The HCABrowser Package

opts_chunk$set(warning=FALSE, message=FALSE)

What is the Human Cell Atlas?

From the Human Cell Atlas (HCA) website:

The cell is the core unit of the human body—the key to understanding the biology of health and the ways in which molecular dysfunction leads to disease. Yet our characterization of the hundreds of types and subtypes of cells in the human body is limited, based partly on techniques that have limited resolution and classifications that do not always map neatly to each other. Genomics has offered a systematic approach, but it has largely been applied in bulk to many cell types at once—masking critical differences between cells—and in isolation from other valuable sources of data.

Recent advances in single-cell genomic analysis of cells and tissues have put systematic, high-resolution and comprehensive reference maps of all human cells within reach. In other words, we can now realistically envision a human cell atlas to serve as a basis for both understanding human health and diagnosing, monitoring, and treating disease.

At its core, a cell atlas would be a collection of cellular reference maps, characterizing each of the thousands of cell types in the human body and where they are found. It would be an extremely valuable resource to empower the global research community to systematically study the biological changes associated with different diseases, understand where genes associated with disease are active in our bodies, analyze the molecular mechanisms that govern the production and activity of different cell types, and sort out how different cell types combine and work together to form tissues.

The Human Cell Atlas facilitates queries on it's [data coordination platform with a RESTFUL API] (


To install this package, use Bioconductor's BiocManager package.

if (!require("BiocManager"))

Connecting to the Human Cell Atlas

The r Biocpkg("HCABrowser") package relies on having network connectivety. Also, the HCA's Data Coordination Platform (DCP) must also be operational. This package is meant for users who have a working knowledge of the HCA DCP's schema to provide the user with the fill richness of the HCA's content. For a more simple-to-use way to access and download data from The Human Cell Atlas, please use the HCAExplorer package.

The HCABrowser object serves as the representation of the Human Cell Atlas. Upon creation, it will automatically peform a cursorary query and display a small table showing the first few bundles of the entire HCA. This intial table contains some columns that we have determined are most useful to users. The output also displays the url of the instance of the HCA DCP being used, the current query, whether bundles or files are being displayed, and the number of bundles in the results

By default, ten bundles per page will be displayed in the result for "raw" output and 100 will be displayed for "summary" output. The host argument dictates the hca-dcp the client will be accessing while the api_url argument dictates what schema the client will be accessing. These three values can be changed in the constructor. The default values are given below.

If the HCA cannot be reached, an error will be thrown displaying the status of the request.

hca <- HCABrowser(api_url='',
                  host = '')

Upon displaying the object, multiple fields can be seen: - The class: HCABrowser - The hca dcp host address that is currently being used. - The current query (assigned using filter()) - The current selection (assigned using select()) - You may notice that some columns are already selected. These columns are automatically selected to allow the user some initial view of the hca.

To change how many pages are being displayed, the per_page() method can be used. (Note the hca dcp had a maximum of 10 bundles per page to be shown at a time)

Since there are far more bundles in the HCA than can be shown, if link is True, the next set of bundles can be obtained using the nextResults method.

hca <- nextResults(hca)

Querying the HCABrowser

The HCA extends the functionality of the r CRANpkg("dplyr") package's filter() and select() methods.

The filter() method allows the user to query the HCA by relating fields to certain values. Character fields can be queried using the operators: - == - != - %in% - %startsWith% - %endsWith% - %contains%

Numeric fields can be queried with the operators: - == - != - %in% - > - < - >= - <=

Queries can be encompassed by parenthesese - ()

Queries can be negated by placing the ! symbol in front

Combination operators can be used to combine queries - & - |

Now we see that "brain" and "Brain" are available values. Since these values are the result of input by other users, there may be errors or inconsistencies. To be safe, both fields can be queried with the following query:

hca2 <- hca %>% filter('files.specimen_from_organism_json.organ.text' == c('Brain', 'brain'))
hca2 <- hca %>% filter('files.specimen_from_organism_json.organ.text' %in% c('Brain', 'brain'))
hca2 <- hca %>% filter('files.specimen_from_organism_json.organ.text' == Brain | 'files.specimen_from_organism_json.organ.text' == brain)

If we also wish to search for results based on the NCBI Taxon ID for mouse, 10090, as well as brain, we can perform this query in a variety of ways.

hca2 <- hca %>% filter('files.specimen_from_organism_json.organ.text' %in% c('Brain', 'brain')) %>%
                filter('specimen_from_organism_json.biomaterial_core.ncbi_taxon_id' == 10090)
hca2 <- hca %>% filter('files.specimen_from_organism_json.organ.text' %in% c('Brain', 'brain'),
                       'specimen_from_organism_json.biomaterial_core.ncbi_taxon_id' == 10090)
hca <- hca %>% filter('files.specimen_from_organism_json.organ.text' %in% c('Brain', 'brain') &
                      'specimen_from_organism_json.biomaterial_core.ncbi_taxon_id' == 10090)

The HCABrowser package is able to handle arbitrarily complex queries on the Human Cell Atlas.

hca2 <- hca %>% filter((!organ.text %in% c('Brain', 'blood')) & 
                       (files.specimen_from_organism_json.genus_species.text == "Homo sapiens" |
                        library_preparation_protocol_json.library_construction_approach.text == 'Smart-seq2')

The HCABrowser object can undo the most recent queries run on it.

hca <- hca %>% filter('files.specimen_from_organism_json.organ.text' == heart)
hca <- hca %>% filter('files.specimen_from_organism_json.organ.text' != brain)
hca <- undoEsQuery(hca, n = 2)

If one would want to start from a fresh query but retain the modifications made to the HCABrowser object, the resetEsQuery() method can be used.

hca <- resetEsQuery(hca)

Using fields(), we can find that the fields paired_end and organ.ontology are availiable. These fields can be shown in our resulting HCABrowser object using the select() method.

hca2 <- hca %>% select('paired_end', 'organ.ontology')
hca2 <- hca %>% select(c('paired_end', 'organ.ontology'))

Use examples

The remainder of this vignette will review applicable uses for this package.

Download a Bundle

hca <- HCABrowser()

hca <- hca %>% filter('files.specimen_from_organism_json.organ.text' == "brain")
result <- searchBundles(hca, replica='aws', output_format='raw')
result <- parseToSearchResults(result)

bundles <- lapply(results(result), function(results) {
    uuid <- stringr::str_split(results[["bundle_fqid"]], '\\.')[[1]][[1]]
    getBundle(hca, uuid=uuid, replica='aws')


Download a File

hca <- HCABrowser()

hca <- hca %>% filter('files.specimen_from_organism_json.organ.text' == "brain")
result <- searchBundles(hca, replica='aws', output_format='raw')
result <- parseToSearchResults(result)

bundles <- lapply(results(result), function(results) {
    uuid <- stringr::str_split(results[["bundle_fqid"]], '\\.')[[1]][[1]]

    bundle <- c()

    checkout_id <- httr::content(checkoutBundle(hca, uuid=uuid, replica='aws'))$checkout_job_id

    bundle_checkout_status <- httr::content(getBundleCheckout(hca, checkout_job_id=checkout_id, replica='aws'))$status

    bundle <- getBundle(uuid=uuid, replica='aws')
    }, error = function(e) {
        message('Missing checkout request')
    }, finally = {

files <- lapply(bundles, function(bundle) {
    uuid <- bundle[['bundle']]['uuid']
    getFile(hca, uuid=uuid, replica='aws')


Download an Expression Matrix

In addition to the method shown below, the HCAMatrixBrowser package can be used itself to download expression matrix data.

# Add to filter (functions like json)
hca <- hca %>% filter('files.project_json.project_core.project_title' == 'Census of Immume Cells')

# Use ropenapi to access search function
ret <- searchBundles(hca, getEsQuery(hca), replica='aws', output_format='raw')

# Prepare SearchResult object
ret <- parseToSearchResults(ret)
res <- results(ret)

# Process SearchResult object, fetch file, save results file to BiocFileCache
for(bundles in res) {
    file <- bundles[['metadata']][['manifest']][['files']]
    if (endsWith('results', file)) {
        file <- searchBundles(hca, uuid=file, replica='aws')

Download all fastq files in a project

Developer notes

Try the HCABrowser package in your browser

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HCABrowser documentation built on Nov. 8, 2020, 7:24 p.m.