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 downloading.
cellxgenedp 1.4.1
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 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)
})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(): 152
## number of datasets(): 910
## number of files(): 1802The portal organizes data hierarchically, with ‘collections’ (research studies, approximately), ‘datasets’, and ‘files’. Discover data using the corresponding functions.
collections(db)## # A tibble: 152 × 18
## collection_id collection_version_id collection_url consortia contact_email
## <chr> <chr> <chr> <list> <chr>
## 1 e75342a8-0f3b-4… 2d569157-4335-40d6-a… https://cellx… <list> nhuebner@mdc…
## 2 661a402a-2a5a-4… 626b26f4-a84c-4f31-8… https://cellx… <lgl [1]> rv4@sanger.a…
## 3 367d95c0-0eb0-4… 4216ddce-94c0-4fdc-9… https://cellx… <list> edl@allenins…
## 4 af893e86-8e9f-4… fc8a8009-02f0-4084-9… https://cellx… <list> ruichen@bcm.…
## 5 48d354f5-a5ca-4… 54caf53d-0a4d-4874-9… https://cellx… <list> Nathan.Salom…
## 6 793fdaec-5067-4… b4431833-4155-48d7-8… https://cellx… <list> m.a.haniffa@…
## 7 13d1c580-4b17-4… c7b93415-bf09-45df-9… https://cellx… <list> my4@sanger.a…
## 8 fbc5881f-1ee3-4… 4f2da30d-407b-4c94-8… https://cellx… <list> Douglas.Stra…
## 9 c114c20f-1ef4-4… 871e2180-9ac4-4025-8… https://cellx… <lgl [1]> shendure@uw.…
## 10 c8565c6a-01a1-4… 07bfe4f4-61bc-463c-a… https://cellx… <list> carmen.sando…
## # ℹ 142 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: 910 × 24
## dataset_id dataset_version_id collection_id donor_id assay batch_condition
## <chr> <chr> <chr> <list> <list> <list>
## 1 f7995301-75… 0a4f9a00-6f75-4ff… e75342a8-0f3… <list> <list> <lgl [1]>
## 2 ed2b673b-02… 61640b98-af3d-4b9… e75342a8-0f3… <list> <list> <lgl [1]>
## 3 bdf69f8d-5a… f40a4b36-e499-48f… e75342a8-0f3… <list> <list> <lgl [1]>
## 4 9434b020-de… 2a96a174-e168-40f… e75342a8-0f3… <list> <list> <lgl [1]>
## 5 83b5e943-a1… 1e9414d2-e347-467… e75342a8-0f3… <list> <list> <lgl [1]>
## 6 65badd7a-92… ae6ef28f-cabc-48d… e75342a8-0f3… <list> <list> <lgl [1]>
## 7 1252c5fb-94… 65df6878-8cc6-49c… e75342a8-0f3… <list> <list> <lgl [1]>
## 8 1062c0f2-2a… 323243d7-0c21-461… e75342a8-0f3… <list> <list> <lgl [1]>
## 9 0fdb6122-46… 45dd32d7-00ff-4a1… e75342a8-0f3… <list> <list> <lgl [1]>
## 10 be46dfdc-0f… 7469da86-82cf-4d7… 661a402a-2a5… <list> <list> <lgl [1]>
## # ℹ 900 more rows
## # ℹ 18 more variables: cell_count <int>, cell_type <list>,
## # development_stage <list>, disease <list>, explorer_url <chr>,
## # is_primary_data <list>, mean_genes_per_cell <dbl>, organism <list>,
## # schema_version <chr>, self_reported_ethnicity <list>, sex <list>,
## # suspension_type <list>, tissue <list>, title <chr>, tombstone <lgl>,
## # x_approximate_distribution <chr>, published_at <date>, revised_at <date>files(db)## # A tibble: 1,802 × 4
## dataset_id filesize filetype url
## <chr> <dbl> <chr> <chr>
## 1 f7995301-7551-4e1d-8396-ffe3c9497ace 3255625301 H5AD https://datasets.ce…
## 2 f7995301-7551-4e1d-8396-ffe3c9497ace 3234403317 RDS https://datasets.ce…
## 3 ed2b673b-0279-454a-998c-3eec361edf54 1010106545 H5AD https://datasets.ce…
## 4 ed2b673b-0279-454a-998c-3eec361edf54 967955201 RDS https://datasets.ce…
## 5 bdf69f8d-5a96-4d6f-a9f5-9ee0e33597b7 35165722 H5AD https://datasets.ce…
## 6 bdf69f8d-5a96-4d6f-a9f5-9ee0e33597b7 26133065 RDS https://datasets.ce…
## 7 9434b020-de42-43eb-bcc4-542b2be69015 860641548 H5AD https://datasets.ce…
## 8 9434b020-de42-43eb-bcc4-542b2be69015 934357743 RDS https://datasets.ce…
## 9 83b5e943-a1d5-4164-b3f2-f7a37f01b524 134378259 H5AD https://datasets.ce…
## 10 83b5e943-a1d5-4164-b3f2-f7a37f01b524 141856536 RDS https://datasets.ce…
## # ℹ 1,792 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: 910 × 12
## donor_id assay batch_condition cell_type development_stage disease
## <list> <list> <list> <list> <list> <list>
## 1 <list [79]> <list [2]> <lgl [1]> <list [1]> <list [10]> <list>
## 2 <list [79]> <list [2]> <lgl [1]> <list [1]> <list [10]> <list>
## 3 <list [66]> <list [2]> <lgl [1]> <list [1]> <list [10]> <list>
## 4 <list [79]> <list [2]> <lgl [1]> <list [2]> <list [10]> <list>
## 5 <list [79]> <list [2]> <lgl [1]> <list [1]> <list [10]> <list>
## 6 <list [79]> <list [2]> <lgl [1]> <list [10]> <list [10]> <list>
## 7 <list [79]> <list [2]> <lgl [1]> <list [1]> <list [10]> <list>
## 8 <list [79]> <list [2]> <lgl [1]> <list [1]> <list [10]> <list>
## 9 <list [79]> <list [2]> <lgl [1]> <list [2]> <list [10]> <list>
## 10 <list [13]> <list [3]> <lgl [1]> <list [8]> <list [8]> <list>
## # ℹ 900 more rows
## # ℹ 6 more variables: is_primary_data <list>, organism <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: 33 × 4
## facet label ontology_term_id n
## <chr> <chr> <chr> <int>
## 1 assay 10x 3' v3 EFO:0009922 499
## 2 assay 10x 3' v2 EFO:0009899 229
## 3 assay Slide-seqV2 EFO:0030062 129
## 4 assay 10x 5' v1 EFO:0011025 69
## 5 assay Smart-seq2 EFO:0008931 62
## 6 assay Visium Spatial Gene Expression EFO:0010961 56
## 7 assay 10x multiome EFO:0030059 54
## 8 assay 10x 5' v2 EFO:0009900 17
## 9 assay 10x 5' transcription profiling EFO:0030004 13
## 10 assay Drop-seq EFO:0008722 12
## # ℹ 23 more rowsfacets(db, "self_reported_ethnicity")## # A tibble: 30 × 4
## facet label ontology_term_id n
## <chr> <chr> <chr> <int>
## 1 self_reported_ethnicity European HANCESTRO:0005 431
## 2 self_reported_ethnicity unknown unknown 311
## 3 self_reported_ethnicity na na 212
## 4 self_reported_ethnicity Asian HANCESTRO:0008 130
## 5 self_reported_ethnicity African American HANCESTRO:0568 57
## 6 self_reported_ethnicity Hispanic or Latin American HANCESTRO:0014 41
## 7 self_reported_ethnicity admixed ancestry HANCESTRO:0306 28
## 8 self_reported_ethnicity African American or Afro-Cari… HANCESTRO:0016 26
## 9 self_reported_ethnicity multiethnic multiethnic 25
## 10 self_reported_ethnicity Greater Middle Eastern (Midd… HANCESTRO:0015 22
## # ℹ 20 more rowsfacets(db, "sex")## # A tibble: 3 × 4
## facet label ontology_term_id n
## <chr> <chr> <chr> <int>
## 1 sex male PATO:0000384 772
## 2 sex female PATO:0000383 554
## 3 sex unknown unknown 71Suppose 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 3293238cells 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: 9 × 18
## collection_id collection_version_id collection_url consortia contact_email
## <chr> <chr> <chr> <list> <chr>
## 1 4195ab4c-20bd-4c… 62466cd5-fca8-4961-b… https://cellx… <list> nnavin@mdand…
## 2 b9fc3d70-5a72-44… b659b6b3-7663-41f8-8… https://cellx… <list> bruce.aronow…
## 3 625f6bf4-2f33-49… 47a89d52-954c-428a-a… https://cellx… <list> a5wang@healt…
## 4 a98b828a-622a-48… 2be54f40-4035-4c92-b… https://cellx… <list> markusbi@med…
## 5 bcb61471-2a44-4d… 346be1d3-d745-45f5-a… https://cellx… <list> info@kpmp.org
## 6 6b701826-37bb-43… fc5d2347-b859-4744-a… https://cellx… <list> astreets@ber…
## 7 62e8f058-9c37-48… 8fc72e6e-b4f8-4f64-8… https://cellx… <list> chanj3@mskcc…
## 8 b953c942-f5d8-43… d221209d-610d-47f0-b… https://cellx… <lgl [1]> icobos@stanf…
## 9 c9706a92-0e5f-46… 184e8999-210d-47e5-a… https://cellx… <list> hnakshat@iup…
## # 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 facilite 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> "184e8999-210d-47e5-aaa8-56224a925a11"
## $ collection_url <chr> "https://cellxgene.cziscience.com/collections/c9…
## $ consortia <list> ["CZI Single-Cell Biology"]
## $ 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://data.humancellatlas.o…
## $ 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> 2023-08-22
## $ published_at <date> 2021-03-25
## $ revised_at <date> 2023-08-22Use 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_at <date> 2021-03-01
## $ published_year <int> 2021
## $ published_month <int> 3
## $ published_day <int> 1
## $ 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: 591 × 4
## collection_id link_name link_type link_url
## <chr> <chr> <chr> <chr>
## 1 e75342a8-0f3b-4ec5-8ee1-245a23e0f7cb <NA> OTHER https:/…
## 2 e75342a8-0f3b-4ec5-8ee1-245a23e0f7cb <NA> OTHER https:/…
## 3 e75342a8-0f3b-4ec5-8ee1-245a23e0f7cb <NA> RAW_DATA https:/…
## 4 661a402a-2a5a-4c71-9b05-b346c57bc451 Human scRNA-seq (E-M… RAW_DATA https:/…
## 5 661a402a-2a5a-4c71-9b05-b346c57bc451 Mouse scRNA-seq (E-M… RAW_DATA https:/…
## 6 661a402a-2a5a-4c71-9b05-b346c57bc451 Reproductive Cell At… OTHER http://…
## 7 661a402a-2a5a-4c71-9b05-b346c57bc451 VenTo Lab LAB_WEBS… https:/…
## 8 367d95c0-0eb0-4dae-8276-9407239421ee Nuclei Isolation fro… PROTOCOL https:/…
## 9 367d95c0-0eb0-4dae-8276-9407239421ee Human Tissue Slicing… PROTOCOL https:/…
## 10 367d95c0-0eb0-4dae-8276-9407239421ee NeMo Analytics - ind… OTHER https:/…
## # ℹ 581 more rowsexternal_links |>
count(link_type)## # A tibble: 5 × 2
## link_type n
## <chr> <int>
## 1 DATA_SOURCE 51
## 2 LAB_WEBSITE 33
## 3 OTHER 264
## 4 PROTOCOL 40
## 5 RAW_DATA 203external_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://data.humance…
## 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> "01357a8e-547f-470d-9958-725b38adca04"
## $ collection_url <chr> "https://cellxgene.cziscience.com/collections/b1…
## $ consortia <list> ["CZI Single-Cell Biology", "Wellcome HCA Strate…
## $ contact_email <chr> "st9@sanger.ac.uk"
## $ contact_name <chr> "Sarah Teichmann"
## $ curator_name <chr> "Batuhan Cakir"
## $ 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> 2023-08-22
## $ published_at <date> 2022-10-04
## $ revised_at <date> 2023-08-24cellxgeneDiscover 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: 64 × 4
## dataset_id filesize filetype url
## <chr> <dbl> <chr> <chr>
## 1 e47c65a8-7d2f-48b8-908e-04ea6505fa26 800797163 H5AD https://datasets.ce…
## 2 e47c65a8-7d2f-48b8-908e-04ea6505fa26 773360314 RDS https://datasets.ce…
## 3 c8d40d53-387b-48f2-9f89-72bfdb9c7c9f 385922942 H5AD https://datasets.ce…
## 4 c8d40d53-387b-48f2-9f89-72bfdb9c7c9f 362851875 RDS https://datasets.ce…
## 5 a6388a6f-6076-401b-9b30-7d4306a20035 315326067 H5AD https://datasets.ce…
## 6 a6388a6f-6076-401b-9b30-7d4306a20035 302258458 RDS https://datasets.ce…
## 7 a41202e6-173c-477c-8b4d-e0688ee1c4cb 82026236 H5AD https://datasets.ce…
## 8 a41202e6-173c-477c-8b4d-e0688ee1c4cb 74894351 RDS https://datasets.ce…
## 9 842c6f5d-4a94-4eef-8510-8c792d1124bc 7211362715 H5AD https://datasets.ce…
## 10 842c6f5d-4a94-4eef-8510-8c792d1124bc 6817801616 RDS https://datasets.ce…
## # ℹ 54 more rowsThe 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.
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 == "de985818-285f-4f59-9dbd-d74968fddba3",
filetype == "H5AD"
) |>
files_download(dry.run = FALSE)
basename(local_file)## [1] "64942e4e-3f6e-4ca0-8226-62e8491b5786.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")## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.
## Warning in H5Aread(A, ...): Reading attribute data of type 'ENUM' not yet
## implemented. Values replaced by NA's.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] 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 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## 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_1.4.1 dplyr_1.1.2
## [3] SingleCellExperiment_1.22.0 SummarizedExperiment_1.30.2
## [5] Biobase_2.60.0 GenomicRanges_1.52.0
## [7] GenomeInfoDb_1.36.2 IRanges_2.34.1
## [9] S4Vectors_0.38.1 BiocGenerics_0.46.0
## [11] MatrixGenerics_1.12.3 matrixStats_1.0.0
## [13] zellkonverter_1.10.1 BiocStyle_2.28.0
##
## loaded via a namespace (and not attached):
## [1] dir.expiry_1.8.0 xfun_0.40 bslib_0.5.1
## [4] htmlwidgets_1.6.2 rhdf5_2.44.0 lattice_0.21-8
## [7] rhdf5filters_1.12.1 rjsoncons_1.0.0 vctrs_0.6.3
## [10] tools_4.3.1 bitops_1.0-7 generics_0.1.3
## [13] curl_5.0.2 parallel_4.3.1 tibble_3.2.1
## [16] fansi_1.0.4 pkgconfig_2.0.3 Matrix_1.6-1
## [19] lifecycle_1.0.3 GenomeInfoDbData_1.2.10 compiler_4.3.1
## [22] httpuv_1.6.11 htmltools_0.5.6 sass_0.4.7
## [25] RCurl_1.98-1.12 yaml_2.3.7 later_1.3.1
## [28] pillar_1.9.0 crayon_1.5.2 jquerylib_0.1.4
## [31] ellipsis_0.3.2 DT_0.28 DelayedArray_0.26.7
## [34] cachem_1.0.8 abind_1.4-5 mime_0.12
## [37] basilisk_1.12.1 tidyselect_1.2.0 digest_0.6.33
## [40] bookdown_0.35 fastmap_1.1.1 grid_4.3.1
## [43] cli_3.6.1 magrittr_2.0.3 S4Arrays_1.0.5
## [46] utf8_1.2.3 withr_2.5.0 promises_1.2.1
## [49] filelock_1.0.2 rmarkdown_2.24 XVector_0.40.0
## [52] httr_1.4.7 reticulate_1.31 png_0.1-8
## [55] HDF5Array_1.28.1 shiny_1.7.5 evaluate_0.21
## [58] knitr_1.43 basilisk.utils_1.12.1 rlang_1.1.1
## [61] Rcpp_1.0.11 xtable_1.8-4 glue_1.6.2
## [64] BiocManager_1.30.22 jsonlite_1.8.7 Rhdf5lib_1.22.0
## [67] R6_2.5.1 zlibbioc_1.46.0