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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | b3568c0 | Dave Tang | 2024-04-08 | CELLxGENE Census |
Following the documentation for the R package {cellxgene.census} which is part of CZ CELLxGENE Discover Census.
{cellxgene.census} provides an API to efficiently access the cloud-hosted Census single-cell data from R. In just a few seconds users can access any slice of Census data using cell or gene filters across hundreds of single-cell datasets.
Census data can be fetched in an iterative fashion for bigger-than-memory slices of data, or quickly exported to basic R structures, as well as {Seurat} or {SingleCellExperiment} objects for downstream analysis.
Install from the R-universe platform. If installing using Ubuntu/Debian, you may need to install the following libraries via APT:
In addition you must have at least cmake v3.21. Install
the {tiledbsoma} dependency first, since this takes some time to compile
and install, it’s better to fail first.
install.packages('tiledbsoma', repos = c('https://tiledb-inc.r-universe.dev',
'https://cloud.r-project.org'))Now install {cellxgene.census}, which should be a breeze now.
install.packages(
"cellxgene.census",
repos=c('https://chanzuckerberg.r-universe.dev', 'https://cloud.r-project.org')
)Now install {Seurat}.
install.packages("Seurat")Querying and fetching the single-cell data and cell/gene metadata
library("cellxgene.census")
packageVersion("cellxgene.census")[1] '1.13.0'The human gene metadata of the Census, for RNA assays, is located at
census$get("census_data")$get("homo_sapiens")$obs. The
mouse cell metadata is at
census$get("census_data")$get("mus_musculus").obs.
To learn what metadata columns are available for fetching and filtering we can directly look at the keys of the cell metadata.
census <- open_soma()The stable Census release is currently 2023-12-15. Specify census_version = "2023-12-15" in future calls to open_soma() to ensure data consistency.my_keys <- census$get("census_data")$get("homo_sapiens")$obs$colnames()
my_keys [1] "soma_joinid"
[2] "dataset_id"
[3] "assay"
[4] "assay_ontology_term_id"
[5] "cell_type"
[6] "cell_type_ontology_term_id"
[7] "development_stage"
[8] "development_stage_ontology_term_id"
[9] "disease"
[10] "disease_ontology_term_id"
[11] "donor_id"
[12] "is_primary_data"
[13] "self_reported_ethnicity"
[14] "self_reported_ethnicity_ontology_term_id"
[15] "sex"
[16] "sex_ontology_term_id"
[17] "suspension_type"
[18] "tissue"
[19] "tissue_ontology_term_id"
[20] "tissue_general"
[21] "tissue_general_ontology_term_id"
[22] "raw_sum"
[23] "nnz"
[24] "raw_mean_nnz"
[25] "raw_variance_nnz"
[26] "n_measured_vars" soma_joinid is a special SOMADataFrame
column that is used for join operations. All of the keys can be used to
fetch specific columns or specific rows matching a condition. For the
latter we need to know the values we are looking for a
priori.
For example let’s see what are the possible values available for
sex. To this we can load all cell metadata but fetching
only for the column sex; column_names are character vector
indicating what metadata columns to fetch.
census$get("census_data")$get("homo_sapiens")$obs$read(column_names = "sex")$concat() |>
as.data.frame() |>
unique() sex
1 male
224 female
3747640 unknownWith this information we can fetch all cell metadata for a specific
sex value, for example “unknown”; the value_filter is an R
expression with selection conditions to fetch rows.
census$get("census_data")$get("homo_sapiens")$obs$read(value_filter = "sex == 'unknown'")$concat() |>
as.data.frame() -> sex_unknown
head(sex_unknown) soma_joinid dataset_id assay
1 3747639 9fcb0b73-c734-40a5-be9c-ace7eea401c9 10x 3' v2
2 3747640 9fcb0b73-c734-40a5-be9c-ace7eea401c9 10x 3' v2
3 3747641 9fcb0b73-c734-40a5-be9c-ace7eea401c9 10x 3' v2
4 3747642 9fcb0b73-c734-40a5-be9c-ace7eea401c9 10x 3' v2
5 3747643 9fcb0b73-c734-40a5-be9c-ace7eea401c9 10x 3' v2
6 3747644 9fcb0b73-c734-40a5-be9c-ace7eea401c9 10x 3' v2
assay_ontology_term_id cell_type cell_type_ontology_term_id
1 EFO:0009899 fibroblast CL:0000057
2 EFO:0009899 fibroblast CL:0000057
3 EFO:0009899 fibroblast CL:0000057
4 EFO:0009899 fibroblast CL:0000057
5 EFO:0009899 fibroblast CL:0000057
6 EFO:0009899 fibroblast CL:0000057
development_stage development_stage_ontology_term_id disease
1 human adult stage HsapDv:0000087 normal
2 human adult stage HsapDv:0000087 normal
3 human adult stage HsapDv:0000087 normal
4 human adult stage HsapDv:0000087 normal
5 human adult stage HsapDv:0000087 normal
6 human adult stage HsapDv:0000087 normal
disease_ontology_term_id donor_id is_primary_data
1 PATO:0000461 Pagella_GSE161267_GSM4904134 TRUE
2 PATO:0000461 Pagella_GSE161267_GSM4904134 TRUE
3 PATO:0000461 Pagella_GSE161267_GSM4904134 TRUE
4 PATO:0000461 Pagella_GSE161267_GSM4904134 TRUE
5 PATO:0000461 Pagella_GSE161267_GSM4904134 TRUE
6 PATO:0000461 Pagella_GSE161267_GSM4904134 TRUE
self_reported_ethnicity self_reported_ethnicity_ontology_term_id sex
1 unknown unknown unknown
2 unknown unknown unknown
3 unknown unknown unknown
4 unknown unknown unknown
5 unknown unknown unknown
6 unknown unknown unknown
sex_ontology_term_id suspension_type tissue tissue_ontology_term_id
1 unknown cell gingiva UBERON:0001828
2 unknown cell gingiva UBERON:0001828
3 unknown cell gingiva UBERON:0001828
4 unknown cell gingiva UBERON:0001828
5 unknown cell gingiva UBERON:0001828
6 unknown cell gingiva UBERON:0001828
tissue_general tissue_general_ontology_term_id raw_sum nnz raw_mean_nnz
1 mucosa UBERON:0000344 547 329 1.662614
2 mucosa UBERON:0000344 982 563 1.744227
3 mucosa UBERON:0000344 12467 3809 3.273038
4 mucosa UBERON:0000344 1053 566 1.860424
5 mucosa UBERON:0000344 548 363 1.509642
6 mucosa UBERON:0000344 678 429 1.580420
raw_variance_nnz n_measured_vars
1 14.559604 31602
2 5.315247 31602
3 109.305683 31602
4 7.430042 31602
5 2.410818 31602
6 11.379616 31602We can use both column_names and
value_filter to perform specific queries. For example
fetching the disease column for the cell_type “B cell” in the
tissue_general “lung”.
cell_metadata_b_cell <- census$get("census_data")$get("homo_sapiens")$obs$read(
value_filter = "cell_type == 'B cell' & tissue_general == 'lung'",
column_names = "disease"
)
cell_metadata_b_cell <- as.data.frame(cell_metadata_b_cell$concat())
table(cell_metadata_b_cell)disease
chronic obstructive pulmonary disease COVID-19
6369 2729
hypersensitivity pneumonitis interstitial lung disease
52 376
lung adenocarcinoma lung large cell carcinoma
62351 1534
lymphangioleiomyomatosis non-small cell lung carcinoma
133 17484
non-specific interstitial pneumonia normal
231 25461
pleomorphic carcinoma pneumonia
1210 50
pulmonary emphysema pulmonary fibrosis
1512 6798
pulmonary sarcoidosis small cell lung carcinoma
6 583
squamous cell lung carcinoma
11920 Use get_seurat() to perform the same type of filtering
but returning a Seurat object.
obs_column_names — character vector indicating the
columns to select for cell metadata.obs_value_filter — expression with selection conditions
to fetch cells meeting a criteria.var_column_names — character vector indicating the
columns to select for gene metadata.var_value_filter — expression with selection conditions
to fetch genes meeting a criteria.library("Seurat")
seurat_obj <- get_seurat(
census, "Homo sapiens",
obs_column_names = c("cell_type", "tissue_general", "disease", "sex"),
var_value_filter = "feature_id %in% c('ENSG00000161798', 'ENSG00000188229')",
obs_value_filter = "cell_type == 'B cell' & tissue_general == 'lung' & disease == 'COVID-19'"
)
saveRDS(object = seurat_obj, file = "data/lung_bcell.rds")Seurat object.
seurat_obj <- readRDS(file = "data/lung_bcell.rds")
seurat_objLoading required package: SeuratObjectLoading required package: sp'SeuratObject' was built under R 4.3.0 but the current version is
4.3.3; it is recomended that you reinstall 'SeuratObject' as the ABI
for R may have changed
Attaching package: 'SeuratObject'The following object is masked from 'package:base':
intersectAn object of class Seurat
2 features across 2729 samples within 1 assay
Active assay: RNA (2 features, 0 variable features)
2 layers present: counts, dataAfter use, the census object should be closed to release memory and
other resources. This also closes all SOMA objects accessed via the
top-level census. Closing can be automated using
on.exit(census$close(), add = TRUE) immediately after
census <- open_soma().
census$close()
sessionInfo()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: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] SeuratObject_5.0.1 sp_2.1-3 RcppSpdlog_0.0.16
[4] cellxgene.census_1.13.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] dotCall64_1.1-1 spam_2.10-0 xfun_0.43
[4] bslib_0.7.0 processx_3.8.4 lattice_0.22-5
[7] callr_3.7.6 vctrs_0.6.5 tools_4.3.3
[10] ps_1.7.6 generics_0.1.3 parallel_4.3.3
[13] curl_5.2.1 tibble_3.2.1 fansi_1.0.6
[16] pkgconfig_2.0.3 Matrix_1.6-5 data.table_1.15.4
[19] assertthat_0.2.1 lifecycle_1.0.4 compiler_4.3.3
[22] stringr_1.5.1 git2r_0.33.0 codetools_0.2-19
[25] getPass_0.2-4 tiledbsoma_1.9.4 httpuv_1.6.15
[28] htmltools_0.5.8.1 nanoarrow_0.4.0.1 sass_0.4.9
[31] yaml_2.3.8 later_1.3.2 pillar_1.9.0
[34] jquerylib_0.1.4 whisker_0.4.1 aws.s3_0.3.21
[37] cachem_1.0.8 RcppCCTZ_0.2.12 tiledb_0.25.0
[40] parallelly_1.37.1 tidyselect_1.2.1 digest_0.6.35
[43] future_1.33.2 stringi_1.8.3 listenv_0.9.1
[46] dplyr_1.1.4 purrr_1.0.2 arrow_15.0.1
[49] rprojroot_2.0.4 fastmap_1.1.1 grid_4.3.3
[52] cli_3.6.2 magrittr_2.0.3 base64enc_0.1-3
[55] triebeard_0.4.1 spdl_0.0.5 utf8_1.2.4
[58] future.apply_1.11.2 aws.signature_0.6.0 promises_1.3.0
[61] bit64_4.0.5 nanotime_0.3.7 rmarkdown_2.26
[64] httr_1.4.7 globals_0.16.3 bit_4.0.5
[67] progressr_0.14.0 zoo_1.8-12 evaluate_0.23
[70] knitr_1.46 rlang_1.1.3 urltools_1.7.3
[73] Rcpp_1.0.12 glue_1.7.0 xml2_1.3.6
[76] rstudioapi_0.16.0 jsonlite_1.8.8 R6_2.5.1
[79] fs_1.6.3