h5ad files
The purpose of this package is to make it easy to query the Human Cell Atlas Data Portal via their data browser API. Visit the Human Cell Atlas for more information on the project.
Evaluate the following code chunk to install packages required for this vignette.
## install from Bioconductor if you haven't already
pkgs <- c("httr", "dplyr", "LoomExperiment", "hca")
pkgs_needed <- pkgs[!pkgs %in% rownames(installed.packages())]
BiocManager::install(pkgs_needed)Load the packages into your R session.
library(httr)
library(dplyr)
library(LoomExperiment)
library(hca)To illustrate use of this package, consider the task of downloading a ‘loom’ file summarizing single-cell gene expression observed in an HCA research project. This could be accomplished by visiting the HCA data portal (at https://data.humancellatlas.org/explore) in a web browser and selecting projects interactively, but it is valuable to accomplish the same goal in a reproducible, flexible, programmatic way. We will (1) discover projects available in the HCA Data Coordinating Center that have loom files; and (2) retrieve the file from the HCA and import the data into R as a ‘LoomExperiment’ object. For illustration, we focus on the ‘Single cell transcriptome analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns’ project.
Use projects() to retrieve the first 200 projects in the HCA’s
default catalog.
projects(size = 200)
## # A tibble: 200 × 14
## projectId projectTitle genusSpecies sampleEntityType specimenOrgan
## <chr> <chr> <list> <list> <list>
## 1 74b6d569-3b11-42ef-… 1.3 Million… <chr [1]> <chr [1]> <chr [1]>
## 2 53c53cd4-8127-4e12-… A Cellular … <chr [1]> <chr [1]> <chr [1]>
## 3 7027adc6-c9c9-46f3-… A Cellular … <chr [1]> <chr [1]> <chr [1]>
## 4 94e4ee09-9b4b-410a-… A Human Liv… <chr [1]> <chr [2]> <chr [1]>
## 5 c5b475f2-76b3-4a8e-… A Partial P… <chr [1]> <chr [1]> <chr [1]>
## 6 60ea42e1-af49-42f5-… A Protocol … <chr [1]> <chr [1]> <chr [1]>
## 7 ef1e3497-515e-4bbe-… A Single-Ce… <chr [1]> <chr [1]> <chr [3]>
## 8 9ac53858-606a-4b89-… A Single-Ce… <chr [1]> <chr [1]> <chr [1]>
## 9 258c5e15-d125-4f2d-… A Single-Ce… <chr [1]> <chr [1]> <chr [1]>
## 10 894ae6ac-5b48-41a8-… A Single-Ce… <chr [1]> <chr [1]> <chr [1]>
## # ℹ 190 more rows
## # ℹ 9 more variables: specimenOrganPart <list>, selectedCellType <list>,
## # libraryConstructionApproach <list>, nucleicAcidSource <list>,
## # pairedEnd <list>, workflow <list>, specimenDisease <list>,
## # donorDisease <list>, developmentStage <list>Use filters() to restrict the projects to just those that contain at
least one ‘loom’ file.
project_filter <- filters(fileFormat = list(is = "loom"))
project_tibble <- projects(project_filter)
project_tibble
## # A tibble: 79 × 14
## projectId projectTitle genusSpecies sampleEntityType specimenOrgan
## <chr> <chr> <list> <list> <list>
## 1 53c53cd4-8127-4e12-… A Cellular … <chr [1]> <chr [1]> <chr [1]>
## 2 7027adc6-c9c9-46f3-… A Cellular … <chr [1]> <chr [1]> <chr [1]>
## 3 c1810dbc-16d2-45c3-… A cell atla… <chr [2]> <chr [1]> <chr [2]>
## 4 a9301beb-e9fa-42fe-… A human cel… <chr [1]> <chr [1]> <chr [14]>
## 5 996120f9-e84f-409f-… A human sin… <chr [1]> <chr [1]> <chr [1]>
## 6 842605c7-375a-47c5-… A single ce… <chr [1]> <chr [1]> <chr [1]>
## 7 cc95ff89-2e68-4a08-… A single ce… <chr [1]> <chr [1]> <chr [3]>
## 8 a004b150-1c36-4af6-… A single-ce… <chr [1]> <chr [1]> <chr [1]>
## 9 1cd1f41f-f81a-486b-… A single-ce… <chr [1]> <chr [1]> <chr [1]>
## 10 8185730f-4113-40d3-… A single-ce… <chr [1]> <chr [1]> <chr [1]>
## # ℹ 69 more rows
## # ℹ 9 more variables: specimenOrganPart <list>, selectedCellType <list>,
## # libraryConstructionApproach <list>, nucleicAcidSource <list>,
## # pairedEnd <list>, workflow <list>, specimenDisease <list>,
## # donorDisease <list>, developmentStage <list>Use standard R commands to further filter projects to the one we are
interested in, with title starting with “Single…”. Extract the
unique projectId for the first project with this title.
project_tibble |>
filter(startsWith(projectTitle, "Single")) |>
head(1) |>
t()
## [,1]
## projectId "4d6f6c96-2a83-43d8-8fe1-0f53bffd4674"
## projectTitle "Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations"
## genusSpecies "Homo sapiens"
## sampleEntityType "specimens"
## specimenOrgan "liver"
## specimenOrganPart "caudate lobe"
## selectedCellType character,0
## libraryConstructionApproach "10x 3' v2"
## nucleicAcidSource "single cell"
## pairedEnd FALSE
## workflow character,2
## specimenDisease "normal"
## donorDisease "normal"
## developmentStage "human adult stage"
projectIds <-
project_tibble |>
filter(startsWith(projectTitle, "Single")) |>
dplyr::pull(projectId)
projectId <- projectIds[1]A project id can be used to discover the title or additional project information.
project_title(projectId)
## [1] "Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations"
project_information(projectId)
## Title
## Single cell RNA sequencing of human liver reveals distinct
## intrahepatic macrophage populations
## Contributors (unknown order; any role)
## Sonya,A,MacParland, Jeff,C,Liu, Gary,D,Bader, Ian,D,McGilvray,
## Xue-Zhong Ma, Brendan,T,Innes, Agata,M,Bartczak, Blair,K,Gage, Justin
## Manuel, Nicholas Khuu, Juan Echeverri, Ivan Linares, Rahul Gupta,
## Michael,L,Cheng, Lewis,Y,Liu, Damra Camat, Sai,W,Chung,
## Rebecca,K,Seliga, Zigong Shao, Elizabeth Lee, Shinichiro Ogawa, Mina
## Ogawa, Michael,D,Wilson, Jason,E,Fish, Markus Selzner, Anand
## Ghanekar, David Grant, Paul Greig, Gonzalo Sapisochin, Nazia Selzner,
## Neil Winegarden, Oyedele Adeyi, Gordon Keller, William,G,Sullivan
## Description
## The liver is the largest solid organ in the body and is critical for
## metabolic and immune functions. However, little is known about the
## cells that make up the human liver and its immune microenvironment.
## Here we report a map of the cellular landscape of the human liver
## using single-cell RNA sequencing. We provide the transcriptional
## profiles of 8444 parenchymal and non-parenchymal cells obtained from
## the fractionation of fresh hepatic tissue from five human livers.
## Using gene expression patterns, flow cytometry, and
## immunohistochemical examinations, we identify 20 discrete cell
## populations of hepatocytes, endothelial cells, cholangiocytes,
## hepatic stellate cells, B cells, conventional and non-conventional T
## cells, NK-like cells, and distinct intrahepatic monocyte/macrophage
## populations. Together, our study presents a comprehensive view of the
## human liver at single-cell resolution that outlines the
## characteristics of resident cells in the liver, and in particular
## provides a map of the human hepatic immune microenvironment.
## DOI
## 10.1038/s41467-018-06318-7
## URL
## https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6197289/
## Project
## https://data.humancellatlas.org/explore/projects/4d6f6c96-2a83-43d8-8fe1-0f53bffd4674files() retrieves (the first 1000) files from the Human Cell Atlas
data portal. Construct a filter to restrict the files to loom files
from the project we are interested in.
file_filter <- filters(
projectId = list(is = projectId),
fileFormat = list(is = "loom")
)
# only the two smallest files
file_tibble <- files(file_filter, size = 2, sort = "fileSize", order = "asc")
file_tibble
## # A tibble: 2 × 8
## fileId name fileFormat size version projectTitle projectId url
## <chr> <chr> <chr> <int> <chr> <chr> <chr> <chr>
## 1 b8150aca-83a2-5c… d178… loom 1.11e9 2021-0… Single cell… 4d6f6c96… http…
## 2 b1f60da2-db89-55… sc-l… loom 1.18e9 2021-0… Single cell… 4d6f6c96… http…files_download() will download one or more files (one for each row)
in file_tibble. The download is more complicated than simply
following the url column of file_tibble, so it is not possible to
simply copy the url into a browser. We’ll download the file and then
immediately import it into R.
file_locations <- file_tibble |> files_download()
LoomExperiment::import(unname(file_locations[1]),
type ="SingleCellLoomExperiment")
## class: SingleCellLoomExperiment
## dim: 58347 348643
## metadata(10): last_modified CreationDate ...
## optimus_output_schema_version pipeline_version
## assays(1): matrix
## rownames: NULL
## rowData names(29): Gene antisense_reads ... reads_per_molecule
## spliced_reads
## colnames: NULL
## colData names(43): CellID antisense_reads ... reads_unmapped
## spliced_reads
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## rowGraphs(0): NULL
## colGraphs(0): NULLNote that files_download() uses [BiocFileCache][https://bioconductor.org/packages/BiocFileCache],
so individual files are only downloaded once.
h5ad filesThis example walks through the process of file discovery and retrieval
in a little more detail, using h5ad files created by the Python
AnnData analysis software and available for some experiments in the
default catalog.
The first challenge is to understand what file formats are available from the HCA. Obtain a tibble describing the ‘facets’ of the data, the number of terms used in each facet, and the number of distinct values used to describe projects.
projects_facets()
## # A tibble: 39 × 3
## facet n_terms n_values
## <chr> <int> <int>
## 1 accessible 2 470
## 2 assayType 2 470
## 3 biologicalSex 5 822
## 4 bionetworkName 8 473
## 5 cellLineType 6 487
## 6 contactName 5911 7362
## 7 contentDescription 72 1921
## 8 dataUseRestriction 3 470
## 9 developmentStage 185 1125
## 10 donorDisease 487 1227
## # ℹ 29 more rowsNote the fileFormat facet, and repeat projects_facets() to
discover detail about available file formats
projects_facets("fileFormat")
## # A tibble: 85 × 3
## facet term count
## <chr> <chr> <int>
## 1 fileFormat fastq.gz 345
## 2 fileFormat xlsx 343
## 3 fileFormat tsv.gz 98
## 4 fileFormat tar 89
## 5 fileFormat mtx.gz 84
## 6 fileFormat loom 79
## 7 fileFormat bam 77
## 8 fileFormat csv.gz 73
## 9 fileFormat txt.gz 67
## 10 fileFormat csv 48
## # ℹ 75 more rowsNote that there are 8 uses of the h5ad file format. Use this as a
filter to discover relevant projects.
filters <- filters(fileFormat = list(is = "h5ad"))
projects(filters)
## # A tibble: 39 × 14
## projectId projectTitle genusSpecies sampleEntityType specimenOrgan
## <chr> <chr> <list> <list> <list>
## 1 cdabcf0b-7602-4abf-… A blood atl… <chr [1]> <chr [1]> <chr [1]>
## 2 c1810dbc-16d2-45c3-… A cell atla… <chr [2]> <chr [1]> <chr [2]>
## 3 c0518445-3b3b-49c6-… A cellular … <chr [1]> <chr [1]> <chr [2]>
## 4 b176d756-62d8-4933-… A human emb… <chr [2]> <chr [1]> <chr [2]>
## 5 2fe3c60b-ac1a-4c61-… A human fet… <chr [1]> <chr [2]> <chr [2]>
## 6 73769e0a-5fcd-41f4-… A proximal-… <chr [1]> <chr [1]> <chr [2]>
## 7 cc95ff89-2e68-4a08-… A single ce… <chr [1]> <chr [1]> <chr [3]>
## 8 957261f7-2bd6-4358-… A spatially… <chr [1]> <chr [1]> <chr [1]>
## 9 1dddae6e-3753-48af-… Cell Types … <chr [1]> <chr [2]> <chr [2]>
## 10 ad98d3cd-26fb-4ee3-… Cells of th… <chr [1]> <chr [1]> <chr [1]>
## # ℹ 29 more rows
## # ℹ 9 more variables: specimenOrganPart <list>, selectedCellType <list>,
## # libraryConstructionApproach <list>, nucleicAcidSource <list>,
## # pairedEnd <list>, workflow <list>, specimenDisease <list>,
## # donorDisease <list>, developmentStage <list>The default tibble produced by projects() contains only some of the
information available; the information is much richer.
To obtain a tibble with an expanded set of columns, you can specify that using
the as parameter set to "tibble_expanded".
# an expanded set of columns for all or the first 4 projects
projects(as = 'tibble_expanded', size = 4)
## # A tibble: 4 × 127
## projectId cellSuspensions.orga…¹ cellSuspensions.organ cellSuspensions.sele…²
## <chr> <list> <chr> <list>
## 1 74b6d569-… <chr [1]> brain <chr [1]>
## 2 53c53cd4-… <chr [2]> prostate gland <chr [7]>
## 3 7027adc6-… <chr [0]> heart <chr [0]>
## 4 94e4ee09-… <chr [0]> liver <chr [0]>
## # ℹ abbreviated names: ¹cellSuspensions.organPart,
## # ²cellSuspensions.selectedCellType
## # ℹ 123 more variables: cellSuspensions.totalCells <int>,
## # cellSuspensions.totalCellsRedundant <int>,
## # dates.aggregateLastModifiedDate <chr>, dates.aggregateSubmissionDate <chr>,
## # dates.aggregateUpdateDate <chr>, dates.lastModifiedDate <chr>,
## # dates.submissionDate <chr>, dates.updateDate <chr>, …In the next sections, we’ll cover other options for the as parameter, and the data formats
they return.
projects() as an R listInstead of retrieving the result of projects() as a tibble, retrieve
it as a ‘list-of-lists’
projects_list <- projects(size = 200, as = "list")This is a complicated structure. We will use lengths(), names(),
and standard R list selection operations to navigate this a bit. At
the top level there are three elements.
lengths(projects_list)
## pagination termFacets hits
## 8 40 200hits represents each project as a list, e.g,.
lengths(projects_list$hits[[1]])
## protocols entryId sources projects
## 2 1 1 1
## samples specimens cellLines donorOrganisms
## 1 1 0 1
## organoids cellSuspensions dates fileTypeSummaries
## 0 1 1 2shows that there are 10 different ways in which the first project is described. Each component is itself a list-of-lists, e.g.,
lengths(projects_list$hits[[1]]$projects[[1]])
## projectId projectTitle projectShortname
## 1 1 1
## laboratory estimatedCellCount isTissueAtlasProject
## 1 1 1
## tissueAtlas bionetworkName dataUseRestriction
## 0 1 0
## projectDescription contributors publications
## 1 6 1
## supplementaryLinks matrices contributedAnalyses
## 1 0 1
## accessions accessible
## 3 1
projects_list$hits[[1]]$projects[[1]]$projectTitle
## [1] "1.3 Million Brain Cells from E18 Mice"One can use standard R commands to navigate this data structure, and
to, e.g., extract the projectTitle of each project.
projects() as an lolUse as = "lol" to create a more convenient way to select, filter and
extract elements from the list-of-lists by projects().
lol <- projects(size = 200, as = "lol")
lol
## # class: lol_hca lol
## # number of distinct paths: 26673
## # total number of elements: 187312
## # number of leaf paths: 20690
## # number of leaf elements: 149162
## # lol_path():
## # A tibble: 26,673 × 3
## path n is_leaf
## <chr> <int> <lgl>
## 1 hits 1 FALSE
## 2 hits[*] 200 FALSE
## 3 hits[*].cellLines 200 FALSE
## 4 hits[*].cellLines[*] 29 FALSE
## 5 hits[*].cellLines[*].cellLineType 29 FALSE
## 6 hits[*].cellLines[*].cellLineType[*] 38 TRUE
## 7 hits[*].cellLines[*].id 29 FALSE
## 8 hits[*].cellLines[*].id[*] 122 TRUE
## 9 hits[*].cellLines[*].modelOrgan 29 FALSE
## 10 hits[*].cellLines[*].modelOrgan[*] 39 TRUE
## # ℹ 26,663 more rowsUse lol_select() to restrict the lol to particular paths, and
lol_filter() to filter results to paths that are leafs, or with
specific numbers of entries.
lol_select(lol, "hits[*].projects[*]")
## # class: lol_hca lol
## # number of distinct paths: 26548
## # total number of elements: 133740
## # number of leaf paths: 20629
## # number of leaf elements: 110208
## # lol_path():
## # A tibble: 26,548 × 3
## path n is_leaf
## <chr> <int> <lgl>
## 1 hits[*].projects[*] 200 FALSE
## 2 hits[*].projects[*].accessible 200 TRUE
## 3 hits[*].projects[*].accessions 200 FALSE
## 4 hits[*].projects[*].accessions[*] 569 FALSE
## 5 hits[*].projects[*].accessions[*].accession 569 TRUE
## 6 hits[*].projects[*].accessions[*].namespace 569 TRUE
## 7 hits[*].projects[*].bionetworkName 200 FALSE
## 8 hits[*].projects[*].bionetworkName[*] 201 TRUE
## 9 hits[*].projects[*].contributedAnalyses 200 FALSE
## 10 hits[*].projects[*].contributedAnalyses.developmentStage 2 FALSE
## # ℹ 26,538 more rows
lol_select(lol, "hits[*].projects[*]") |>
lol_filter(n == 44, is_leaf)
## # class: lol_hca lol
## # number of distinct paths: 0
## # total number of elements: 0
## # number of leaf paths: 0
## # number of leaf elements: 0
## # lol_path():
## # A tibble: 0 × 3
## # ℹ 3 variables: path <chr>, n <int>, is_leaf <lgl>lol_pull() extracts a path from the lol as a vector; lol_lpull()
extracts paths as lists.
titles <- lol_pull(lol, "hits[*].projects[*].projectTitle")
length(titles)
## [1] 200
head(titles, 2)
## [1] "1.3 Million Brain Cells from E18 Mice"
## [2] "A Cellular Anatomy of the Normal Adult Human Prostate and Prostatic Urethra"projects() tibbles with specific columnsThe path or its abbreviation can be used to specify the columns of
the tibble to be returned by the projects() query.
Here we retrieve additional details of donor count and total cells by adding appropriate path abbreviations to a named character vector. Names on the character vector can be used to rename the path more concisely, but the paths must uniquely identify elements in the list-of-lists.
columns <- c(
projectId = "hits[*].entryId",
projectTitle = "hits[*].projects[*].projectTitle",
genusSpecies = "hits[*].donorOrganisms[*].genusSpecies[*]",
donorCount = "hits[*].donorOrganisms[*].donorCount",
cellSuspensions.organ = "hits[*].cellSuspensions[*].organ[*]",
totalCells = "hits[*].cellSuspensions[*].totalCells"
)
projects <- projects(filters, columns = columns)
projects
## # A tibble: 39 × 6
## projectId projectTitle genusSpecies donorCount cellSuspensions.organ
## <chr> <chr> <list> <int> <list>
## 1 cdabcf0b-7602-4ab… A blood atl… <chr [1]> 124 <chr [1]>
## 2 c1810dbc-16d2-45c… A cell atla… <chr [2]> 24 <chr [2]>
## 3 c0518445-3b3b-49c… A cellular … <chr [1]> 17 <chr [2]>
## 4 b176d756-62d8-493… A human emb… <chr [2]> 36 <chr [2]>
## 5 2fe3c60b-ac1a-4c6… A human fet… <chr [1]> 38 <chr [2]>
## 6 73769e0a-5fcd-41f… A proximal-… <chr [1]> 3 <chr [2]>
## 7 cc95ff89-2e68-4a0… A single ce… <chr [1]> 28 <chr [3]>
## 8 957261f7-2bd6-435… A spatially… <chr [1]> 13 <chr [1]>
## 9 1dddae6e-3753-48a… Cell Types … <chr [1]> 6 <chr [1]>
## 10 ad98d3cd-26fb-4ee… Cells of th… <chr [1]> 14 <chr [1]>
## # ℹ 29 more rows
## # ℹ 1 more variable: totalCells <list>Note that the cellSuspensions.organ and totalCells columns have more than
one entry per project.
projects |>
select(projectId, cellSuspensions.organ, totalCells)
## # A tibble: 39 × 3
## projectId cellSuspensions.organ totalCells
## <chr> <list> <list>
## 1 cdabcf0b-7602-4abf-9afb-3b410e545703 <chr [1]> <int [0]>
## 2 c1810dbc-16d2-45c3-b45e-3e675f88d87b <chr [2]> <int [2]>
## 3 c0518445-3b3b-49c6-b8fc-c41daa4eacba <chr [2]> <int [2]>
## 4 b176d756-62d8-4933-83a4-8b026380262f <chr [2]> <int [2]>
## 5 2fe3c60b-ac1a-4c61-9b59-f6556c0fce63 <chr [2]> <int [1]>
## 6 73769e0a-5fcd-41f4-9083-41ae08bfa4c1 <chr [2]> <int [0]>
## 7 cc95ff89-2e68-4a08-a234-480eca21ce79 <chr [3]> <int [3]>
## 8 957261f7-2bd6-4358-a6ed-24ee080d5cfc <chr [1]> <int [0]>
## 9 1dddae6e-3753-48af-b20e-fa22abad125d <chr [1]> <int [0]>
## 10 ad98d3cd-26fb-4ee3-99c9-8a2ab085e737 <chr [1]> <int [1]>
## # ℹ 29 more rowsIn this case, the mapping between cellSuspensions.organ and totalCells
is clear, but in general more refined navigation of the lol structure may be
necessary.
projects |>
select(projectId, cellSuspensions.organ, totalCells) |>
filter(
## 2023-06-06 two projects have different 'organ' and
## 'totalCells' lengths, causing problems with `unnest()`
lengths(cellSuspensions.organ) == lengths(totalCells)
) |>
tidyr::unnest(c("cellSuspensions.organ", "totalCells"))
## # A tibble: 28 × 3
## projectId cellSuspensions.organ totalCells
## <chr> <chr> <int>
## 1 c1810dbc-16d2-45c3-b45e-3e675f88d87b thymus 456000
## 2 c1810dbc-16d2-45c3-b45e-3e675f88d87b colon 16000
## 3 c0518445-3b3b-49c6-b8fc-c41daa4eacba lung 40200
## 4 c0518445-3b3b-49c6-b8fc-c41daa4eacba nose 7087
## 5 b176d756-62d8-4933-83a4-8b026380262f forelimb 48000
## 6 b176d756-62d8-4933-83a4-8b026380262f hindlimb 56000
## 7 cc95ff89-2e68-4a08-a234-480eca21ce79 immune system 274182
## 8 cc95ff89-2e68-4a08-a234-480eca21ce79 blood 1615910
## 9 cc95ff89-2e68-4a08-a234-480eca21ce79 bone marrow 600000
## 10 ad98d3cd-26fb-4ee3-99c9-8a2ab085e737 heart 791000
## # ℹ 18 more rowsSelect the following entry, augment the filter, and query available files
projects |>
filter(startsWith(projectTitle, "Reconstruct")) |>
glimpse()
## Rows: 1
## Columns: 6
## $ projectId <chr> "f83165c5-e2ea-4d15-a5cf-33f3550bffde"
## $ projectTitle <chr> "Reconstructing the human first trimester fetal-…
## $ genusSpecies <list> "Homo sapiens"
## $ donorCount <int> 16
## $ cellSuspensions.organ <list> <"blood", "decidua", "placenta">
## $ totalCells <list> <>This approach can be used to customize the tibbles returned by the
other main functions in the package, files(), samples(), and
bundles().
The relevant file can be selected and downloaded using the technique in the first example.
filters <- filters(
projectId = list(is = "f83165c5-e2ea-4d15-a5cf-33f3550bffde"),
fileFormat = list(is = "h5ad")
)
files <-
files(filters) |>
head(1) # only first file, for demonstration
files |> t()
## [,1]
## fileId "6d4fedcf-857d-5fbb-9928-8b9605500a69"
## name "vento18_ss2.processed.h5ad"
## fileFormat "h5ad"
## size "82121633"
## version "2021-02-10T16:56:40.419579Z"
## projectTitle "Reconstructing the human first trimester fetal-maternal interface using single cell transcriptomics"
## projectId "f83165c5-e2ea-4d15-a5cf-33f3550bffde"
## url "https://service.azul.data.humancellatlas.org/repository/files/6d4fedcf-857d-5fbb-9928-8b9605500a69?catalog=dcp42&version=2021-02-10T16%3A56%3A40.419579Z"file_path <- files_download(files)"h5ad" files can be read as SingleCellExperiment objects using the
zellkonverter package.
## don't want large amount of data read from disk
sce <- zellkonverter::readH5AD(file_path, use_hdf5 = TRUE)
sceproject_filter <- filters(fileFormat = list(is = "csv"))
project_tibble <- projects(project_filter)
project_tibble |>
filter(
startsWith(
projectTitle,
"Reconstructing the human first trimester"
)
)
## # A tibble: 1 × 14
## projectId projectTitle genusSpecies sampleEntityType specimenOrgan
## <chr> <chr> <list> <list> <list>
## 1 f83165c5-e2ea-4d15-a… Reconstruct… <chr [1]> <chr [1]> <chr [3]>
## # ℹ 9 more variables: specimenOrganPart <list>, selectedCellType <list>,
## # libraryConstructionApproach <list>, nucleicAcidSource <list>,
## # pairedEnd <list>, workflow <list>, specimenDisease <list>,
## # donorDisease <list>, developmentStage <list>
projectId <-
project_tibble |>
filter(
startsWith(
projectTitle,
"Reconstructing the human first trimester"
)
) |>
pull(projectId)
file_filter <- filters(
projectId = list(is = projectId),
fileFormat = list(is = "csv")
)
## first 4 files will be returned
file_tibble <- files(file_filter, size = 4)
file_tibble |>
files_download()
## 7f9a181e-24c5-5462-b308-7fef5b1bda2a-2021-02-10T16:56:40.419579Z
## "/home/biocbuild/.cache/R/hca/1140313915de75_1140313915de75.csv"
## d04c6e3c-b740-5586-8420-4480a1b5706c-2021-02-10T16:56:40.419579Z
## "/home/biocbuild/.cache/R/hca/1140312a75589_1140312a75589.csv"
## d30ffc0b-7d6e-5b85-aff9-21ec69663a81-2021-02-10T16:56:40.419579Z
## "/home/biocbuild/.cache/R/hca/1140313cd10871_1140313cd10871.csv"
## e1517725-01b0-5346-9788-afca63e9993a-2021-02-10T16:56:40.419579Z
## "/home/biocbuild/.cache/R/hca/114031789c18db_114031789c18db.csv"The files(), bundles(), and samples() can all return many 1000’s
of results. It is necessary to ‘page’ through these to see all of
them. We illustrate pagination with projects(), retrieving only 30 projects.
Pagination works for the default tibble output
page_1_tbl <- projects(size = 30)
page_1_tbl
## # A tibble: 30 × 14
## projectId projectTitle genusSpecies sampleEntityType specimenOrgan
## <chr> <chr> <list> <list> <list>
## 1 74b6d569-3b11-42ef-… 1.3 Million… <chr [1]> <chr [1]> <chr [1]>
## 2 53c53cd4-8127-4e12-… A Cellular … <chr [1]> <chr [1]> <chr [1]>
## 3 7027adc6-c9c9-46f3-… A Cellular … <chr [1]> <chr [1]> <chr [1]>
## 4 94e4ee09-9b4b-410a-… A Human Liv… <chr [1]> <chr [2]> <chr [1]>
## 5 c5b475f2-76b3-4a8e-… A Partial P… <chr [1]> <chr [1]> <chr [1]>
## 6 60ea42e1-af49-42f5-… A Protocol … <chr [1]> <chr [1]> <chr [1]>
## 7 ef1e3497-515e-4bbe-… A Single-Ce… <chr [1]> <chr [1]> <chr [3]>
## 8 9ac53858-606a-4b89-… A Single-Ce… <chr [1]> <chr [1]> <chr [1]>
## 9 258c5e15-d125-4f2d-… A Single-Ce… <chr [1]> <chr [1]> <chr [1]>
## 10 894ae6ac-5b48-41a8-… A Single-Ce… <chr [1]> <chr [1]> <chr [1]>
## # ℹ 20 more rows
## # ℹ 9 more variables: specimenOrganPart <list>, selectedCellType <list>,
## # libraryConstructionApproach <list>, nucleicAcidSource <list>,
## # pairedEnd <list>, workflow <list>, specimenDisease <list>,
## # donorDisease <list>, developmentStage <list>
page_2_tbl <- page_1_tbl |> hca_next()
page_2_tbl
## # A tibble: 30 × 14
## projectId projectTitle genusSpecies sampleEntityType specimenOrgan
## <chr> <chr> <list> <list> <list>
## 1 9f17ed7d-9325-4723-… A single ce… <chr [1]> <chr [1]> <chr [1]>
## 2 842605c7-375a-47c5-… A single ce… <chr [1]> <chr [1]> <chr [1]>
## 3 cc95ff89-2e68-4a08-… A single ce… <chr [1]> <chr [1]> <chr [3]>
## 4 a62dae2e-cd69-4d5c-… A single-ce… <chr [2]> <chr [1]> <chr [6]>
## 5 6663070f-fd8b-41a9-… A single-ce… <chr [1]> <chr [1]> <chr [1]>
## 6 c31fa434-c9ed-4263-… A single-ce… <chr [1]> <chr [1]> <chr [18]>
## 7 dcc28fb3-7bab-48ce-… A single-ce… <chr [1]> <chr [1]> <chr [1]>
## 8 d3446f0c-30f3-4a12-… A single-ce… <chr [1]> <chr [1]> <chr [1]>
## 9 a004b150-1c36-4af6-… A single-ce… <chr [1]> <chr [1]> <chr [1]>
## 10 1defdada-a365-44ad-… A single-ce… <chr [1]> <chr [1]> <chr [1]>
## # ℹ 20 more rows
## # ℹ 9 more variables: specimenOrganPart <list>, selectedCellType <list>,
## # libraryConstructionApproach <list>, nucleicAcidSource <list>,
## # pairedEnd <list>, workflow <list>, specimenDisease <list>,
## # donorDisease <list>, developmentStage <list>
## should be identical to page_1_tbl
page_2_tbl |> hca_prev()
## # A tibble: 30 × 14
## projectId projectTitle genusSpecies sampleEntityType specimenOrgan
## <chr> <chr> <list> <list> <list>
## 1 74b6d569-3b11-42ef-… 1.3 Million… <chr [1]> <chr [1]> <chr [1]>
## 2 53c53cd4-8127-4e12-… A Cellular … <chr [1]> <chr [1]> <chr [1]>
## 3 7027adc6-c9c9-46f3-… A Cellular … <chr [1]> <chr [1]> <chr [1]>
## 4 94e4ee09-9b4b-410a-… A Human Liv… <chr [1]> <chr [2]> <chr [1]>
## 5 c5b475f2-76b3-4a8e-… A Partial P… <chr [1]> <chr [1]> <chr [1]>
## 6 60ea42e1-af49-42f5-… A Protocol … <chr [1]> <chr [1]> <chr [1]>
## 7 ef1e3497-515e-4bbe-… A Single-Ce… <chr [1]> <chr [1]> <chr [3]>
## 8 9ac53858-606a-4b89-… A Single-Ce… <chr [1]> <chr [1]> <chr [1]>
## 9 258c5e15-d125-4f2d-… A Single-Ce… <chr [1]> <chr [1]> <chr [1]>
## 10 894ae6ac-5b48-41a8-… A Single-Ce… <chr [1]> <chr [1]> <chr [1]>
## # ℹ 20 more rows
## # ℹ 9 more variables: specimenOrganPart <list>, selectedCellType <list>,
## # libraryConstructionApproach <list>, nucleicAcidSource <list>,
## # pairedEnd <list>, workflow <list>, specimenDisease <list>,
## # donorDisease <list>, developmentStage <list>Pagination also works for the lol objects
page_1_lol <- projects(size = 5, as = "lol")
page_1_lol |>
lol_pull("hits[*].projects[*].projectTitle")
## [1] "1.3 Million Brain Cells from E18 Mice"
## [2] "A Cellular Anatomy of the Normal Adult Human Prostate and Prostatic Urethra"
## [3] "A Cellular Atlas of Pitx2-Dependent Cardiac Development."
## [4] "A Human Liver Cell Atlas reveals Heterogeneity and Epithelial Progenitors"
## [5] "A Partial Picture of the Single-Cell Transcriptomics of Human IgA Nephropathy"
page_2_lol <-
page_1_lol |>
hca_next()
page_2_lol |>
lol_pull("hits[*].projects[*].projectTitle")
## [1] "A Protocol for Revealing Oral Neutrophil Heterogeneity by Single-Cell Immune Profiling in Human Saliva"
## [2] "A Single-Cell Atlas of the Human Healthy Airways"
## [3] "A Single-Cell Characterization of Human Post-implantation Embryos Cultured In Vitro Delineates Morphogenesis in Primary Syncytialization"
## [4] "A Single-Cell Transcriptome Atlas of Glia Diversity in the Human Hippocampus across the Lifespan and in Alzheimer’s Disease"
## [5] "A Single-Cell Transcriptome Atlas of the Human Pancreas."
## should be identical to page_1_lol
page_2_lol |>
hca_prev() |>
lol_pull("hits[*].projects[*].projectTitle")
## [1] "1.3 Million Brain Cells from E18 Mice"
## [2] "A Cellular Anatomy of the Normal Adult Human Prostate and Prostatic Urethra"
## [3] "A Cellular Atlas of Pitx2-Dependent Cardiac Development."
## [4] "A Human Liver Cell Atlas reveals Heterogeneity and Epithelial Progenitors"
## [5] "A Partial Picture of the Single-Cell Transcriptomics of Human IgA Nephropathy"Much like projects() and files(), samples() and bundles() allow you to
provide a filter object and additional criteria to retrieve data in the
form of samples and bundles respectively
heart_filters <- filters(organ = list(is = "heart"))
heart_samples <- samples(filters = heart_filters, size = 4)
heart_samples
## # A tibble: 4 × 6
## entryId projectTitle genusSpecies disease format count
## <chr> <chr> <chr> <chr> <list> <lis>
## 1 012c52ff-4770-4c0c-8c2e-c348da… A Cellular … Mus musculus normal <chr> <int>
## 2 035db5b9-a219-4df8-bfc9-117cd0… A Cellular … Mus musculus normal <chr> <int>
## 3 09e425f7-22d7-487e-b78b-78b449… A Cellular … Mus musculus normal <chr> <int>
## 4 2273e44d-9fbc-4c13-8cb3-3caf8a… A Cellular … Mus musculus normal <chr> <int>
heart_bundles <- bundles(filters = heart_filters, size = 4)
heart_bundles
## # A tibble: 4 × 6
## projectTitle genusSpecies samples files bundleUuid bundleVersion
## <chr> <chr> <list> <lis> <chr> <chr>
## 1 A Cellular Atlas of Pitx2… Mus musculus <chr> <chr> 0d391bd1-… 2021-02-26T0…
## 2 A Cellular Atlas of Pitx2… Mus musculus <chr> <chr> 165a2df1-… 2021-02-26T0…
## 3 A Cellular Atlas of Pitx2… Mus musculus <chr> <chr> 166c1b1a-… 2023-07-19T1…
## 4 A Cellular Atlas of Pitx2… Mus musculus <chr> <chr> 18bad6b1-… 2021-02-26T0…HCA experiments are organized into catalogs, each of which can be summarized
with the hca::summary() function
heart_filters <- filters(organ = list(is = "heart"))
hca::summary(filters = heart_filters, type = "fileTypeSummaries")
## # A tibble: 34 × 3
## format count totalSize
## <chr> <int> <dbl>
## 1 fastq.gz 30365 3.09e13
## 2 fastq 316 6.53e11
## 3 tsv.gz 273 1.39e11
## 4 png 270 8.96e 6
## 5 h5 180 1.75e10
## 6 loom 169 3.56e11
## 7 bam 164 3.28e12
## 8 zip 148 9.46e 9
## 9 mtx.gz 98 1.91e10
## 10 csv 89 1.15e 8
## # ℹ 24 more rows
first_catalog <- catalogs()[1]
hca::summary(type = "overview", catalog = first_catalog)
## # A tibble: 7 × 2
## name value
## <chr> <dbl>
## 1 projectCount 4.7 e 2
## 2 specimenCount 2.24e 4
## 3 speciesCount 3 e 0
## 4 fileCount 5.24e 5
## 5 totalFileSize 3.26e14
## 6 donorCount 9.12e 3
## 7 labCount 7.88e 2Each project, file, sample, and bundles has its own unique ID by which, in conjunction with its catalog, can be to uniquely identify them.
heart_filters <- filters(organ = list(is = "heart"))
heart_projects <- projects(filters = heart_filters, size = 4)
heart_projects
## # A tibble: 4 × 14
## projectId projectTitle genusSpecies sampleEntityType specimenOrgan
## <chr> <chr> <chr> <list> <list>
## 1 7027adc6-c9c9-46f3-8… A Cellular … Mus musculus <chr [1]> <chr [1]>
## 2 a9301beb-e9fa-42fe-b… A human cel… Homo sapiens <chr [1]> <chr [14]>
## 3 902dc043-7091-445c-9… A human cel… Homo sapiens <chr [1]> <chr [1]>
## 4 2fe3c60b-ac1a-4c61-9… A human fet… Homo sapiens <chr [2]> <chr [2]>
## # ℹ 9 more variables: specimenOrganPart <list>, selectedCellType <lgl>,
## # libraryConstructionApproach <list>, nucleicAcidSource <list>,
## # pairedEnd <lgl>, workflow <list>, specimenDisease <chr>,
## # donorDisease <chr>, developmentStage <list>
projectId <-
heart_projects |>
filter(
startsWith(
projectTitle,
"Cells of the adult human"
)
) |>
dplyr::pull(projectId)
result <- projects_detail(uuid = projectId)The result is a list containing three elements representing
information for navigating next or previous (alphabetical, by default)
(pagination) project, the filters (termFacets) available, and
details of the project (hits).
names(result)
## [1] "pagination" "termFacets" "hits"As mentioned above, the hits are a complicated list-of-lists
structure. A very convenient way to explore this structure visually
is with listview::jsonedit(result). Selecting individual elements is
possible using the lol interface; an alternative is
cellxgenedp::jmespath().
lol(result)
## # class: lol
## # number of distinct paths: 687
## # total number of elements: 47708
## # number of leaf paths: 405
## # number of leaf elements: 31612
## # lol_path():
## # A tibble: 687 × 3
## path n is_leaf
## <chr> <int> <lgl>
## 1 hits 1 FALSE
## 2 hits[*] 10 FALSE
## 3 hits[*].cellLines 10 FALSE
## 4 hits[*].cellSuspensions 10 FALSE
## 5 hits[*].cellSuspensions[*] 12 FALSE
## 6 hits[*].cellSuspensions[*].organ 12 FALSE
## 7 hits[*].cellSuspensions[*].organPart 12 FALSE
## 8 hits[*].cellSuspensions[*].organPart[*] 14 TRUE
## 9 hits[*].cellSuspensions[*].organ[*] 12 TRUE
## 10 hits[*].cellSuspensions[*].selectedCellType 12 FALSE
## # ℹ 677 more rowsSee the accompanying “Human Cell Atlas Manifests” vignette on details
pertaining to the use of the manifest endpoint and further
annotation of .loom files.
sessionInfo()
## R Under development (unstable) (2024-10-21 r87258)
## 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
##
## 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] httr_1.4.7 hca_1.15.0
## [3] LoomExperiment_1.25.0 BiocIO_1.17.0
## [5] rhdf5_2.51.0 SingleCellExperiment_1.29.0
## [7] SummarizedExperiment_1.37.0 Biobase_2.67.0
## [9] GenomicRanges_1.59.0 GenomeInfoDb_1.43.0
## [11] IRanges_2.41.0 S4Vectors_0.45.0
## [13] BiocGenerics_0.53.0 MatrixGenerics_1.19.0
## [15] matrixStats_1.4.1 dplyr_1.1.4
## [17] BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 blob_1.2.4 filelock_1.0.3
## [4] fastmap_1.2.0 BiocFileCache_2.15.0 promises_1.3.0
## [7] digest_0.6.37 mime_0.12 lifecycle_1.0.4
## [10] RSQLite_2.3.7 magrittr_2.0.3 compiler_4.5.0
## [13] rlang_1.1.4 sass_0.4.9 tools_4.5.0
## [16] utf8_1.2.4 yaml_2.3.10 knitr_1.48
## [19] S4Arrays_1.7.0 htmlwidgets_1.6.4 bit_4.5.0
## [22] curl_5.2.3 DelayedArray_0.33.0 abind_1.4-8
## [25] miniUI_0.1.1.1 HDF5Array_1.35.0 withr_3.0.2
## [28] purrr_1.0.2 grid_4.5.0 fansi_1.0.6
## [31] xtable_1.8-4 Rhdf5lib_1.29.0 cli_3.6.3
## [34] rmarkdown_2.28 crayon_1.5.3 generics_0.1.3
## [37] tzdb_0.4.0 DBI_1.2.3 cachem_1.1.0
## [40] stringr_1.5.1 zlibbioc_1.53.0 parallel_4.5.0
## [43] BiocManager_1.30.25 XVector_0.47.0 vctrs_0.6.5
## [46] Matrix_1.7-1 jsonlite_1.8.9 bookdown_0.41
## [49] hms_1.1.3 bit64_4.5.2 archive_1.1.9
## [52] jquerylib_0.1.4 tidyr_1.3.1 glue_1.8.0
## [55] DT_0.33 stringi_1.8.4 later_1.3.2
## [58] UCSC.utils_1.3.0 tibble_3.2.1 pillar_1.9.0
## [61] htmltools_0.5.8.1 rhdf5filters_1.19.0 GenomeInfoDbData_1.2.13
## [64] R6_2.5.1 dbplyr_2.5.0 vroom_1.6.5
## [67] evaluate_1.0.1 shiny_1.9.1 lattice_0.22-6
## [70] readr_2.1.5 memoise_2.0.1 httpuv_1.6.15
## [73] bslib_0.8.0 Rcpp_1.0.13 SparseArray_1.7.0
## [76] xfun_0.48 pkgconfig_2.0.3