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('HCAExplorer')
library(HCAExplorer)
One of the primary tasks of the HCAExplorer is to obtain metadata files of projects and then pass them down to other pipelines to download useful information like expression matrices. To illustrate the functionality of the package, we will first embark on the task of obtaining expression matrices from a selection of projects. We will first, initiate an HCAExplorer object, then look at functions useful for navigating the HCAExplorer object, and finally we will download the manifest file and use it to obtain expression matrices as a LoomExperiment object.
The HCAExplorer package relies on having network connectivety. Also, the a link to a viable digest of the Human Cell Atlas must also be operational. The backend that we are using will be using is refered to as the “azul backend”. This package is meant to mirror the functionality of the HCA Data Explorer.
The HCAExplorer object serves as the representation of the Human Cell
Atlas. Upon creation, it will automatically perform a cursorary query and
display a small table showing the first few project 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 digest
being used, the current query, relevant information about the quantity of data
being displayed, and finally the table of projects.
By default, 15 entries per page will be displayed in the result and the default url to the HCA DCP will be used. These two values can be changed in the constructor or later on using methods.
If the HCA cannot be reached, an error will be thrown displaying the status of the request.
hca <- HCAExplorer(url = 'https://service.explore.data.humancellatlas.org', per_page = 15)
hca
## class: HCAExplorer
## Using azul backend at:
## https://service.explore.data.humancellatlas.org
##
## Donor count: 289
## Specimens: 780
## Estimated Cells: 4535231
## Files: 536961
## File Size: 30.2 Tb
##
## Showing projects with 15 results per page.# A tibble: 15 x 7
## projects.projec~ samples.sampleE~ samples.organ protocols.libra~
## <chr> <chr> <chr> <chr>
## 1 1.3 Million Bra~ specimens brain 10X v2 sequenci~
## 2 A Single-Cell T~ specimens pancreas inDrop, NULL
## 3 A single-cell m~ specimens embryo 10X 3' v1 seque~
## 4 A single-cell r~ specimens blood, hemat~ 10X v2 sequenci~
## 5 A single-cell t~ specimens eye 10X v2 sequenci~
## 6 Assessing the r~ organoids <NA> 10X v2 sequenci~
## 7 Bone marrow pla~ specimens hematopoieti~ MARS-seq, NULL
## 8 Cell hashing wi~ specimens blood CITE-seq, NULL
## 9 Census of Immun~ specimens blood, immun~ 10X v2 sequenci~
## 10 Comparison, cal~ cellLines <NA> DroNc-Seq, Drop~
## 11 Dissecting the ~ specimens liver 10X v2 sequenci~
## 12 Ischaemic sensi~ specimens esophagus, l~ 10X v2 sequenci~
## 13 Melanoma infilt~ specimens lymph node, ~ Smart-seq2, NULL
## 14 Precursors of h~ specimens blood Smart-seq2, NULL
## 15 Profiling of CD~ cellLines <NA> 10X v2 sequenci~
## # ... with 3 more variables: protocols.pairedEnd <chr>,
## # donorOrganisms.genusSpecies <chr>, samples.disease <chr>
## Showing page 1 of 2
Upon displaying the object, multiple fields can be seen:
HCAExplorerlink to more results is available.tibble of the query. (This table is abbreviated to show only
columns that we determined are most useful to the user.)The results tibble can be obtained using the results() method.
results(hca)
## # A tibble: 15 x 52
## protocols.libra~ protocols.instr~ protocols.paire~ protocols.workf~
## <chr> <chr> <chr> <chr>
## 1 10X v2 sequenci~ Illumina HiSeq ~ FALSE, NULL NULL
## 2 inDrop, NULL Illumina HiSeq ~ TRUE, NULL NULL
## 3 10X 3' v1 seque~ Illumina HiSeq ~ FALSE, NULL NULL
## 4 10X v2 sequenci~ Illumina HiSeq ~ FALSE, NULL optimus_v1.3.5,~
## 5 10X v2 sequenci~ Illumina HiSeq ~ FALSE, NULL optimus_v1.3.5,~
## 6 10X v2 sequenci~ Illumina HiSeq ~ TRUE, NULL optimus_v1.3.1,~
## 7 MARS-seq, NULL Illumina NextSe~ TRUE, NULL NULL
## 8 CITE-seq, NULL Illumina Hiseq ~ FALSE, NULL NULL
## 9 10X v2 sequenci~ Illumina HiSeq ~ FALSE, NULL optimus_v1.3.2,~
## 10 DroNc-Seq, Drop~ Illumina NextSe~ TRUE, NULL NULL
## 11 10X v2 sequenci~ Illumina Hiseq ~ FALSE, NULL optimus_v1.3.5,~
## 12 10X v2 sequenci~ Illumina HiSeq ~ FALSE, TRUE, NU~ optimus_v1.3.5,~
## 13 Smart-seq2, NULL Illumina HiSeq ~ TRUE, NULL NULL
## 14 Smart-seq2, NULL Illumina HiSeq ~ FALSE, NULL NULL
## 15 10X v2 sequenci~ Illumina HiSeq ~ FALSE, NULL optimus_v1.3.1,~
## # ... with 48 more variables: protocols.assayType <chr>, entryId <chr>,
## # projects.projectTitle <chr>, projects.projectShortname <chr>,
## # projects.laboratory <chr>, projects.arrayExpressAccessions <chr>,
## # projects.geoSeriesAccessions <chr>, projects.insdcProjectAccessions <chr>,
## # projects.insdcStudyAccessions <chr>, projects.supplementaryLinks <chr>,
## # samples.sampleEntityType <chr>, samples.effectiveOrgan <chr>,
## # samples.organ <chr>, samples.source <chr>, samples.disease <chr>,
## # samples.organPart <chr>, samples.id <chr>,
## # samples.preservationMethod <chr>, specimens.id <chr>,
## # specimens.organ <chr>, specimens.organPart <chr>, specimens.disease <chr>,
## # specimens.preservationMethod <chr>, specimens.source <chr>,
## # donorOrganisms.id <chr>, donorOrganisms.donorCount <chr>,
## # donorOrganisms.genusSpecies <chr>, donorOrganisms.organismAge <chr>,
## # donorOrganisms.organismAgeUnit <chr>,
## # donorOrganisms.organismAgeRange <chr>, donorOrganisms.biologicalSex <chr>,
## # donorOrganisms.disease <chr>, cellSuspensions.organ <chr>,
## # cellSuspensions.organPart <chr>, cellSuspensions.selectedCellType <chr>,
## # cellSuspensions.totalCells <chr>, fileTypeSummaries.fileType <chr>,
## # fileTypeSummaries.count <chr>, fileTypeSummaries.totalSize <chr>,
## # samples.modelOrgan <chr>, samples.modelOrganPart <chr>, cellLines.id <chr>,
## # cellLines.cellLineType <chr>, cellLines.modelOrgan <chr>,
## # organoids.id <chr>, organoids.modelOrgan <chr>,
## # organoids.modelOrganPart <chr>, samples.cellLineType <chr>
There are various columns that can be displayed in an HCAExplorer object. By
default, only a few columns are shown. We can change which columns are shown
by using select. For example, the following will only show
projects.projectTitle and samples.organ columns when the object as shown.
hca <- hca %>% select('projects.projectTitle', 'samples.organ')
hca
## class: HCAExplorer
## Using azul backend at:
## https://service.explore.data.humancellatlas.org
##
## Donor count: 289
## Specimens: 780
## Estimated Cells: 4535231
## Files: 536961
## File Size: 30.2 Tb
##
## Showing projects with 15 results per page.# A tibble: 15 x 2
## projects.projectTitle samples.organ
## <chr> <chr>
## 1 1.3 Million Brain Cells from E18 Mice brain
## 2 A Single-Cell Transcriptomic Map of the Hum~ pancreas
## 3 A single-cell molecular map of mouse gastru~ embryo
## 4 A single-cell reference map of transcriptio~ blood, hematopoietic system, lu~
## 5 A single-cell transcriptome atlas of the ad~ eye
## 6 Assessing the relevance of organoids to mod~ <NA>
## 7 Bone marrow plasma cells from hip replaceme~ hematopoietic system
## 8 Cell hashing with barcoded antibodies enabl~ blood
## 9 Census of Immune Cells blood, immune system
## 10 Comparison, calibration, and benchmarking o~ <NA>
## 11 Dissecting the human liver cellular landsca~ liver
## 12 Ischaemic sensitivity of human tissue by si~ esophagus, lung, spleen
## 13 Melanoma infiltration of stromal and immune~ lymph node, skin of body, tumor
## 14 Precursors of human CD4+ cytotoxic T lympho~ blood
## 15 Profiling of CD34+ cells from human bone ma~ <NA>
## Showing page 1 of 2
The original selection can be restored with resetSelect()
hca <- resetSelect(hca)
hca
## class: HCAExplorer
## Using azul backend at:
## https://service.explore.data.humancellatlas.org
##
## Donor count: 289
## Specimens: 780
## Estimated Cells: 4535231
## Files: 536961
## File Size: 30.2 Tb
##
## Showing projects with 15 results per page.# A tibble: 15 x 7
## projects.projec~ samples.sampleE~ samples.organ protocols.libra~
## <chr> <chr> <chr> <chr>
## 1 1.3 Million Bra~ specimens brain 10X v2 sequenci~
## 2 A Single-Cell T~ specimens pancreas inDrop, NULL
## 3 A single-cell m~ specimens embryo 10X 3' v1 seque~
## 4 A single-cell r~ specimens blood, hemat~ 10X v2 sequenci~
## 5 A single-cell t~ specimens eye 10X v2 sequenci~
## 6 Assessing the r~ organoids <NA> 10X v2 sequenci~
## 7 Bone marrow pla~ specimens hematopoieti~ MARS-seq, NULL
## 8 Cell hashing wi~ specimens blood CITE-seq, NULL
## 9 Census of Immun~ specimens blood, immun~ 10X v2 sequenci~
## 10 Comparison, cal~ cellLines <NA> DroNc-Seq, Drop~
## 11 Dissecting the ~ specimens liver 10X v2 sequenci~
## 12 Ischaemic sensi~ specimens esophagus, l~ 10X v2 sequenci~
## 13 Melanoma infilt~ specimens lymph node, ~ Smart-seq2, NULL
## 14 Precursors of h~ specimens blood Smart-seq2, NULL
## 15 Profiling of CD~ cellLines <NA> 10X v2 sequenci~
## # ... with 3 more variables: protocols.pairedEnd <chr>,
## # donorOrganisms.genusSpecies <chr>, samples.disease <chr>
## Showing page 1 of 2
To toggle whether projects, samples, or file are being displayed in the
tibble, the activate() method can be used to choose which to display.
## The HCAExplorer object is activated here by 'samples'
hca <- hca %>% activate('samples')
hca
## class: HCAExplorer
## Using azul backend at:
## https://service.explore.data.humancellatlas.org
##
## Donor count: 289
## Specimens: 780
## Estimated Cells: 4535231
## Files: 536961
## File Size: 30.2 Tb
##
## Showing projects with 15 results per page.# A tibble: 15 x 12
## samples.id projects.projec~ samples.sampleE~ samples.organ samples.organPa~
## <chr> <chr> <chr> <chr> <chr>
## 1 E18_20160~ 1.3 Million Bra~ specimens brain cortex
## 2 H1_pancre~ A Single-Cell T~ specimens pancreas NULL
## 3 embryo_po~ A single-cell m~ specimens embryo NULL
## 4 PP001, PP~ A single-cell r~ specimens blood, hemat~ Left lateral ba~
## 5 17-010-R,~ A single-cell t~ specimens eye retinal neural ~
## 6 Org_HPSI0~ Assessing the r~ organoids <NA> <NA>
## 7 Hip10_spe~ Bone marrow pla~ specimens hematopoieti~ bone marrow
## 8 Specimen_~ Cell hashing wi~ specimens blood NULL
## 9 1_BM1, 1_~ Census of Immun~ specimens blood, immun~ bone marrow, um~
## 10 cell_line~ Comparison, cal~ cellLines <NA> <NA>
## 11 P1TLH_liv~ Dissecting the ~ specimens liver caudate lobe
## 12 284CFrSpl~ Ischaemic sensi~ specimens esophagus, l~ esophagus mucos~
## 13 1104_LN, ~ Melanoma infilt~ specimens lymph node, ~ NULL
## 14 Subject10~ Precursors of h~ specimens blood peripheral bloo~
## 15 HS_BM_1_c~ Profiling of CD~ cellLines <NA> <NA>
## # ... with 7 more variables: cellSuspensions.selectedCellType <chr>,
## # protocols.libraryConstructionApproach <chr>, protocols.pairedEnd <chr>,
## # donorOrganisms.genusSpecies <chr>, donorOrganisms.organismAge <chr>,
## # donorOrganisms.biologicalSex <chr>, samples.disease <chr>
## Showing page 1 of 2
## Revert back to showing projects with 'projects'
hca <- hca %>% activate('projects')
hca
## class: HCAExplorer
## Using azul backend at:
## https://service.explore.data.humancellatlas.org
##
## Donor count: 289
## Specimens: 780
## Estimated Cells: 4535231
## Files: 536961
## File Size: 30.2 Tb
##
## Showing projects with 15 results per page.# A tibble: 15 x 7
## projects.projec~ samples.sampleE~ samples.organ protocols.libra~
## <chr> <chr> <chr> <chr>
## 1 1.3 Million Bra~ specimens brain 10X v2 sequenci~
## 2 A Single-Cell T~ specimens pancreas inDrop, NULL
## 3 A single-cell m~ specimens embryo 10X 3' v1 seque~
## 4 A single-cell r~ specimens blood, hemat~ 10X v2 sequenci~
## 5 A single-cell t~ specimens eye 10X v2 sequenci~
## 6 Assessing the r~ organoids <NA> 10X v2 sequenci~
## 7 Bone marrow pla~ specimens hematopoieti~ MARS-seq, NULL
## 8 Cell hashing wi~ specimens blood CITE-seq, NULL
## 9 Census of Immun~ specimens blood, immun~ 10X v2 sequenci~
## 10 Comparison, cal~ cellLines <NA> DroNc-Seq, Drop~
## 11 Dissecting the ~ specimens liver 10X v2 sequenci~
## 12 Ischaemic sensi~ specimens esophagus, l~ 10X v2 sequenci~
## 13 Melanoma infilt~ specimens lymph node, ~ Smart-seq2, NULL
## 14 Precursors of h~ specimens blood Smart-seq2, NULL
## 15 Profiling of CD~ cellLines <NA> 10X v2 sequenci~
## # ... with 3 more variables: protocols.pairedEnd <chr>,
## # donorOrganisms.genusSpecies <chr>, samples.disease <chr>
## Showing page 1 of 2
Looking at the bottom of the output, it can be that there are more pages of
results to be shown. The next set of entries can be obtained using the
nextResults method.
hca <- nextResults(hca)
hca
## class: HCAExplorer
## Using azul backend at:
## https://service.explore.data.humancellatlas.org
##
## Donor count: 289
## Specimens: 780
## Estimated Cells: 4535231
## Files: 536961
## File Size: 30.2 Tb
##
## Showing projects with 15 results per page.# A tibble: 13 x 7
## projects.projec~ samples.sampleE~ samples.organ protocols.libra~
## <chr> <chr> <chr> <chr>
## 1 Reconstructing ~ specimens blood, decid~ 10X v2 sequenci~
## 2 Single Cell Tra~ specimens kidney inDrop, NULL
## 3 Single cell pro~ cellLines, spec~ embryo, NULL 10X v2 sequenci~
## 4 Single cell tra~ specimens pancreas Smart-seq2, NULL
## 5 Single-cell RNA~ cellLines <NA> Smart-seq2, NULL
## 6 Single-cell RNA~ specimens pancreas Smart-seq2, NULL
## 7 Spatio-temporal~ specimens kidney 10X v2 sequenci~
## 8 Structural Remo~ specimens colon 10X 3' v2 seque~
## 9 Systematic comp~ specimens blood, brain 10X v2 sequenci~
## 10 Tabula Muris: T~ specimens adipose tiss~ Smart-seq2, NULL
## 11 The Single Cell~ specimens kidney 10X 5' v2 seque~
## 12 The emergent la~ specimens embryo, endo~ 10X v2 sequenci~
## 13 Transcriptomic ~ specimens eye 10x 3' v3 seque~
## # ... with 3 more variables: protocols.pairedEnd <chr>,
## # donorOrganisms.genusSpecies <chr>, samples.disease <chr>
## Showing page 2 of 2
Once the HCAExplorer object is made, one can beging browsing the data present in the Human Cell Atlas.
Suppose we would like to search projects that have samples taken from a
particular organ. First, it is helpdul to understand which fields are available
to query upon. To do this, use the fields() method.
hca <- HCAExplorer()
fields(hca)
## [1] "organ" "sampleEntityType"
## [3] "project" "assayType"
## [5] "instrumentManufacturerModel" "institution"
## [7] "donorDisease" "organismAgeUnit"
## [9] "organismAge" "pairedEnd"
## [11] "preservationMethod" "genusSpecies"
## [13] "projectTitle" "modelOrganPart"
## [15] "disease" "specimenOrganPart"
## [17] "workflow" "contactName"
## [19] "specimenOrgan" "effectiveOrgan"
## [21] "organPart" "publicationTitle"
## [23] "cellLineType" "libraryConstructionApproach"
## [25] "biologicalSex" "laboratory"
## [27] "projectDescription" "selectedCellType"
## [29] "specimenDisease" "fileFormat"
## [31] "modelOrgan"
This function return all possible fields that can be queried upon. We can now
see that their is a field named “organ”. Since, we are looking at what
values are avaiable for querying on organs, we can now use the values()
method to do just that.
values(hca, 'organ')
## # A tibble: 32 x 2
## value hits
## <chr> <chr>
## 1 blood 6
## 2 5 kidney
## 3 4 pancreas
## 4 4 brain
## 5 3 embryo
## 6 3 lung
## 7 3 eye
## 8 2 hematopoietic system
## 9 2 liver
## 10 2 skin of body
## # ... with 22 more rows
We can now see all possible values of 'organ' across all project as well as their frequency. Let's now decide that we would like to see projects that involve either blood or brain samples. The next step is to perform the query.
The HCAExplorer extends the functionality of the dplyr package's
filter() and select() methods.
The filter() method allows the user to query the Human Cell Atlas by relating
fields to certain values. Character fields can be queried using the operators:
==%in%Combination operators can be used to combine queries
&We can use either the == or %in% operator in a filter statement to contruct
a query.
hca2 <- hca %>% filter(organ == c('blood', 'brain'))
hca <- hca %>% filter(organ %in% c('blood', 'brain'))
hca
## class: HCAExplorer
## Using azul backend at:
## https://service.explore.data.humancellatlas.org
##
## Donor count: 53
## Specimens: 161
## Estimated Cells: 1676505
## Files: 134443
## File Size: 7.1 Tb
##
## Showing projects with 15 results per page.# A tibble: 8 x 7
## projects.projec~ samples.sampleE~ samples.organ protocols.libra~
## <chr> <chr> <chr> <chr>
## 1 1.3 Million Bra~ specimens brain 10X v2 sequenci~
## 2 A single-cell r~ specimens blood, hemat~ 10X v2 sequenci~
## 3 Cell hashing wi~ specimens blood CITE-seq, NULL
## 4 Census of Immun~ specimens blood, immun~ 10X v2 sequenci~
## 5 Precursors of h~ specimens blood Smart-seq2, NULL
## 6 Reconstructing ~ specimens blood, decid~ 10X v2 sequenci~
## 7 Systematic comp~ specimens blood, brain 10X v2 sequenci~
## 8 Tabula Muris: T~ specimens adipose tiss~ Smart-seq2, NULL
## # ... with 3 more variables: protocols.pairedEnd <chr>,
## # donorOrganisms.genusSpecies <chr>, samples.disease <chr>
## Showing page 1 of 1
Suppose we also wish to also search for results based on the disease.
We already know a “disease” field exists from our field() function. Now we
can see what disease values are present in our current results.
values(hca, 'disease')
## # A tibble: 2 x 2
## value hits
## <chr> <chr>
## 1 5 normal
## 2 3 orofaciodigital syndrome VIII
These are the possible values only for the results of our previous search.
Now suppose we would like to search for project only that have samples with no
disease (we see through values() that this is labeled as “normal”). We can
now accomplish this with any of the following searchs. To show multiple
searches, we will also use the methods undoQuery() and resetQuery() to step
reset our search. undoQuery() can step back one or many queries.
resetQuery() undos all queries.
hca <- hca %>% filter(disease == 'normal')
hca <- undoQuery(hca, n = 2L)
hca <- hca %>% filter(organ %in% c('Brain', 'brain'), disease == 'normal')
hca <- resetQuery(hca)
hca <- hca %>% filter(organ %in% c('Brain', 'brain') & disease == 'normal')
hca
## class: HCAExplorer
## Using azul backend at:
## https://service.explore.data.humancellatlas.org
##
## Donor count: 4
## Specimens: 4
## Estimated Cells: 1345175
## Files: 48909
## File Size: 3.4 Tb
##
## Showing projects with 15 results per page.# A tibble: 2 x 7
## projects.projec~ samples.sampleE~ samples.organ protocols.libra~
## <chr> <chr> <chr> <chr>
## 1 1.3 Million Bra~ specimens brain 10X v2 sequenci~
## 2 Systematic comp~ specimens blood, brain 10X v2 sequenci~
## # ... with 3 more variables: protocols.pairedEnd <chr>,
## # donorOrganisms.genusSpecies <chr>, samples.disease <chr>
## Showing page 1 of 1
We can refine out search further by using subsetting to only include
a few results. Here, the [ symbol can be used to select paricular rows by
either index or project name. These selections are added to our search as a
query against the “projectId”. Here we take the first two results from our
HCAExplorer object.
hca <- hca[1:2,]
hca
## class: HCAExplorer
## Using azul backend at:
## https://service.explore.data.humancellatlas.org
##
## Donor count: 4
## Specimens: 4
## Estimated Cells: 1345175
## Files: 48909
## File Size: 3.4 Tb
##
## Showing projects with 15 results per page.# A tibble: 2 x 7
## projects.projec~ samples.sampleE~ samples.organ protocols.libra~
## <chr> <chr> <chr> <chr>
## 1 1.3 Million Bra~ specimens brain 10X v2 sequenci~
## 2 Systematic comp~ specimens blood, brain 10X v2 sequenci~
## # ... with 3 more variables: protocols.pairedEnd <chr>,
## # donorOrganisms.genusSpecies <chr>, samples.disease <chr>
## Showing page 1 of 1
Now that we have completed our query, we can obtain the file manifest of our
selected projects. First, we must find which possible file formats are
available for download. To do this, we use the getManifestFileFormats().
formats <- getManifestFileFormats(hca)
formats
## [1] "fastq" "csv" "txt" "fastq.gz" "bam" "results"
## [7] "matrix" "bai" "unknown" "csv.gz" "npy" "npz"
Now that we have the possible file formats, we can download the manifest
as a tibble. To do this, we use the getManifest() method.
manifest <- getManifest(hca, fileFormat = formats[1])
manifest
## # A tibble: 16,377 x 43
## bundle_uuid bundle_version file_name file_format read_index file_size
## <chr> <dttm> <chr> <chr> <chr> <dbl>
## 1 0fdd8ea6-f~ 2019-05-16 21:18:13 E18_2016~ fastq read1 126940705
## 2 304015b8-3~ 2019-05-16 21:18:13 E18_2016~ fastq index1 41875415
## 3 304015b8-3~ 2019-05-16 21:18:13 E18_2016~ fastq read1 100735061
## 4 0bdfa0f9-0~ 2019-05-16 21:18:13 E18_2016~ fastq read2 361817158
## 5 05281a89-a~ 2019-05-16 21:18:13 E18_2016~ fastq read2 362196479
## 6 05281a89-a~ 2019-05-16 21:18:13 E18_2016~ fastq index1 35644537
## 7 0bf6812c-6~ 2019-05-16 21:18:13 E18_2016~ fastq read1 82151404
## 8 01f0c3f7-b~ 2019-05-16 21:18:13 E18_2016~ fastq index1 45572373
## 9 0631f2b7-d~ 2019-05-16 21:18:13 E18_2016~ fastq index1 35523811
## 10 0be57ce6-b~ 2019-05-16 21:18:13 E18_2016~ fastq read2 391350070
## # ... with 16,367 more rows, and 37 more variables: file_uuid <chr>,
## # file_version <dttm>, file_sha256 <chr>, file_content_type <chr>,
## # cell_suspension.provenance.document_id <chr>,
## # cell_suspension.estimated_cell_count <dbl>,
## # cell_suspension.selected_cell_type <chr>,
## # sequencing_process.provenance.document_id <chr>,
## # sequencing_protocol.instrument_manufacturer_model <chr>,
## # sequencing_protocol.paired_end <lgl>,
## # library_preparation_protocol.library_construction_approach <chr>,
## # project.provenance.document_id <chr>,
## # project.contributors.institution <chr>,
## # project.contributors.laboratory <chr>,
## # project.project_core.project_short_name <chr>,
## # project.project_core.project_title <chr>,
## # specimen_from_organism.provenance.document_id <chr>,
## # specimen_from_organism.diseases <chr>, specimen_from_organism.organ <chr>,
## # specimen_from_organism.organ_part <chr>,
## # specimen_from_organism.preservation_storage.preservation_method <chr>,
## # donor_organism.sex <chr>,
## # donor_organism.biomaterial_core.biomaterial_id <chr>,
## # donor_organism.provenance.document_id <chr>,
## # donor_organism.genus_species <chr>, donor_organism.diseases <chr>,
## # donor_organism.organism_age <dbl>, donor_organism.organism_age_unit <chr>,
## # cell_line.provenance.document_id <lgl>,
## # cell_line.biomaterial_core.biomaterial_id <lgl>,
## # organoid.provenance.document_id <lgl>,
## # organoid.biomaterial_core.biomaterial_id <lgl>, organoid.model_organ <lgl>,
## # organoid.model_organ_part <lgl>, `_entity_type` <chr>,
## # sample.provenance.document_id <chr>,
## # sample.biomaterial_core.biomaterial_id <chr>
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server 2012 R2 x64 (build 9600)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=C
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] HCAExplorer_1.3.1 dplyr_1.0.0 BiocStyle_2.17.0 knitr_1.29
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.5 pillar_1.4.6 compiler_4.0.2
## [4] BiocManager_1.30.10 plyr_1.8.6 tools_4.0.2
## [7] digest_0.6.25 jsonlite_1.7.0 evaluate_0.14
## [10] lifecycle_0.2.0 tibble_3.0.3 pkgconfig_2.0.3
## [13] rlang_0.4.7 tidygraph_1.2.0 igraph_1.2.5
## [16] cli_2.0.2 curl_4.3 yaml_2.2.1
## [19] parallel_4.0.2 xfun_0.15 stringr_1.4.0
## [22] httr_1.4.1 xml2_1.3.2 hms_0.5.3
## [25] generics_0.0.2 S4Vectors_0.27.12 vctrs_0.3.2
## [28] stats4_4.0.2 tidyselect_1.1.0 glue_1.4.1
## [31] R6_2.4.1 fansi_0.4.1 rmarkdown_2.3
## [34] readr_1.3.1 purrr_0.3.4 tidyr_1.1.0
## [37] magrittr_1.5 ellipsis_0.3.1 htmltools_0.5.0
## [40] BiocGenerics_0.35.4 assertthat_0.2.1 utf8_1.1.4
## [43] stringi_1.4.6 crayon_1.3.4
S3 object-oriented programming paradigm is used.dplyr package can be used to manipulate objects in the
HCAExplorer package.