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"))
install.packages("BiocManager")
BiocManager::install('HCABrowser')
library(HCABrowser)
The HCABrowser package relies on having network connectivety. Also, the HCA's Data Coordination Platform (DCP) must also be operational.
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 and the default url to the HCA DCP will be used. Currently, the maximum number of bundles that can be shown per query is ten. These two values can be changed in the constructor.
If the HCA cannot be reached, an error will be thrown displaying the status of the request.
hca <- HCABrowser(url = 'https://dss.data.humancellatlas.org/v1', per_page = 10)
hca
## class: HCABrowser
## Using hca-dcp at:
## https://dss.data.humancellatlas.org/v1
##
## Current Query:
##
## Current Selection:
##
## class: SearchResult
## bundle 1 - 10 of 702516
## link: TRUE
##
## Showing bundles with 10 results per page
## # A tibble: 10 x 5
## project_json.pr… project_json.pr… specimen_from_o… bundle_fqid
## <chr> <chr> <chr> <fct>
## 1 Tabula Muris Tabula Muris: T… bladder ffffba2d-3…
## 2 Tabula Muris Tabula Muris: T… lung epcam ffffaf55-f…
## 3 <NA> <NA> <NA> ffffab51-b…
## 4 <NA> <NA> <NA> ffffa79b-9…
## 5 <NA> <NA> <NA> ffffa79b-9…
## 6 <NA> <NA> <NA> ffffa79b-9…
## 7 <NA> <NA> <NA> ffffa79b-9…
## 8 <NA> <NA> <NA> ffffa79b-9…
## 9 Tabula Muris Tabula Muris: T… thymus fffee4fc-7…
## 10 <NA> <NA> <NA> fffeddc6-a…
## # … with 1 more variable: bundle_url <fct>
Upon displaying the object, multiple fields can be seen:
HCABrowserfilter())select())
link to more results is availiabletibble of the queryThe results tibble can be obtained using the results() method.
results(hca)
## # A tibble: 10 x 5
## project_json.pr… project_json.pr… specimen_from_o… bundle_fqid
## <chr> <chr> <chr> <fct>
## 1 Tabula Muris Tabula Muris: T… bladder ffffba2d-3…
## 2 Tabula Muris Tabula Muris: T… lung epcam ffffaf55-f…
## 3 <NA> <NA> <NA> ffffab51-b…
## 4 <NA> <NA> <NA> ffffa79b-9…
## 5 <NA> <NA> <NA> ffffa79b-9…
## 6 <NA> <NA> <NA> ffffa79b-9…
## 7 <NA> <NA> <NA> ffffa79b-9…
## 8 <NA> <NA> <NA> ffffa79b-9…
## 9 Tabula Muris Tabula Muris: T… thymus fffee4fc-7…
## 10 <NA> <NA> <NA> fffeddc6-a…
## # … with 1 more variable: bundle_url <fct>
To toggle whether bundles or files are being displayed in the tibble, the
activate() method can be used to choose which to display.
## Bundles are diaplyed be default
nrow(results(hca))
## [1] 10
## The HCABrowser object is activated here by 'files'
hca <- hca %>% activate('files')
hca
## class: HCABrowser
## Using hca-dcp at:
## https://dss.data.humancellatlas.org/v1
##
## Current Query:
##
## Current Selection:
##
## class: SearchResult
## bundle 1 - 10 of 702516
## link: TRUE
##
## Showing files with 10 results per page
## # A tibble: 10 x 5
## project_json.pr… project_json.pr… specimen_from_o… bundle_fqid
## <chr> <chr> <chr> <fct>
## 1 Tabula Muris Tabula Muris: T… bladder ffffba2d-3…
## 2 Tabula Muris Tabula Muris: T… lung epcam ffffaf55-f…
## 3 <NA> <NA> <NA> ffffab51-b…
## 4 <NA> <NA> <NA> ffffa79b-9…
## 5 <NA> <NA> <NA> ffffa79b-9…
## 6 <NA> <NA> <NA> ffffa79b-9…
## 7 <NA> <NA> <NA> ffffa79b-9…
## 8 <NA> <NA> <NA> ffffa79b-9…
## 9 Tabula Muris Tabula Muris: T… thymus fffee4fc-7…
## 10 <NA> <NA> <NA> fffeddc6-a…
## # … with 1 more variable: bundle_url <fct>
nrow(results(hca))
## [1] 10
## Revert back to showing bundles with 'bundles'
hca <- hca %>% activate('bundles')
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)
#hca2 <- hca %>% per_page(n = 5)
#hca2
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)
hca
## class: HCABrowser
## Using hca-dcp at:
## https://dss.data.humancellatlas.org/v1
##
## Current Query:
##
## Current Selection:
##
## class: SearchResult
## bundle 11 - 20 of 702516
## link: TRUE
##
## Showing bundles with 10 results per page
## # A tibble: 10 x 5
## bundle_fqid bundle_url project_json.pr… project_json.pr…
## <fct> <fct> <chr> <chr>
## 1 fffeddc6-a… https://d… <NA> <NA>
## 2 fffeddc6-a… https://d… <NA> <NA>
## 3 fffeddc6-a… https://d… <NA> <NA>
## 4 fffeddc6-a… https://d… <NA> <NA>
## 5 fffe6080-3… https://d… <NA> <NA>
## 6 fffe6080-3… https://d… <NA> <NA>
## 7 fffe6080-3… https://d… <NA> <NA>
## 8 fffe6080-3… https://d… <NA> <NA>
## 9 fffe6080-3… https://d… <NA> <NA>
## 10 fffe55c1-1… https://d… Fetal/Maternal … Reconstructing …
## # … with 1 more variable: specimen_from_organism_json.organ.text <chr>
To show which fields are available to query, use the supportedFilters() method.
hca <- HCABrowser()
hca %>% fields
## # A tibble: 394 x 2
## abbreviated_names field_names
## <chr> <chr>
## 1 address files.project_json.contributors.add…
## 2 alcohol_history files.donor_organism_json.medical_h…
## 3 analysis_file_json.describedBy files.analysis_file_json.describedBy
## 4 analysis_file_json.file_core.file_… files.analysis_file_json.file_core.…
## 5 analysis_file_json.schema_type files.analysis_file_json.schema_type
## 6 analysis_process_json.describedBy files.analysis_process_json.describ…
## 7 analysis_process_json.schema_type files.analysis_process_json.schema_…
## 8 analysis_protocol_json.describedBy files.analysis_protocol_json.descri…
## 9 analysis_protocol_json.protocol_co… files.analysis_protocol_json.protoc…
## 10 analysis_protocol_json.schema_type files.analysis_protocol_json.schema…
## # … with 384 more rows
The abbreviated_fields column indicates the shortest possible name that can be
used to reference the field. The field_names columns shows the whole schema
name on the json schema.
Availiable values to these fields can be found using the values() method.
If the parameter fields is specified, it will display values related to those
field. If not specified, the values for all fields will be shown.
hca %>% values(c('organ.text', 'library_construction_approach.text'))
## # A tibble: 23 x 2
## field_names value
## <fct> <fct>
## 1 files.specimen_from_organism_json.organ.text pancreas
## 2 files.specimen_from_organism_json.organ.text tumor
## 3 files.specimen_from_organism_json.organ.text embryo
## 4 files.specimen_from_organism_json.organ.text lymph node
## 5 files.specimen_from_organism_json.organ.text Brain
## 6 files.specimen_from_organism_json.organ.text immune system
## 7 files.specimen_from_organism_json.organ.text blood
## 8 files.specimen_from_organism_json.organ.text skin
## 9 files.specimen_from_organism_json.organ.text skin of body
## 10 files.specimen_from_organism_json.organ.text spleen
## # … with 13 more rows
The HCA extends the functionality of the 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
&|As an example, in order to find HCA resources associated with the brain. It can
the be seen by looking at the result of the fields method, that
organ.text can be used to reference files.specimen_from_organism.organ.text.
Running organ.text through values, we see the following:
hca %>% values('organ.text')
## # A tibble: 15 x 2
## field_names value
## <fct> <fct>
## 1 files.specimen_from_organism_json.organ.text pancreas
## 2 files.specimen_from_organism_json.organ.text tumor
## 3 files.specimen_from_organism_json.organ.text embryo
## 4 files.specimen_from_organism_json.organ.text lymph node
## 5 files.specimen_from_organism_json.organ.text Brain
## 6 files.specimen_from_organism_json.organ.text immune system
## 7 files.specimen_from_organism_json.organ.text blood
## 8 files.specimen_from_organism_json.organ.text skin
## 9 files.specimen_from_organism_json.organ.text skin of body
## 10 files.specimen_from_organism_json.organ.text spleen
## 11 files.specimen_from_organism_json.organ.text oesophagus
## 12 files.specimen_from_organism_json.organ.text brain
## 13 files.specimen_from_organism_json.organ.text kidney
## 14 files.specimen_from_organism_json.organ.text "bone "
## 15 files.specimen_from_organism_json.organ.text bone
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(organ.text == c('Brain', 'brain'))
hca2 <- hca %>% filter(organ.text %in% c('Brain', 'brain'))
hca2 <- hca %>% filter(organ.text == Brain | organ.text == brain)
hca2
## class: HCABrowser
## Using hca-dcp at:
## https://dss.data.humancellatlas.org/v1
##
## Current Query:
## organ.text == Brain | organ.text == brain
##
## Current Selection:
##
## class: SearchResult
## bundle 1 - 10 of 27291
## link: TRUE
##
## Showing bundles with 10 results per page
## # A tibble: 10 x 5
## project_json.pr… project_json.pr… specimen_from_o… bundle_fqid
## <chr> <chr> <chr> <fct>
## 1 1M Neurons 1.3 Million Bra… Brain fffcea5e-2…
## 2 Tabula Muris Tabula Muris: T… brain fffcc997-3…
## 3 Tabula Muris Tabula Muris: T… brain fffb302b-1…
## 4 Tabula Muris Tabula Muris: T… brain fff898a9-b…
## 5 Tabula Muris Tabula Muris: T… brain fff57cf8-5…
## 6 Tabula Muris Tabula Muris: T… brain fff5329f-b…
## 7 Tabula Muris Tabula Muris: T… brain ffee0443-1…
## 8 Tabula Muris Tabula Muris: T… brain ffe7dee0-2…
## 9 Tabula Muris Tabula Muris: T… brain ffe61686-a…
## 10 Tabula Muris Tabula Muris: T… brain ffe1d3d3-d…
## # … with 1 more variable: bundle_url <fct>
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(organ.text %in% c('Brain', 'brain')) %>%
filter('specimen_from_organism_json.biomaterial_core.ncbi_taxon_id' == 10090)
hca2 <- hca %>% filter(organ.text %in% c('Brain', 'brain'),
'specimen_from_organism_json.biomaterial_core.ncbi_taxon_id' == 10090)
hca <- hca %>% filter(organ.text %in% c('Brain', 'brain') &
'specimen_from_organism_json.biomaterial_core.ncbi_taxon_id' == 10090)
hca
## class: HCABrowser
## Using hca-dcp at:
## https://dss.data.humancellatlas.org/v1
##
## Current Query:
## organ.text %in% c("Brain", "brain") & "specimen_from_organism_json.biomaterial_core.ncbi_taxon_id" == 10090
##
## Current Selection:
##
## class: SearchResult
## bundle 1 - 10 of 27291
## link: TRUE
##
## Showing bundles with 10 results per page
## # A tibble: 10 x 6
## project_json.pr… project_json.pr… specimen_from_o… specimen_from_o…
## <chr> <chr> <chr> <chr>
## 1 1M Neurons 1.3 Million Bra… 10090 Brain
## 2 Tabula Muris Tabula Muris: T… 10090 brain
## 3 Tabula Muris Tabula Muris: T… 10090 brain
## 4 Tabula Muris Tabula Muris: T… 10090 brain
## 5 Tabula Muris Tabula Muris: T… 10090 brain
## 6 Tabula Muris Tabula Muris: T… 10090 brain
## 7 Tabula Muris Tabula Muris: T… 10090 brain
## 8 Tabula Muris Tabula Muris: T… 10090 brain
## 9 Tabula Muris Tabula Muris: T… 10090 brain
## 10 Tabula Muris Tabula Muris: T… 10090 brain
## # … with 2 more variables: bundle_fqid <fct>, bundle_url <fct>
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')
)
hca2
## class: HCABrowser
## Using hca-dcp at:
## https://dss.data.humancellatlas.org/v1
##
## Current Query:
## organ.text %in% c("Brain", "brain") & "specimen_from_organism_json.biomaterial_core.ncbi_taxon_id" == 10090
## (!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")
##
## Current Selection:
##
## class: SearchResult
## bundle 0 - 0 of 0
## link: FALSE
##
## Showing bundles with 10 results per page
## # A tibble: 0 x 0
The HCABrowser object can undo the most recent queries run on it.
hca <- hca %>% filter(organ.text == heart)
hca <- hca %>% filter(organ.text != brain)
hca <- hca %>% undoEsQuery(n = 2)
hca
## class: HCABrowser
## Using hca-dcp at:
## https://dss.data.humancellatlas.org/v1
##
## Current Query:
## organ.text %in% c("Brain", "brain") & "specimen_from_organism_json.biomaterial_core.ncbi_taxon_id" == 10090
##
## Current Selection:
##
## class: SearchResult
## bundle 1 - 10 of 27291
## link: TRUE
##
## Showing bundles with 10 results per page
## # A tibble: 10 x 6
## project_json.pr… project_json.pr… specimen_from_o… specimen_from_o…
## <chr> <chr> <chr> <chr>
## 1 1M Neurons 1.3 Million Bra… 10090 Brain
## 2 Tabula Muris Tabula Muris: T… 10090 brain
## 3 Tabula Muris Tabula Muris: T… 10090 brain
## 4 Tabula Muris Tabula Muris: T… 10090 brain
## 5 Tabula Muris Tabula Muris: T… 10090 brain
## 6 Tabula Muris Tabula Muris: T… 10090 brain
## 7 Tabula Muris Tabula Muris: T… 10090 brain
## 8 Tabula Muris Tabula Muris: T… 10090 brain
## 9 Tabula Muris Tabula Muris: T… 10090 brain
## 10 Tabula Muris Tabula Muris: T… 10090 brain
## # … with 2 more variables: bundle_fqid <fct>, bundle_url <fct>
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 <- 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(paired_end, organ.ontology)
hca2 <- hca %>% select(c('paired_end', 'organ.ontology'))
hca2
## class: HCABrowser
## Using hca-dcp at:
## https://dss.data.humancellatlas.org/v1
##
## Current Query:
## organ.text %in% c("Brain", "brain") & "specimen_from_organism_json.biomaterial_core.ncbi_taxon_id" == 10090
##
## Current Selection:
##
## class: SearchResult
## bundle 1 - 10 of 27291
## link: TRUE
##
## Showing bundles with 10 results per page
## # A tibble: 10 x 8
## project_json.pr… project_json.pr… sequencing_prot… specimen_from_o…
## <chr> <chr> <lgl> <chr>
## 1 1M Neurons 1.3 Million Bra… FALSE 10090
## 2 Tabula Muris Tabula Muris: T… TRUE 10090
## 3 Tabula Muris Tabula Muris: T… TRUE 10090
## 4 Tabula Muris Tabula Muris: T… TRUE 10090
## 5 Tabula Muris Tabula Muris: T… TRUE 10090
## 6 Tabula Muris Tabula Muris: T… TRUE 10090
## 7 Tabula Muris Tabula Muris: T… TRUE 10090
## 8 Tabula Muris Tabula Muris: T… TRUE 10090
## 9 Tabula Muris Tabula Muris: T… TRUE 10090
## 10 Tabula Muris Tabula Muris: T… TRUE 10090
## # … with 4 more variables:
## # specimen_from_organism_json.organ.ontology <chr>,
## # specimen_from_organism_json.organ.text <chr>, bundle_fqid <fct>,
## # bundle_url <fct>
Finally, instead of a using the filter and select methods, one can query
the Human Cell Atlas by using a json query and the postSearch() method.
Note that unabbreviated names must be used for these kinds of queries.
Once the user is satisfied with their query, the results() method can be
used to create a tibble of the first n bundles in the search.
Note that since the maximum page size is 10, the method will need to make
multiple requests to the hca dcp. This may take some time for large requests.
res <- hca %>% results(n = 36)
res
## # A tibble: 36 x 6
## project_json.pr… project_json.pr… specimen_from_o… specimen_from_o…
## <chr> <chr> <chr> <chr>
## 1 1M Neurons 1.3 Million Bra… 10090 Brain
## 2 Tabula Muris Tabula Muris: T… 10090 brain
## 3 Tabula Muris Tabula Muris: T… 10090 brain
## 4 Tabula Muris Tabula Muris: T… 10090 brain
## 5 Tabula Muris Tabula Muris: T… 10090 brain
## 6 Tabula Muris Tabula Muris: T… 10090 brain
## 7 Tabula Muris Tabula Muris: T… 10090 brain
## 8 Tabula Muris Tabula Muris: T… 10090 brain
## 9 Tabula Muris Tabula Muris: T… 10090 brain
## 10 Tabula Muris Tabula Muris: T… 10090 brain
## # … with 26 more rows, and 2 more variables: bundle_fqid <fct>,
## # bundle_url <fct>
One can also simply pull bundles from the HCABrowser object's results.
bundle_fqids <- hca %>% pullBundles(n = 1)
bundle_fqids
## [1] "fffcea5e-2e6c-4ca1-9aa9-c23b90b2e8b8.2019-05-16T211813.059000Z"
One can query using just the bundle fqids by using the showBundles method.
hca <- hca %>% showBundles(bundle_fqids = bundle_fqids)
hca
## class: HCABrowser
## Using hca-dcp at:
## https://dss.data.humancellatlas.org/v1
##
## Current Query:
## organ.text %in% c("Brain", "brain") & "specimen_from_organism_json.biomaterial_core.ncbi_taxon_id" == 10090
## "uuid" %in% bundle_fqids
##
## Current Selection:
##
## class: SearchResult
## bundle 1 - 1 of 1
## link: FALSE
##
## Showing bundles with 10 results per page
## # A tibble: 1 x 6
## project_json.pr… project_json.pr… specimen_from_o… specimen_from_o…
## <chr> <chr> <chr> <chr>
## 1 1M Neurons 1.3 Million Bra… 10090 Brain
## # … with 2 more variables: bundle_fqid <fct>, bundle_url <fct>
sessionInfo()
## R version 3.6.1 (2019-07-05)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.10-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.10-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 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
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] HCABrowser_1.2.0 dplyr_0.8.3 BiocStyle_2.14.0 knitr_1.25
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.2 plyr_1.8.4 googleAuthR_1.1.1
## [4] pillar_1.4.2 compiler_3.6.1 BiocManager_1.30.9
## [7] dbplyr_1.4.2 tools_3.6.1 zeallot_0.1.0
## [10] digest_0.6.22 bit_1.1-14 lifecycle_0.1.0
## [13] jsonlite_1.6 gargle_0.4.0 BiocFileCache_1.10.0
## [16] RSQLite_2.1.2 evaluate_0.14 memoise_1.1.0
## [19] tibble_2.1.3 pkgconfig_2.0.3 rlang_0.4.1
## [22] igraph_1.2.4.1 tidygraph_1.1.2 cli_1.1.0
## [25] DBI_1.0.0 parallel_3.6.1 curl_4.2
## [28] yaml_2.2.0 xfun_0.10 stringr_1.4.0
## [31] httr_1.4.1 hms_0.5.1 fs_1.3.1
## [34] vctrs_0.2.0 S4Vectors_0.24.0 rappdirs_0.3.1
## [37] stats4_3.6.1 bit64_0.9-7 tidyselect_0.2.5
## [40] glue_1.3.1 R6_2.4.0 fansi_0.4.0
## [43] rmarkdown_1.16 tidyr_1.0.0 readr_1.3.1
## [46] purrr_0.3.3 blob_1.2.0 magrittr_1.5
## [49] BiocGenerics_0.32.0 backports_1.1.5 htmltools_0.4.0
## [52] assertthat_0.2.1 utf8_1.1.4 stringi_1.4.3
## [55] crayon_1.3.4
S3 object-oriented programming paradigm is used.dplyr package can be used to manipulate objects in the
HCABrowser package.