1 h5mread_1.1.1 utf8_1.2.6
## [43] withr_3.0.2 filelock_1.0.3 promises_1.3.3
## [46] rmarkdown_2.29 XVector_0.49.0 httr_1.4.7
## [49] reticulate_1.42.0 png_0.1-8 HDF5Array_1.37.0
## [52] shiny_1.11.1 evaluate_1.0.4 knitr_1.50
## [55] rlang_1.1.6 Rcpp_1.1.0 xtable_1.8-4
## [58] glue_1.8.0 BiocManager_1.30.26 jsonlite_2.0.0
## [61] Rhdf5lib_1.31.0 R6_2.6.1