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, Entity API, and Ontology API. 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. The Search API is primarily searching relevant data information and is referenced to the Elasticsearch API. The Entity API is specifically utilized in the bulk_data_transfer() function for Globus URL retrieval, while the Ontology API is applied in the organ() function. Referencing to HuBMAP Data Portal, the functions in HuBAMPR package reflects the data information of HuBMAP as much as possible.

HuBMAP Data incorporates three different identifiers: HuBMAP ID, Universally Unique Identifier (UUID), and Digital Object Identifiers (DOI). 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("christinehou11/HuBMAPR")

3 Basic User Guide

3.1 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")

3.2 Data Discovery

HuBMAP data portal page displays five categories of entity data, which are Dataset, Sample, Donor, Publication, and Collection, chronologically (last modified date time). Using corresponding functions to explore entity data.

datasets_df <- datasets()
datasets_df
#> # A tibble: 3,613 × 14
#>    uuid        hubmap_id dataset_type dataset_type_additio…¹ organ analyte_class
#>    <chr>       <chr>     <chr>        <chr>                  <chr> <chr>        
#>  1 908771a0f8… HBM575.L… ATACseq      ""                     Fall… "DNA"        
#>  2 e6f8f61dd9… HBM884.S… ATACseq      ""                     Fall… ""           
#>  3 2720b3ebae… HBM823.R… ATACseq      ""                     Fall… "DNA"        
#>  4 2ffc818343… HBM689.P… ATACseq      ""                     Fall… ""           
#>  5 93f7cf7704… HBM727.Q… ATACseq      ""                     Uter… "DNA"        
#>  6 a79b47ebdd… HBM466.W… ATACseq      ""                     Fall… "DNA"        
#>  7 55b41c9aaa… HBM685.Q… ATACseq      ""                     Uter… "DNA"        
#>  8 ebe3f00de9… HBM833.K… ATACseq      ""                     Fall… ""           
#>  9 719efde175… HBM734.V… ATACseq      ""                     Uter… ""           
#> 10 ae0f6009a5… HBM423.L… ATACseq      ""                     Uter… "DNA"        
#> # ℹ 3,603 more rows
#> # ℹ abbreviated name: ¹​dataset_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>

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 rows

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   424

Any 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> <"b64883fc7c11d070b5e5222c656eaae7",…
#> $ ancestors                        <list> [["b64883fc7c11d070b5e5222c656eaae7"…
#> $ 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> "5cb9ec3acfe11753b9a2899fe87ee0e4"
#> $ descendants                      <list> [["Auto-fluorescence [Image Pyramid]…
#> $ description                      <chr> "Autofluorescence Microscopy collecte…
#> $ display_subtype                  <chr> "Auto-fluorescence"
#> $ doi_url                          <chr> "https://doi.org/10.35079/HBM793.WWZC…
#> $ donor                            <list> ["Jamie Allen", "jamie.l.allen@vander…
#> $ entity_type                      <chr> "Dataset"
#> $ files                            <list> []
#> $ group_name                       <chr> "Vanderbilt TMC"
#> $ group_uuid                       <chr> "73bb26e4-ed43-11e8-8f19-0a7c1eab007a"
#> $ hubmap_id                        <chr> "HBM793.WWZC.833"
#> $ immediate_ancestor_ids           <list> "b64883fc7c11d070b5e5222c656eaae7"
#> $ immediate_descendant_ids         <list> "5cb9ec3acfe11753b9a2899fe87ee0e4"
#> $ index_version                    <chr> "3.5.4"
#> $ last_modified_timestamp          <dbl> 1.71691e+12
#> $ metadata                         <list> [[["a9099c6", "https://github.com/hub…
#> $ origin_samples                   <list> [["Jamie Allen", "jamie.l.allen@vande…
#> $ provider_info                    <chr> "VAN0042-RK-3 block AF : ./VAN0042-RK…
#> $ published_timestamp              <dbl> 1.715267e+12
#> $ registered_doi                   <chr> "10.35079/HBM793.WWZC.833"
#> $ source_samples                   <list> [["Jamie Allen", "jamie.l.allen@vand…
#> $ status                           <chr> "Published"
#> $ title                            <chr> "Auto-fluorescence data from the kid…
#> $ uuid                             <chr> "2e7123dcf2e9092a42ac42e44c2d686b"

# 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.

3.3 Metadata

To retrieve the metadata for Dataset, Sample, and Donor metadata, use dataset_metadata(), sample_metadata(), and donor_metadata().

dataset_metadata("993bb1d6fa02e2755fd69613bb9d6e08")
#> # A tibble: 19 × 2
#>    Key                             Value                                        
#>    <chr>                           <chr>                                        
#>  1 acquisition_instrument_model    "Axio Scan.Z1"                               
#>  2 acquisition_instrument_vendor   "Zeiss Microscopy"                           
#>  3 analyte_class                   "endogenous fluorophores"                    
#>  4 antibodies_path                 "extras/antibodies.tsv"                      
#>  5 contributors_path               "extras/contributors.tsv"                    
#>  6 data_path                       "."                                          
#>  7 dataset_type                    "Auto-fluorescence"                          
#>  8 is_image_preprocessing_required "no"                                         
#>  9 is_targeted                     "No"                                         
#> 10 metadata_schema_id              "c9c6a02b-010e-4217-96dc-f7ef71dd14c4"       
#> 11 parent_sample_id                "HBM836.RXKQ.893"                            
#> 12 preparation_protocol_doi        "https://dx.doi.org/10.17504/protocols.io.7e…
#> 13 source_storage_duration_unit    "hour"                                       
#> 14 source_storage_duration_value   "2"                                          
#> 15 tile_configuration              "Not applicable"                             
#> 16 donor.Age                       "57.0 years"                                 
#> 17 donor.Body Mass Index           "25.30 kg/m2"                                
#> 18 donor.Race                      "White "                                     
#> 19 donor.Sex                       "Male "

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 "

3.4 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.

3.5 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()

3.7 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
#>  An atlas of healthy and injured cell states and niches in the human kidney
#> Abstract
#>  Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei/cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types that include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, epigenomic regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive or maladaptive repair, transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.
#> Manuscript
#>  - Nature: https://doi.org/10.1038/s41586-023-05769-3
#> Corresponding Authors
#>  - Michael T. Eadon 0000-0003-3066-2876
#>  - Pierre C. Dagher 0000-0003-3321-5561
#>  - Tarek M. El-Achkar 0000-0003-4645-3614
#>  - Kun Zhang 0000-0002-7596-5224
#>  - Matthias Kretzler 0000-0003-4064-0582
#>  - Sanjay Jain 0000-0003-2804-127X
#> Data Types
#>  - RNAseq
#> Organs
#>  - Kidney (Right)

Some 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: 50 × 3
#>    name                  affiliation                                    orcid_id
#>    <chr>                 <chr>                                          <chr>   
#>  1 Blue B. Lake          Department of Bioengineering, University of C… 0000-00…
#>  2 Rajasree Menon        Department of Computational Medicine and Bioi… 0000-00…
#>  3 Seth Winfree          Department of Pathology and Microbiology, Uni… 0000-00…
#>  4 Qiwen Hu              Department of Biomedical Informatics, Harvard… 0000-00…
#>  5 Ricardo Melo Ferreira Department of Medicine, Indiana University Sc… 0000-00…
#>  6 Kian Kalhor           Department of Bioengineering, University of C… 0000-00…
#>  7 Daria Barwinska       Department of Medicine, Indiana University Sc… 0000-00…
#>  8 Edgar A. Otto         Department of Internal Medicine, Division of … 0000-00…
#>  9 Michael Ferkowicz     Department of Medicine, Indiana University Sc… 0000-00…
#> 10 Dinh Diep             Department of Bioengineering, University of C… 0000-00…
#> # ℹ 40 more rows

4 File Download

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. The function will direct you to chrome if the files are accessible; otherwise, the error messages will be printed out with addition instructions, either providing dbGAP/SRA URLs or pointing out the unavailability.

uuid_globus <- "d1dcab2df80590d8cd8770948abaf976"

bulk_data_transfer(uuid_globus)

uuid_dbGAP_SRA <- "d926c41ac08f3c2ba5e61eec83e90b0c"

bulk_data_transfer(uuid_dbGAP_SRA)

uuid_not_avail <- "0eb5e457b4855ce28531bc97147196b6"

bulk_data_transfer(uuid_not_avail)

You can choose to download data files on Globus webpage by clicking download button after choosing the desired document. You can also preview and download data files using rglobus package. Follow the instructions here.

R session information

#> R version 4.4.1 (2024-06-14)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#> 
#> Matrix products: default
#> BLAS:   /media/volume/teran2_disk/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.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] HuBMAPR_1.0.3    ggplot2_3.5.1    tidyr_1.3.1      dplyr_1.1.4      BiocStyle_2.34.0
#> 
#> 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_1.0.1      grid_4.4.1          bookdown_0.41      
#> [11] fastmap_1.2.0       jsonlite_1.8.9      whisker_0.4.1       tinytex_0.54        BiocManager_1.30.25
#> [16] purrr_1.0.2         fansi_1.0.6         scales_1.3.0        httr2_1.0.6         jquerylib_0.1.4    
#> [21] cli_3.6.3           rlang_1.1.4         munsell_0.5.1       withr_3.0.2         cachem_1.1.0       
#> [26] yaml_2.3.10         tools_4.4.1         colorspace_2.1-1    curl_6.0.0          vctrs_0.6.5        
#> [31] rjsoncons_1.3.1     R6_2.5.1            lifecycle_1.0.4     magick_2.8.5        stringr_1.5.1      
#> [36] pkgconfig_2.0.3     pillar_1.9.0        bslib_0.8.0         gtable_0.3.6        glue_1.8.0         
#> [41] Rcpp_1.0.13-1       xfun_0.49           tibble_3.2.1        tidyselect_1.2.1    knitr_1.49         
#> [46] farver_2.1.2        htmltools_0.5.8.1   rmarkdown_2.29      labeling_0.4.3      compiler_4.4.1