Explore Human BioMolecular Atlas Program Data Portal
23 January 2025
Package
HuBMAPR 1.1.7
Contents
1 Overview
‘HuBMAP’ data portal (https://portal.hubmapconsortium.org/) provides an open,
global bio-molecular atlas of the human body at the cellular level. HuBMAPR
package provides an alternative interface to explore the data via R.
The HuBMAP Consortium offers several
APIs.
To achieve the main objectives, HuBMAPR package specifically integrates three
APIs:
Search API: The Search API is primarily searching relevant data information and is referenced to the Elasticsearch API.
Entity API: The Entity API is specifically utilized in the
bulk_data_transfer()function for Globus URL retrievalOntology API: The Ontology API is applied in the
organ()function to provide additional information about the abbreviation and corresponding full name of each organ.
Each API serves a distinct purpose with unique query capabilities, tailored to
meet various needs. Utilizing the httr2 and rjsoncons packages, HuBMAPR
effectively manages, modifies, and executes multiple requests via these APIs,
presenting responses in formats such as tibble or character. These outputs are
further modified for clarity in the final results from the HuBMAPR functions,
and these functions help reflect the data information of HuBMAP Data Portal as
much as possible.
Using temporary storage to cache API responses
facilitates efficient data retrieval by reducing the need for
redundant requests to the HuBMAP Data Portal. This
approach minimizes server load, improves response times (e.g.
datasets() takes less than 4 seconds to retrieve more than
3500 records’ information, shown below),
and enhances overall query efficiency. By
periodically clearing cached data or directing them to a
temporary directory, the process ensures that the retrieved
information remains relevant while managing storage effectively.
This caching mechanism supports a smoother and more efficient
user experience when accessing data from the portal.
HuBMAP Data incorporates three different identifiers:
HuBMAP ID, e.g. HBM399.VCTL.353
Universally Unique Identifier (UUID), e.g. 7036a70229eff1a51af965454dddbe7d
Digital Object Identifiers (DOI), e.g. 10.35079/HBM399.VCTL.353.
The HuBMAPR
package utilizes the UUID - a 32-digit hexadecimal number - and the more
human-readable HuBMAP ID as two common identifiers in the retrieved results.
Considering precision and compatibility with software implementation and data
storage, UUID serves as the primary identifier to retrieve data across various
functions, with the UUID mapping uniquely to its corresponding HuBMAP ID.
The systematic nomenclature is adopted for functions in the package by appending the entity category prefix to the concise description of the specific functionality. Most of the functions are grouped by entity categories, thereby simplifying the process of selecting the appropriate functions to retrieve the desired information associated with the given UUID from the specific entity category. The structure of these functions is heavily consistent across all entity categories with some exceptions for collection and publication.
2 Installation
HuBMAPR is a R package. The package can be installed by
if (!requireNamespace("BiocManager")) {
install.packages("BiocManager")
}
BiocManager::install("HuBMAPR")Install development version from GitHub:
remotes::install_github("christinehou11/HuBMAPR")3 Basic User Guide
3.1 Implementation Notes
This session is to provide a guidance on extending or customizing the HuBMAPR
package to accommodate potential future changes in data structure, enhancing
the package’s long-term utility. We included a brief outline to illustrate the
basics of the principles and approach to package design.
Identify an API end point
Provide an R client to translate R data structures to the arguments and parameters required by the API
Handle the response in a consistent way with respect to argument and response validation
Format the return value as a ‘tibble’ or ‘character’ to minimize cognitive demands on the user for interpreting the result, and to facilitate incorporation into general R workflows
3.2 Load Necessary Packages
Load additional packages. dplyr package is widely used in this vignettes to
conduct data wrangling and specific information extraction.
library("dplyr")
library("tidyr")
library("ggplot2")
library("HuBMAPR")
library("pryr")3.3 Data Discovery
HuBMAP data portal page displays chronologically (last modified date time)
five categories of entity data:
Dataset
Sample
Donor
Publication
Collection.
Using corresponding functions to explore entity data.
system.time({
datasets_df <- datasets()
})
#> user system elapsed
#> 1.411 0.100 4.203
object_size(datasets_df)
#> 1.05 MB
datasets_df
#> # A tibble: 3,711 × 14
#> uuid hubmap_id dataset_type dataset_type_additio…¹ organ analyte_class
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 d4fc9da8a2… HBM639.D… ATACseq "sciATACseq" Heart "DNA"
#> 2 6cd2abf4cb… HBM577.K… ATACseq "" Heart ""
#> 3 acaf75b829… HBM947.R… ATACseq "sciATACseq" Heart "DNA"
#> 4 97d807e4d5… HBM948.F… ATACseq "" Heart ""
#> 5 76bfd5a517… HBM254.J… ATACseq "sciATACseq" Heart "DNA"
#> 6 bb53dbcfc6… HBM333.G… ATACseq "" Heart ""
#> 7 dadef64c3b… HBM625.S… ATACseq "" Heart ""
#> 8 e74ba9723d… HBM569.M… ATACseq "" Heart ""
#> 9 2d4d2f368c… HBM836.L… ATACseq "sciATACseq" Heart "DNA"
#> 10 0bf827ea01… HBM953.R… ATACseq "sciATACseq" Heart "DNA"
#> # ℹ 3,701 more rows
#> # ℹ abbreviated name: ¹dataset_type_additional_information
#> # ℹ 8 more variables: sample_category <chr>, status <chr>,
#> # dataset_processing_category <chr>, pipeline <chr>, registered_by <chr>,
#> # donor_hubmap_id <chr>, group_name <chr>, last_modified_timestamp <chr>samples(), donors(), collections(), and publications() work same
as above.
The default tibble produced by corresponding entity function only reflects
selected information. To see the names of selected information, use following
commands for each entity category. Specify as parameter to display
information in the format of "character" or "tibble".
# as = "tibble" (default)
datasets_col_tbl <- datasets_default_columns(as = "tibble")
datasets_col_tbl
#> # A tibble: 14 × 1
#> columns
#> <chr>
#> 1 uuid
#> 2 hubmap_id
#> 3 group_name
#> 4 dataset_type_additional_information
#> 5 dataset_type
#> 6 organ
#> 7 analyte_class
#> 8 dataset_processing_category
#> 9 sample_category
#> 10 registered_by
#> 11 status
#> 12 pipeline
#> 13 last_modified_timestamp
#> 14 donor_hubmap_id
# as = "character"
datasets_col_char <- datasets_default_columns(as = "character")
datasets_col_char
#> [1] "uuid" "hubmap_id"
#> [3] "group_name" "dataset_type_additional_information"
#> [5] "dataset_type" "organ"
#> [7] "analyte_class" "dataset_processing_category"
#> [9] "sample_category" "registered_by"
#> [11] "status" "pipeline"
#> [13] "last_modified_timestamp" "donor_hubmap_id"samples_default_columns(), donors_default_columns(),
collections_default_columns(), and publications_default_columns() work same
as above.
A brief overview of selected information for five entity categories is:
tbl <- bind_cols(
dataset = datasets_default_columns(as = "character"),
sample = c(samples_default_columns(as = "character"), rep(NA, 7)),
donor = c(donors_default_columns(as = "character"), rep(NA, 6)),
collection = c(collections_default_columns(as = "character"),
rep(NA, 10)),
publication = c(publications_default_columns(as = "character"),
rep(NA, 7))
)
tbl
#> # A tibble: 14 × 5
#> dataset sample donor collection publication
#> <chr> <chr> <chr> <chr> <chr>
#> 1 uuid uuid hubm… uuid uuid
#> 2 hubmap_id hubmap_id uuid hubmap_id hubmap_id
#> 3 group_name group_name grou… title title
#> 4 dataset_type_additional_information sample_cate… Sex last_modi… publicatio…
#> 5 dataset_type organ Age <NA> last_modif…
#> 6 organ last_modifi… Body… <NA> publicatio…
#> 7 analyte_class donor_hubma… Race <NA> publicatio…
#> 8 dataset_processing_category <NA> last… <NA> <NA>
#> 9 sample_category <NA> <NA> <NA> <NA>
#> 10 registered_by <NA> <NA> <NA> <NA>
#> 11 status <NA> <NA> <NA> <NA>
#> 12 pipeline <NA> <NA> <NA> <NA>
#> 13 last_modified_timestamp <NA> <NA> <NA> <NA>
#> 14 donor_hubmap_id <NA> <NA> <NA> <NA>Use organ() to read through the available organs included in HuBMAP. It can
be helpful to filter retrieved data based on organ information.
organs <- organ()
organs
#> # A tibble: 43 × 2
#> abbreviation name
#> <chr> <chr>
#> 1 BD Blood
#> 2 BL Bladder
#> 3 BM Bone Marrow
#> 4 BR Brain
#> 5 BV Blood Vasculature
#> 6 HT Heart
#> 7 LA Larynx
#> 8 LB Bronchus (Left)
#> 9 LE Eye (Left)
#> 10 LF Fallopian Tube (Left)
#> # ℹ 33 more rows3.3.1 Data Wrangling Examples
Data wrangling and filter are welcome to retrieve data based on interested information.
# Example from datasets()
datasets_df |>
filter(organ == 'Small Intestine') |>
count()
#> # A tibble: 1 × 1
#> n
#> <int>
#> 1 424Any dataset, sample, donor, collection, and publication has special HuBMAP ID and UUID, and UUID is the main ID to be used in most of functions for specific detail retrievals.
The column of donor_hubmap_id is included in the retrieved tibbles from
samples() and datasets(), which can help to join the tibble.
donors_df <- donors()
donor_sub <- donors_df |>
filter(Sex == "Female",
Age <= 76 & Age >= 55,
Race == "White",
`Body Mass Index` <= 25,
last_modified_timestamp >= "2020-01-08" &
last_modified_timestamp <= "2020-06-30") |>
head(1)
# Datasets
donor_sub_dataset <- donor_sub |>
left_join(datasets_df |>
select(-c(group_name, last_modified_timestamp)) |>
rename("dataset_uuid" = "uuid",
"dataset_hubmap_id" = "hubmap_id"),
by = c("hubmap_id" = "donor_hubmap_id"))
donor_sub_dataset
#> # A tibble: 0 × 19
#> # ℹ 19 variables: uuid <chr>, hubmap_id <chr>, group_name <chr>, Sex <chr>,
#> # Age <dbl>, Body Mass Index <dbl>, Race <chr>,
#> # last_modified_timestamp <chr>, dataset_uuid <chr>, dataset_hubmap_id <chr>,
#> # dataset_type <chr>, dataset_type_additional_information <chr>, organ <chr>,
#> # analyte_class <chr>, sample_category <chr>, status <chr>,
#> # dataset_processing_category <chr>, pipeline <chr>, registered_by <chr>
# Samples
samples_df <- samples()
donor_sub_sample <- donor_sub |>
left_join(samples_df |>
select(-c(group_name, last_modified_timestamp)) |>
rename("sample_uuid" = "uuid",
"sample_hubmap_id" = "hubmap_id"),
by = c("hubmap_id" = "donor_hubmap_id"))
donor_sub_sample
#> # A tibble: 0 × 12
#> # ℹ 12 variables: uuid <chr>, hubmap_id <chr>, group_name <chr>, Sex <chr>,
#> # Age <dbl>, Body Mass Index <dbl>, Race <chr>,
#> # last_modified_timestamp <chr>, sample_uuid <chr>, sample_hubmap_id <chr>,
#> # sample_category <chr>, organ <chr>You can use *_detail(uuid) to retrieve all available information for any
entry of any entity category given UUID. Use select() and unnest_*()
functions to expand list-columns. It will be convenient to view tables with
multiple columns but one row using glimpse().
dataset_uuid <- datasets_df |>
filter(dataset_type == "Auto-fluorescence",
organ == "Kidney (Right)") |>
head(1) |>
pull(uuid)
# Full Information
dataset_detail(dataset_uuid) |> glimpse()
#> Rows: 1
#> Columns: 35
#> $ ancestor_ids <list> <"de1076a42144debd94ba4f5d1f9b6d57",…
#> $ ancestors <list> [["de1076a42144debd94ba4f5d1f9b6d57"…
#> $ contacts <list> [["Biomolecular Multimodal Imaging C…
#> $ contains_human_genetic_sequences <lgl> FALSE
#> $ contributors <list> [["Biomolecular Multimodal Imaging Ce…
#> $ created_by_user_displayname <chr> "HuBMAP Process"
#> $ created_by_user_email <chr> "hubmap@hubmapconsortium.org"
#> $ created_timestamp <dbl> 1.711126e+12
#> $ creation_action <chr> "Create Dataset Activity"
#> $ data_access_level <chr> "public"
#> $ dataset_type <chr> "Auto-fluorescence"
#> $ descendant_ids <list> "61530ed23518ee5f6cdaa818e91dde65"
#> $ descendants <list> [["Auto-fluorescence [Image Pyramid]…
#> $ description <chr> "Autofluorescence Microscopy collecte…
#> $ display_subtype <chr> "Auto-fluorescence"
#> $ doi_url <chr> "https://doi.org/10.35079/HBM223.DJQM…
#> $ donor <list> ["Jamie Allen", "jamie.l.allen@vander…
#> $ entity_type <chr> "Dataset"
#> $ group_name <chr> "Vanderbilt TMC"
#> $ group_uuid <chr> "73bb26e4-ed43-11e8-8f19-0a7c1eab007a"
#> $ hubmap_id <chr> "HBM223.DJQM.264"
#> $ immediate_ancestor_ids <list> "de1076a42144debd94ba4f5d1f9b6d57"
#> $ immediate_descendant_ids <list> "61530ed23518ee5f6cdaa818e91dde65"
#> $ index_version <chr> "3.6.1"
#> $ ingest_metadata <list> [[["a9099c6", "https://github.com/hub…
#> $ last_modified_timestamp <dbl> 1.71691e+12
#> $ metadata <list> ["Axio Scan.Z1", "Zeiss Microscopy", …
#> $ origin_samples <list> [["Jamie Allen", "jamie.l.allen@vande…
#> $ provider_info <chr> "VAN0042-RK-3 block AF : ./VAN0042-R…
#> $ published_timestamp <dbl> 1.715267e+12
#> $ registered_doi <chr> "10.35079/HBM223.DJQM.264"
#> $ source_samples <list> [["Jamie Allen", "jamie.l.allen@vand…
#> $ status <chr> "Published"
#> $ title <chr> "Auto-fluorescence data from the kidn…
#> $ uuid <chr> "993bb1d6fa02e2755fd69613bb9d6e08"
# Specific Information
dataset_detail(uuid = dataset_uuid) |>
select(contributors) |>
unnest_longer(contributors) |>
unnest_wider(everything())
#> # A tibble: 16 × 11
#> affiliation display_name email first_name is_contact is_operator
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Biomolecular Multimodal… Jamie L. Al… jami… Jamie No Yes
#> 2 Delft Center for System… Lukasz Migas l.g.… Lukasz No Yes
#> 3 Biomolecular Multimodal… Nathan Heat… nath… Nathan No Yes
#> 4 Biomolecular Multimodal… Jeffrey M. … jeff… Jeffrey Yes No
#> 5 Delft Center for System… Leonor Tide… l.e.… Leonoor No Yes
#> 6 Delft Center for System… Raf Van de … Raf.… Raf No No
#> 7 Biomolecular Multimodal… Melissa A. … meli… Melissa No Yes
#> 8 Biomolecular Multimodal… Madeline E.… made… Madeline No Yes
#> 9 Biomolecular Multimodal… Ellie L. Pi… elli… Ellie No Yes
#> 10 Delft Center for System… Felipe Moser f.a.… Felipe No Yes
#> 11 Division of Nephrology … Mark deCaes… mark… Mark No Yes
#> 12 Division of Nephrology … Agnes B. Fo… agne… Agnes No Yes
#> 13 Division of Nephrology … Haichun Yang haic… Haichun No Yes
#> 14 Biomolecular Multimodal… Tina Tsui tina… Tina No Yes
#> 15 Biomolecular Multimodal… Katerina V.… kate… Katerina No Yes
#> 16 Biomolecular Multimodal… Allison B. … alli… Allison No Yes
#> # ℹ 5 more variables: is_principal_investigator <chr>, last_name <chr>,
#> # metadata_schema_id <chr>, middle_name_or_initial <chr>, orcid <chr>sample_detail(), donor_detail(), collection_detail(), and
publication_detail() work same as above.
3.4 Metadata
To retrieve the metadata for Dataset, Sample, and Donor metadata,
use dataset_metadata(), sample_metadata(), and donor_metadata().
dataset_metadata("993bb1d6fa02e2755fd69613bb9d6e08")
#> New names:
#> • `` -> `...1`
#> # A tibble: 22 × 2
#> Key Value
#> <chr> <chr>
#> 1 acquisition_instrument_model "Axio Scan.Z1"
#> 2 acquisition_instrument_vendor "Zeiss Microscopy"
#> 3 analyte_class "Endogenous fluorophore"
#> 4 antibodies_path "extras/antibodies.tsv"
#> 5 contributors_path "extras/contributors.tsv"
#> 6 data_path "."
#> 7 dataset_type "Auto-fluorescence"
#> 8 intended_tile_overlap_percentage ""
#> 9 is_image_preprocessing_required "no"
#> 10 is_targeted "No"
#> # ℹ 12 more rows
sample_metadata("8ecdbdc3e2d04898e2563d666658b6a9")
#> # A tibble: 5 × 2
#> Key Value
#> <chr> <chr>
#> 1 donor.Age "71.0 years"
#> 2 donor.Apolipoprotein E phenotype "Apolipoprotein E phenotype "
#> 3 donor.Pathology note "Pathology note "
#> 4 donor.Race "White "
#> 5 donor.Sex "Male "
donor_metadata("b2c75c96558c18c9e13ba31629f541b6")
#> # A tibble: 8 × 2
#> Key Value
#> <chr> <chr>
#> 1 Age "41.0 years"
#> 2 Body Mass Index "37.10 kg/m2"
#> 3 Cause of Death "Cerebrovascular accident "
#> 4 Death Event "Natural causes "
#> 5 Mechanism of Injury "Intracranial hemorrhage "
#> 6 Race "White "
#> 7 Sex "Female "
#> 8 Social History "Smoker "3.5 Derived Data
Some datasets from Dataset entity has derived (support) dataset(s). Use
dataset_derived() to retrieve. A tibble with selected details will be
retrieved as if the given dataset has support dataset; otherwise, nothing
returns.
# no derived/support dataset
dataset_uuid_1 <- "3acdb3ed962b2087fbe325514b098101"
dataset_derived(uuid = dataset_uuid_1)
#> NULL
# has derived/support dataset
dataset_uuid_2 <- "baf976734dd652208d13134bc5c4594b"
dataset_derived(uuid = dataset_uuid_2) |> glimpse()
#> Rows: 1
#> Columns: 6
#> $ uuid <chr> "bbbf5a5b29986dd57910daab00193f35"
#> $ hubmap_id <chr> ""
#> $ data_types <chr> ""
#> $ dataset_type <chr> "Histology [Image Pyramid]"
#> $ status <chr> ""
#> $ last_modified_timestamp <chr> "NA"Sample and Donor have derived samples and datasets. In HuBAMPR
package, sample_derived() and donor_derived() functions are available to
use to see the derived datasets and samples from one sample given sample UUID
or one donor given donor UUID. Specify entity_type parameter to retrieve
derived Dataset or Sample.
sample_uuid <- samples_df |>
filter(last_modified_timestamp >= "2023-01-01" &
last_modified_timestamp <= "2023-10-01",
organ == "Kidney (Left)") |>
head(1) |>
pull(uuid)
sample_uuid
#> [1] "c40774aa2f52a2811db15c5ca1949314"
# Derived Datasets
sample_derived(uuid = sample_uuid, entity_type = "Dataset")
#> # A tibble: 12 × 2
#> uuid derived_dataset_count
#> <chr> <int>
#> 1 4fddf6de0f42a7e2648b547affefc234 1
#> 2 b6fd505b8e8e1829a2783570f9f25256 0
#> 3 c3db2027e148e92fecb85e7d6a1fd708 1
#> 4 3a10030d3323e5353cfdc3ada45cad86 0
#> 5 71642e4c4a9cc12f59f3317b4a19adc9 1
#> 6 bd42ab2f422e45ce6b0f3f55171de8aa 0
#> 7 c8ad223f01b45b25e0dcb07c48a42762 1
#> 8 f7b49444b974c98c6300e0bfe5fc3a75 0
#> 9 beb1b65624fe85b527ee2ce80ef208b2 1
#> 10 c25d6febe5b007ad32bc59246c99833d 0
#> 11 744647801573d1d5700ee7523089734c 1
#> 12 4a98c43ab3b20b06c11dfbed5fd9034b 0
# Derived Samples
sample_derived(uuid = sample_uuid, entity_type = "Sample")
#> # A tibble: 3 × 2
#> uuid organ
#> <chr> <chr>
#> 1 ec54b7d4ab4545166a0d121b3dc1ec3f Kidney (Left)
#> 2 ae98f6ca4f1f9950f7e7e1dedc2acc10 Kidney (Left)
#> 3 b099a37195f532e4b384020dc0e94bb5 Kidney (Left)donor_derived() works same as above.
3.6 Provenance Data
For individual entries from Dataset and Sample entities,
uuid_provenance() helps to retrieve the provenance of the entry as a list of
characters (UUID, HuBMAP ID, and entity type) from the most recent ancestor to
the furthest ancestor. There is no ancestor for Donor UUID, and an empty list
will be returned.
# dataset provenance
dataset_uuid <- "3e4c568d9ce8df9d73b8cddcf8d0fec3"
uuid_provenance(dataset_uuid)
#> [[1]]
#> [1] "eba120ab7bbd864a6f6f3ad41e598d25, Sample"
#>
#> [[2]]
#> [1] "468d73d28b9e8c43ffa5fbd56d8e46e3, Sample"
#>
#> [[3]]
#> [1] "1c749716d32310351cb9557c7e2937a0, Sample"
#>
#> [[4]]
#> [1] "c09f875545a64694d70a28091ffbcf8b, Donor"
# sample provenance
sample_uuid <- "35e16f13caab262f446836f63cf4ad42"
uuid_provenance(sample_uuid)
#> [[1]]
#> [1] "0b43d8d0dbbc5e3923a8b963650ab8e3, Sample"
#>
#> [[2]]
#> [1] "eed96170f42554db84d97d1652bb23ef, Sample"
#>
#> [[3]]
#> [1] "1628b6f7eb615862322d6274a6bc9fa0, Donor"
# donor provenance
donor_uuid <- "0abacde2443881351ff6e9930a706c83"
uuid_provenance(donor_uuid)
#> list()3.8 Additional Information
To read the textual description of one Collection or Publication, use
collection_information() or publication_information() respectively.
collection_information(uuid = collection_uuid)
#> Title
#> Spatiotemporal coordination at the maternal-fetal interface promotes trophoblast invasion and vascular remodeling in the first half of human pregnancy
#> Description
#> Beginning in the first trimester, fetally derived extravillous trophoblasts (EVTs) invade the uterus and remodel its spiral arteries, transforming them into large, dilated blood vessels left with a thin, discontinuous smooth muscle layer and partially lined with EVTs. Several mechanisms have been proposed to explain how EVTs coordinate with the maternal decidua to promote a tissue microenvironment conducive to spiral artery remodeling (SAR). However, it remains a matter of debate which immune and stromal cell types participate in these interactions and how this process evolves with respect to gestational age. Here, we used a multiomic approach that combined the strengths of spatial proteomics and transcriptomics to construct the first spatiotemporal atlas of the human maternal-fetal interface in the first half of pregnancy. We used multiplexed ion beam imaging by time of flight (MIBI-TOF) and a 37-plex antibody panel to analyze ∼500,000 cells and 588 spiral arteries within intact decidua from 66 patients between 6-20 weeks of gestation, integrating this with coregistered transcriptomic profiles. Gestational age substantially influenced the frequency of many maternal immune and stromal cells, with tolerogenic subsets expressing CD206, CD163, TIM-3, Galectin-9, and IDO-1 increasingly enriched and colocalized at later time points. In contrast, SAR progression preferentially correlated with EVT invasion and was transcriptionally defined by 78 gene ontology pathways exhibiting unique monotonic and biphasic trends. Lastly, we developed an integrated model of SAR supporting an intravasation mechanism where invasion is accompanied by upregulation of pro-angiogenic, immunoregulatory EVT programs that promote interactions with vascular endothelium while avoiding activation of immune cells in circulating maternal blood. Taken together, these results support a coordinated model of decidualization in which increasing gestational age drives a transition in maternal decidua towards a tolerogenic niche conducive to locally regulated, EVT-dependent SAR.
#> DOI
#> - https://doi.org/10.35079/hbm585.qpdv.454
#> URL
#> - 10.35079/hbm585.qpdv.454
publication_information(uuid = publication_uuid)
#> Title
#> Organization of the human intestine at single-cell resolution
#> Abstract
#> We investigated the spatial arrangement of individual cells using multiplexed imaging, as well as single-nucleus RNA and open chromatin assays, across eight different regions of the intestine from nine donors. Through comprehensive analyses, we observed significant variations in cell compositions among the intestinal regions and the intricate nature of epithelial subtypes. Furthermore, we discovered that similar cell types form distinct neighborhoods and communities, highlighting the presence of unique immunological niches within the intestine. Additionally, we identified gene regulatory differences within these cells, suggesting the existence of a regulatory differentiation cascade, and established associations between specific cell types and the heritability of intestinal diseases. These findings elucidate the intricate cell composition, regulation, and spatial organization within this organ, providing a valuable reference map for advancing our understanding of human biology and disease.
#> Manuscript
#> - Nature: https://doi.org/10.1038/s41586-023-05915-x
#> Corresponding Authors
#> - John Hickey 0000-0001-9961-7673
#> Data Types
#> - CODEX
#> Organs
#> - Small IntestineSome additional contact/author/contributor information can be retrieved using
dataset_contributor() for Dataset entity, collection_contact() and
collection_contributors() for Collection entity, or
publication_authors() for Publication entity.
# Dataset
dataset_contributors(uuid = dataset_uuid)
#> # A tibble: 2 × 11
#> affiliation display_name email first_name is_contact is_operator
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 "University of Californi… Xingzhao Wen xzwe… Xingzhao Yes Yes
#> 2 "University of Californi… Sheng Zhong szho… Sheng Yes No
#> # ℹ 5 more variables: is_principal_investigator <chr>, last_name <chr>,
#> # metadata_schema_id <chr>, middle_name_or_initial <chr>, orcid <chr>
# Collection
collection_contacts(uuid = collection_uuid)
#> # A tibble: 2 × 3
#> name affiliation orcid_id
#> <chr> <chr> <chr>
#> 1 Shirley Greenbaum Department of Pathology, Stanford University 0000-0002-0680…
#> 2 Michael Angelo Department of Pathology, Stanford University 0000-0003-1531…
collection_contributors(uuid = collection_uuid)
#> # A tibble: 13 × 3
#> name affiliation orcid_id
#> <chr> <chr> <chr>
#> 1 Shirley Greenbaum Department of Pathology, Stanford University 0000-00…
#> 2 Inna Averbukh Department of Pathology, Stanford University 0000-00…
#> 3 Erin Soon Department of Pathology, Stanford University 0000-00…
#> 4 Gabrielle Rizzuto Department of Pathology, UCSF 0000-00…
#> 5 Noah Greenwald Department of Pathology, Stanford University 0000-00…
#> 6 Marc Bosse Department of Pathology, Stanford University 0000-00…
#> 7 Eleni G. Jaswa Department of Obstetrics, Gynecology & Reproducti… 0000-00…
#> 8 Zumana Khair Department of Pathology, Stanford University 0000-00…
#> 9 David Van Valen Division of Biology and Bioengineering, Californi… 0000-00…
#> 10 Leeat Keren Department of Molecular Cell Biology, Weizmann In… 0000-00…
#> 11 Travis Hollmann Department of Pathology, Memorial Sloan Kettering… 0000-00…
#> 12 Matt van de Rjin Department of Pathology, Stanford University 0000-00…
#> 13 Michael Angelo Department of Pathology, Stanford University 0000-00…
# Publication
publication_authors(uuid = publication_uuid)
#> # A tibble: 3 × 3
#> name affiliation orcid_id
#> <chr> <chr> <chr>
#> 1 John Hickey Stanford University 0000-0001-9961-7673
#> 2 Chiara Caraccio Stanford University 0000-0002-3580-1348
#> 3 Garry Nolan Stanford University 0000-0002-8862-90434 File Transfer
For each dataset, there are corresponding data files. Most of the datasets’ files are available on HuBMAP Globus with corresponding URL. Some of the datasets’ files are not available via Globus, but can be accessed via dbGAP (database of Genotypes and Phenotypes) and/or SRA (Sequence Read Archive). But some of the datasets’ files are not available in any authorized platform.
Each dataset available on Globus has different components of data-related files to preview and download, include but not limited to images, metadata files, downstream analysis reports, raw data products, etc.
Use bulk_data_transfer() to know whether data files are open-accessed or
restricted. Only open-accessed files can be downloaded for downstream analysis.
4.0.0.1 Files are publicly accessible
HuBMAP stored all public data files on Globus, which is a open-source and safe
platform for the large-size data storage. For every dataset which the data
files can be publicly accessed, the bulk_data_transfer() function will direct
to corresponding Globus webpage in Chrome.
uuid_globus <- "d1dcab2df80590d8cd8770948abaf976"
bulk_data_transfer(uuid_globus)
By selecting the data file and clicking on “Download” button, the data file can be downloaded to the specific directory.
4.0.0.1.1 Alternative data transfer method using rglobus package
Martin Morgan, one of the HuBMAPR package creators, generated an
experimental package called
rglobus.
Globus is in part a cloud-based file transfer service, available at
https://www.globus.org/. This package provides an R client with the ability
to discover and navigate collections, and to transfer files and directories
between collections. Therefore, rglobus is an alternative method to transfer
HuBMAP data files on the local computer using HuBMAP dataset UUID.
rglobus has the vignette documentation
here using
HuBMAP collection as the main example to illustrate how to discover and
navigate the correct collection, and transfer the files.
Since rglobus is an experimental package, the functionality may not
be complete. It is possible to see transfer issues while using functions. There
will be more information updated in the future. You are welcome to report any
issue or provide any comment here
to help us develop.
4.0.0.2 Files are restricted
For every dataset which the data files are restricted under dbGAP or SRA, the
bulk_data_transfer() function will print out the instruction messages. The
dbGaP or/and SRA link(s) allow the users to request the protected-access
sequence data from authenticated platform.
uuid_dbGAP_SRA <- "d926c41ac08f3c2ba5e61eec83e90b0c"
bulk_data_transfer(uuid_dbGAP_SRA)Pruning cache
Error in bulk_data_transfer(uuid_dbGAP_SRA) :
This dataset contains protected-access human sequence data.
If you are not a Consortium member,
you must access these data through dbGaP if available.
dbGaP authentication is required for downloading.
View documentation on how to attain dbGaP access.
Additional Help: 'https://hubmapconsortium.org/contact-form/'
Navigate to the 'Bioproject' or 'Sequencing Read Archive' links.
dbGaP URL:
https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002267
Select the 'Run' link on the page to download the dataset.
Additional documentation: https://www.ncbi.nlm.nih.gov/sra/docs/.
SRA URL: https://www.ncbi.nlm.nih.gov/sra/SRX13283313.)R session information
#> R Under development (unstable) (2025-01-20 r87609)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.21-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 LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] pryr_0.1.6 HuBMAPR_1.1.7 ggplot2_3.5.1 tidyr_1.3.1 dplyr_1.1.4 BiocStyle_2.35.0
#>
#> loaded via a namespace (and not attached):
#> [1] rappdirs_0.3.3 sass_0.4.9 utf8_1.2.4 generics_0.1.3 stringi_1.8.4
#> [6] digest_0.6.37 magrittr_2.0.3 evaluate_1.0.3 grid_4.5.0 bookdown_0.42
#> [11] fastmap_1.2.0 lobstr_1.1.2 jsonlite_1.8.9 whisker_0.4.1 tinytex_0.54
#> [16] BiocManager_1.30.25 purrr_1.0.2 scales_1.3.0 codetools_0.2-20 httr2_1.1.0
#> [21] jquerylib_0.1.4 cli_3.6.3 rlang_1.1.5 munsell_0.5.1 withr_3.0.2
#> [26] cachem_1.1.0 yaml_2.3.10 tools_4.5.0 colorspace_2.1-1 curl_6.2.0
#> [31] vctrs_0.6.5 rjsoncons_1.3.1 R6_2.5.1 magick_2.8.5 lifecycle_1.0.4
#> [36] stringr_1.5.1 pkgconfig_2.0.3 pillar_1.10.1 bslib_0.8.0 gtable_0.3.6
#> [41] glue_1.8.0 Rcpp_1.0.14 xfun_0.50 tibble_3.2.1 tidyselect_1.2.1
#> [46] knitr_1.49 farver_2.1.2 htmltools_0.5.8.1 rmarkdown_2.29 labeling_0.4.3
#> [51] compiler_4.5.0 prettyunits_1.2.0