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 1.6.2
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(): 195
## number of datasets(): 1275
## number of files(): 2518The portal organizes data hierarchically, with ‘collections’ (research studies, approximately), ‘datasets’, and ‘files’. Discover data using the corresponding functions.
collections(db)## # A tibble: 195 × 18
## collection_id collection_version_id collection_url consortia contact_email
## <chr> <chr> <chr> <list> <chr>
## 1 59c9ecfe-c47d-4… 8db59637-4b42-4195-b… https://cellx… <lgl [1]> twc@stanford…
## 2 a18474f4-ff1e-4… e1078e5b-da88-49d8-b… https://cellx… <lgl [1]> kpn2114@colu…
## 3 f5af7a2f-ab4c-4… cb80d978-a1a2-4696-8… https://cellx… <lgl [1]> Yanling_Liao…
## 4 aee9c366-f2fb-4… 62fc92b9-92f9-4f6f-9… https://cellx… <lgl [1]> kjensen@tgen…
## 5 e5f58829-1a66-4… 519f5ac5-1f84-4b48-9… https://cellx… <chr [2]> angela.pisco…
## 6 b52eb423-5d0d-4… e75822ca-72d5-4d7f-a… https://cellx… <chr [2]> st9@sanger.a…
## 7 e3aa612b-0d7d-4… 8bfe4964-eab1-4ae8-b… https://cellx… <chr [1]> hongkuiz@all…
## 8 1b014f39-f202-4… 8b7765f4-c81a-46fe-a… https://cellx… <lgl [1]> kimberly.ald…
## 9 48d354f5-a5ca-4… be9483df-dbbd-421f-b… https://cellx… <chr [1]> Nathan.Salom…
## 10 37f1f46d-6dfa-4… 55fe1b45-a16f-4d6c-a… https://cellx… <lgl [1]> ecker@salk.e…
## # ℹ 185 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: 1,275 × 32
## dataset_id dataset_version_id collection_id donor_id assay batch_condition
## <chr> <chr> <chr> <list> <list> <list>
## 1 595c9010-99… b4645848-e3d8-492… 59c9ecfe-c47… <chr> <list> <lgl [1]>
## 2 2f05ab20-a0… 3b715360-b0ae-4e5… 59c9ecfe-c47… <chr> <list> <lgl [1]>
## 3 faed4f71-6b… 1cd4f84b-7fe4-463… a18474f4-ff1… <chr> <list> <lgl [1]>
## 4 e5233a94-9e… 80d1f22a-8f51-49d… a18474f4-ff1… <chr> <list> <lgl [1]>
## 5 9b188f26-c8… 1c158adf-90ea-47b… a18474f4-ff1… <chr> <list> <lgl [1]>
## 6 94423ec1-21… 7c4220a8-ce41-49f… a18474f4-ff1… <chr> <list> <lgl [1]>
## 7 7bb64315-9e… 9ec33c56-14a4-481… a18474f4-ff1… <chr> <list> <lgl [1]>
## 8 773b9b2e-70… f186deb1-4926-404… a18474f4-ff1… <chr> <list> <lgl [1]>
## 9 63bb6359-39… a5df7f5b-34d8-421… a18474f4-ff1… <chr> <list> <lgl [1]>
## 10 03d5794d-cd… 8db806f1-a518-4a2… a18474f4-ff1… <chr> <list> <lgl [1]>
## # ℹ 1,265 more rows
## # ℹ 26 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: 2,518 × 4
## dataset_id filesize filetype url
## <chr> <dbl> <chr> <chr>
## 1 595c9010-99ec-462d-b6a1-2b2fe5407871 333391557 H5AD https://datasets.ce…
## 2 595c9010-99ec-462d-b6a1-2b2fe5407871 348066008 RDS https://datasets.ce…
## 3 2f05ab20-a092-4bab-9276-3e0eb24e3fee 630668977 H5AD https://datasets.ce…
## 4 2f05ab20-a092-4bab-9276-3e0eb24e3fee 683935787 RDS https://datasets.ce…
## 5 faed4f71-6b50-4fc7-bd1c-8f385dccfdce 31737910 H5AD https://datasets.ce…
## 6 faed4f71-6b50-4fc7-bd1c-8f385dccfdce 26350706 RDS https://datasets.ce…
## 7 e5233a94-9e43-418c-8209-6f1400c31530 1321449650 H5AD https://datasets.ce…
## 8 e5233a94-9e43-418c-8209-6f1400c31530 1311136088 RDS https://datasets.ce…
## 9 9b188f26-c8e1-4a78-af15-622a35a371fc 36324999 H5AD https://datasets.ce…
## 10 9b188f26-c8e1-4a78-af15-622a35a371fc 30585807 RDS https://datasets.ce…
## # ℹ 2,508 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: 1,275 × 15
## donor_id assay batch_condition cell_type development_stage disease
## <list> <list> <list> <list> <list> <list>
## 1 <chr [14]> <list [1]> <lgl [1]> <list [7]> <list [13]> <list>
## 2 <chr [16]> <list [1]> <lgl [1]> <list [13]> <list [14]> <list>
## 3 <chr [39]> <list [2]> <lgl [1]> <list [3]> <list [25]> <list>
## 4 <chr [43]> <list [2]> <lgl [1]> <list [31]> <list [28]> <list>
## 5 <chr [37]> <list [2]> <lgl [1]> <list [1]> <list [23]> <list>
## 6 <chr [42]> <list [2]> <lgl [1]> <list [5]> <list [27]> <list>
## 7 <chr [70]> <list [3]> <lgl [1]> <list [31]> <list [39]> <list>
## 8 <chr [42]> <list [2]> <lgl [1]> <list [10]> <list [27]> <list>
## 9 <chr [41]> <list [2]> <lgl [1]> <list [7]> <list [26]> <list>
## 10 <chr [40]> <list [2]> <lgl [1]> <list [4]> <list [26]> <list>
## # ℹ 1,265 more rows
## # ℹ 9 more variables: embeddings <list>, feature_biotype <list>,
## # feature_reference <list>, 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: 38 × 4
## facet label ontology_term_id n
## <chr> <chr> <chr> <int>
## 1 assay 10x 3' v3 EFO:0009922 597
## 2 assay 10x 3' v2 EFO:0009899 271
## 3 assay Slide-seqV2 EFO:0030062 223
## 4 assay Visium Spatial Gene Expression EFO:0010961 131
## 5 assay 10x 5' v1 EFO:0011025 90
## 6 assay Smart-seq2 EFO:0008931 63
## 7 assay 10x multiome EFO:0030059 62
## 8 assay sci-RNA-seq EFO:0010550 33
## 9 assay 10x 5' v2 EFO:0009900 24
## 10 assay 10x 5' transcription profiling EFO:0030004 21
## # ℹ 28 more rowsfacets(db, "self_reported_ethnicity")## # A tibble: 33 × 4
## facet label ontology_term_id n
## <chr> <chr> <chr> <int>
## 1 self_reported_ethnicity European HANCESTRO:0005 544
## 2 self_reported_ethnicity unknown unknown 434
## 3 self_reported_ethnicity na na 366
## 4 self_reported_ethnicity Asian HANCESTRO:0008 143
## 5 self_reported_ethnicity Hispanic or Latin American HANCESTRO:0014 63
## 6 self_reported_ethnicity African American HANCESTRO:0568 62
## 7 self_reported_ethnicity Native American,Hispanic or L… HANCESTRO:0013,… 50
## 8 self_reported_ethnicity African American or Afro-Cari… HANCESTRO:0016 32
## 9 self_reported_ethnicity Greater Middle Eastern (Midd… HANCESTRO:0015 22
## 10 self_reported_ethnicity African HANCESTRO:0010 19
## # ℹ 23 more rowsfacets(db, "sex")## # A tibble: 3 × 4
## facet label ontology_term_id n
## <chr> <chr> <chr> <int>
## 1 sex male PATO:0000384 986
## 2 sex female PATO:0000383 762
## 3 sex unknown unknown 180Suppose 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 4419584cells 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: 14 × 18
## collection_id collection_version_id collection_url consortia contact_email
## <chr> <chr> <chr> <list> <chr>
## 1 62e8f058-9c37-4… 645d7ef5-f8ab-4b40-8… https://cellx… <chr [1]> chanj3@mskcc…
## 2 4195ab4c-20bd-4… 4bb38c2f-9c6a-4895-b… https://cellx… <chr [1]> nnavin@mdand…
## 3 b9fc3d70-5a72-4… a73587fc-5019-46e2-a… https://cellx… <chr [1]> bruce.aronow…
## 4 f17b9205-f61f-4… 87279d9b-8ab2-4e10-9… https://cellx… <chr [1]> genevieve.ko…
## 5 cec4ef8e-1e70-4… 7b72315c-9210-459a-b… https://cellx… <lgl [1]> Ryan.Logan@u…
## 6 bcb61471-2a44-4… 447d101d-5306-4321-a… https://cellx… <chr [3]> info@kpmp.org
## 7 c9706a92-0e5f-4… 04464954-ab11-4a56-b… https://cellx… <chr [1]> hnakshat@iup…
## 8 72d37bc9-76cc-4… e95dbb27-c619-4f2d-9… https://cellx… <chr [1]> m.sepp@zmbh.…
## 9 625f6bf4-2f33-4… fb5e1588-a7ee-41d6-a… https://cellx… <chr [1]> a5wang@healt…
## 10 e1fa9900-3fc9-4… 93bc2a17-6fb0-4329-9… https://cellx… <lgl [1]> j.ma@yale.edu
## 11 a98b828a-622a-4… 93db548a-c8de-4581-a… https://cellx… <chr [1]> markusbi@med…
## 12 6b701826-37bb-4… d63af6c4-1427-4237-a… https://cellx… <chr [1]> astreets@ber…
## 13 b953c942-f5d8-4… 4ac10bd3-3a86-4356-b… https://cellx… <lgl [1]> icobos@stanf…
## 14 71f4bccf-53d4-4… 7a0d0734-79cc-48fd-9… https://cellx… <chr [1]> kevinmbyrd@g…
## # 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> "04464954-ab11-4a56-b136-e924b750f1bd"
## $ 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> 2024-03-19
## $ published_at <date> 2021-03-25
## $ revised_at <date> 2024-03-22Use 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: 770 × 4
## collection_id link_name link_type link_url
## <chr> <chr> <chr> <chr>
## 1 59c9ecfe-c47d-4a6a-bab0-895cc0c1942b GSE159812 RAW_DATA https://www…
## 2 59c9ecfe-c47d-4a6a-bab0-895cc0c1942b <NA> DATA_SOURCE https://twc…
## 3 59c9ecfe-c47d-4a6a-bab0-895cc0c1942b <NA> LAB_WEBSITE https://www…
## 4 59c9ecfe-c47d-4a6a-bab0-895cc0c1942b <NA> LAB_WEBSITE https://web…
## 5 59c9ecfe-c47d-4a6a-bab0-895cc0c1942b <NA> RAW_DATA https://dat…
## 6 a18474f4-ff1e-4864-af69-270b956cee5b EGAD00001010074 RAW_DATA https://ega…
## 7 a18474f4-ff1e-4864-af69-270b956cee5b <NA> OTHER https://git…
## 8 f5af7a2f-ab4c-4728-829e-48efb9562105 GSE222250 RAW_DATA https://www…
## 9 aee9c366-f2fb-470b-8937-577d5d87d3fc Synapse RAW_DATA https://www…
## 10 e5f58829-1a66-40b5-a624-9046778e74f5 <NA> RAW_DATA https://reg…
## # ℹ 760 more rowsexternal_links |>
count(link_type)## # A tibble: 5 × 2
## link_type n
## <chr> <int>
## 1 DATA_SOURCE 35
## 2 LAB_WEBSITE 41
## 3 OTHER 349
## 4 PROTOCOL 45
## 5 RAW_DATA 300external_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> "1265f8ec-9918-499a-8b7d-ede70c8d77c0"
## $ 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> 2024-03-19
## $ published_at <date> 2022-10-04
## $ revised_at <date> 2024-03-22cellxgeneVisualization 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] "dba8eab1-fb75-4a40-be68-2bcd8094ac52.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: 33145 31696
## metadata(5): citation default_embedding schema_reference schema_version
## title
## assays(1): X
## rownames(33145): ENSG00000243485 ENSG00000237613 ... ENSG00000277475
## ENSG00000268674
## rowData names(5): feature_is_filtered feature_name feature_reference
## feature_biotype feature_length
## colnames(31696): CMGpool_AAACCCAAGGACAACC CMGpool_AAACCCACAATCTCTT ...
## K109064_TTTGTTGGTTGCATCA K109064_TTTGTTGGTTGGACCC
## colData names(36): donor_id self_reported_ethnicity_ontology_term_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 4.3.3 (2024-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.18-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_1.6.2 dplyr_1.1.4
## [3] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
## [5] Biobase_2.62.0 GenomicRanges_1.54.1
## [7] GenomeInfoDb_1.38.8 IRanges_2.36.0
## [9] S4Vectors_0.40.2 BiocGenerics_0.48.1
## [11] MatrixGenerics_1.14.0 matrixStats_1.2.0
## [13] zellkonverter_1.12.1 BiocStyle_2.30.0
##
## loaded via a namespace (and not attached):
## [1] dir.expiry_1.10.0 xfun_0.43 bslib_0.7.0
## [4] htmlwidgets_1.6.4 rhdf5_2.46.1 lattice_0.22-6
## [7] rhdf5filters_1.14.1 rjsoncons_1.2.0 vctrs_0.6.5
## [10] tools_4.3.3 bitops_1.0-7 generics_0.1.3
## [13] curl_5.2.1 parallel_4.3.3 tibble_3.2.1
## [16] fansi_1.0.6 pkgconfig_2.0.3 Matrix_1.6-5
## [19] lifecycle_1.0.4 GenomeInfoDbData_1.2.11 compiler_4.3.3
## [22] httpuv_1.6.15 htmltools_0.5.8 sass_0.4.9
## [25] RCurl_1.98-1.14 yaml_2.3.8 later_1.3.2
## [28] pillar_1.9.0 crayon_1.5.2 jquerylib_0.1.4
## [31] DT_0.32 DelayedArray_0.28.0 cachem_1.0.8
## [34] abind_1.4-5 mime_0.12 basilisk_1.14.3
## [37] tidyselect_1.2.1 digest_0.6.35 bookdown_0.38
## [40] fastmap_1.1.1 grid_4.3.3 cli_3.6.2
## [43] SparseArray_1.2.4 magrittr_2.0.3 S4Arrays_1.2.1
## [46] utf8_1.2.4 withr_3.0.0 promises_1.2.1
## [49] filelock_1.0.3 httr_1.4.7 rmarkdown_2.26
## [52] XVector_0.42.0 reticulate_1.35.0 png_0.1-8
## [55] HDF5Array_1.30.1 shiny_1.8.1 evaluate_0.23
## [58] knitr_1.45 basilisk.utils_1.14.1 rlang_1.1.3
## [61] Rcpp_1.0.12 xtable_1.8-4 glue_1.7.0
## [64] BiocManager_1.30.22 jsonlite_1.8.8 Rhdf5lib_1.24.2
## [67] R6_2.5.1 zlibbioc_1.48.2