Pull the development branch from Bioconductor/HCAMatrixBrowser
if (!requireNamespace("BiocManager", quietly = TRUE)
install.packages("BiocManager")
BiocManager::install("HCAMatrixBrowser")library(HCAMatrixBrowser)
library(rapiclient)
library(AnVIL)Create an HCA api object using the rapiclient package:
(hca <- HCAMatrix())## service: HCAMatrix
## tags(); use hcamatrix$<tab completion>:
## # A tibble: 14 x 3
## tag operation summary
## <chr> <chr> <chr>
## 1 internal matrix.lambdas.api.v0.core.dss_no~ matrix.lambdas.api.v0.core.dss_n~
## 2 v0 matrix.lambdas.api.v0.core.get_fo~ matrix.lambdas.api.v0.core.get_f~
## 3 v0 matrix.lambdas.api.v0.core.get_ma~ matrix.lambdas.api.v0.core.get_m~
## 4 v0 matrix.lambdas.api.v0.core.post_m~ matrix.lambdas.api.v0.core.post_~
## 5 v1 matrix.lambdas.api.v1.core.get_fe~ matrix.lambdas.api.v1.core.get_f~
## 6 v1 matrix.lambdas.api.v1.core.get_fe~ matrix.lambdas.api.v1.core.get_f~
## 7 v1 matrix.lambdas.api.v1.core.get_fi~ matrix.lambdas.api.v1.core.get_f~
## 8 v1 matrix.lambdas.api.v1.core.get_fi~ matrix.lambdas.api.v1.core.get_f~
## 9 v1 matrix.lambdas.api.v1.core.get_fi~ matrix.lambdas.api.v1.core.get_f~
## 10 v1 matrix.lambdas.api.v1.core.get_fi~ matrix.lambdas.api.v1.core.get_f~
## 11 v1 matrix.lambdas.api.v1.core.get_fo~ matrix.lambdas.api.v1.core.get_f~
## 12 v1 matrix.lambdas.api.v1.core.get_fo~ matrix.lambdas.api.v1.core.get_f~
## 13 v1 matrix.lambdas.api.v1.core.get_ma~ matrix.lambdas.api.v1.core.get_m~
## 14 v1 matrix.lambdas.api.v1.core.post_m~ matrix.lambdas.api.v1.core.post_~
## tag values:
## internal, v0, v1
## schemas():
## v0_MatrixRequest, v0_MatrixResponse, v0_MatrixErrorResponse,
## v0_DssNotification, v1_ComparisonFilter
## # ... with 14 more elementsCheck that the schemas are available as found in the api configuration file: Note. Due to downgrade from Open API Specification (OAS) version 3 to OAS version 2 (formerly known as Swagger), the full list of schemas may not be shown.
schemas(hca)## $v0_MatrixRequest
## v0_MatrixRequest(bundle_fqids, bundle_fqids_url, format)
##
## $v0_MatrixResponse
## v0_MatrixResponse(request_id, status, matrix_location, eta, message)
##
## $v0_MatrixErrorResponse
## v0_MatrixErrorResponse(message)
##
## $v0_DssNotification
## v0_DssNotification(transaction_id, subscription_id, event_type,
## match)
##
## $v1_ComparisonFilter
## v1_ComparisonFilter(op, field, value)
##
## $v1_LogicalFilter
## v1_LogicalFilter(op, value)
##
## $v1_MatrixRequest
## v1_MatrixRequest(filter, fields, format, feature)
##
## $v1_MatrixResponse
## v1_MatrixResponse(request_id, status, matrix_url, message, eta)
##
## $v1_MatrixErrorResponse
## v1_MatrixErrorResponse(status_code, message)
##
## $v1_MatrixFeatureDetail
## v1_MatrixFeatureDetail(feature, feature_description)
##
## $v1_FieldDetail
## v1_FieldDetail(field_name, field_description, field_type)
##
## $v1_NumericFieldDetail
## v1_NumericFieldDetail(field_name, field_description, field_type,
## minimum, maximum)
##
## $v1_CategoricalFieldDetail
## v1_CategoricalFieldDetail(field_name, field_description, field_type,
## cell_counts)
##
## $Match
## Match(bundle_uuid, bundle_version)Here we make available a number of helper functions that allow the user to explore the possible queries supported by the HCA Matrix Service data model.
available_filters(hca)## [1] "cell_suspension.provenance.document_id"
## [2] "genes_detected"
## [3] "total_umis"
## [4] "barcode"
## [5] "emptydrops_is_cell"
## [6] "specimen_from_organism.provenance.document_id"
## [7] "cell_suspension.genus_species.ontology"
## [8] "cell_suspension.genus_species.ontology_label"
## [9] "donor_organism.provenance.document_id"
## [10] "donor_organism.human_specific.ethnicity.ontology"
## [11] "donor_organism.human_specific.ethnicity.ontology_label"
## [12] "donor_organism.diseases.ontology"
## [13] "donor_organism.diseases.ontology_label"
## [14] "donor_organism.development_stage.ontology"
## [15] "donor_organism.development_stage.ontology_label"
## [16] "donor_organism.sex"
## [17] "donor_organism.is_living"
## [18] "derived_organ_ontology"
## [19] "derived_organ_label"
## [20] "derived_organ_parts_ontology"
## [21] "derived_organ_parts_label"
## [22] "specimen_from_organism.organ.ontology"
## [23] "specimen_from_organism.organ.ontology_label"
## [24] "specimen_from_organism.organ_parts.ontology"
## [25] "specimen_from_organism.organ_parts.ontology_label"
## [26] "library_preparation_protocol.provenance.document_id"
## [27] "library_preparation_protocol.input_nucleic_acid_molecule.ontology"
## [28] "library_preparation_protocol.input_nucleic_acid_molecule.ontology_label"
## [29] "library_preparation_protocol.library_construction_method.ontology"
## [30] "library_preparation_protocol.library_construction_method.ontology_label"
## [31] "library_preparation_protocol.end_bias"
## [32] "library_preparation_protocol.strand"
## [33] "project.provenance.document_id"
## [34] "project.project_core.project_short_name"
## [35] "project.project_core.project_title"
## [36] "analysis_protocol.provenance.document_id"
## [37] "dss_bundle_fqid"
## [38] "bundle_uuid"
## [39] "bundle_version"
## [40] "file_uuid"
## [41] "file_version"
## [42] "analysis_protocol.protocol_core.protocol_id"
## [43] "analysis_working_group_approval_status"We can get details for a particular filter:
filter_detail(hca, "genes_detected")## $field_description
## [1] "Count of genes with a non-zero count."
##
## $field_name
## [1] "genes_detected"
##
## $field_type
## [1] "numeric"
##
## $maximum
## [1] 13108
##
## $minimum
## [1] 2We can obtain the available formats for any request by doing:
available_formats(hca)## [1] "loom" "csv" "mtx"We can get additional details (via HTML pop-up page):
format_detail(hca, "mtx")The matrix service provides two types of features (i.e., genes and transcripts):
available_features(hca)## [1] "gene" "transcript"and a short description of a selected feature:
feature_detail(hca, "gene")## $feature
## [1] "gene"
##
## $feature_description
## [1] "Genes from the GENCODE v27 comprehensive annotation."Using the rapiclient package, we can obtain schema or models for queries:
bundle_fqids <-
c("980b814a-b493-4611-8157-c0193590e7d9.2018-11-12T131442.994059Z",
"7243c745-606d-4827-9fea-65a925d5ab98.2018-11-07T002256.653887Z")
req <- schemas(hca)$v0_MatrixRequest(
bundle_fqids = bundle_fqids, format = "loom"
)
req## $bundle_fqids
## [1] "980b814a-b493-4611-8157-c0193590e7d9.2018-11-12T131442.994059Z"
## [2] "7243c745-606d-4827-9fea-65a925d5ab98.2018-11-07T002256.653887Z"
##
## $format
## [1] "loom"For requests such as these, it is better to use the API layer provided by
HCAMatrixBrowser (see examples below).
The HCA group has provided example JSON requests written in python:
The published notebook is available here: https://github.com/HumanCellAtlas/matrix-service/blob/master/docs/HCA%20Matrix%20Service%20to%20Scanpy.ipynb
The following JSON request applies a couple of filters on the project short name and the number of genes detected.
The provided example request looks like:
jsonlite::fromJSON(
txt = '{"filter": {"op": "and", "value": [ {"op": "=", "value": "Single cell transcriptome analysis of human pancreas", "field": "project.project_core.project_short_name"}, {"op": ">=", "value": 300, "field": "genes_detected"} ] }}'
)## $filter
## $filter$op
## [1] "and"
##
## $filter$value
## op value
## 1 = Single cell transcriptome analysis of human pancreas
## 2 >= 300
## field
## 1 project.project_core.project_short_name
## 2 genes_detectedAdvanced usage note. We are interested in using the following endpoint:
hca$matrix.lambdas.api.v1.core.post_matrix## matrix.lambdas.api.v1.core.post_matrix
## matrix.lambdas.api.v1.core.post_matrix
## Description:
## Request an expression matrix using a set of filters to be ANDed and
## applied to the HCA expression data.
##
## Parameters:
## filter (object)
## Filter to apply in a matrix request.
## fields (array[string])
## Metadata fields to include in output matrix.
## format (string)
## Format for the output matrix.
## feature (string)
## Feature type to include in the output matrix. For example, genes
## vs. transcripts.The HCAMatrixBrowser allows filtering using a non-standard evaluation
method typical of the ‘tidyverse’. Here we recreate the filters provided
in the example above.
hcafilt <- filter(hca,
project.project_core.project_short_name ==
"Single cell transcriptome analysis of human pancreas" &
genes_detected >= 300)We can subsequently view the generated filter from the operation:
filters(hcafilt)## $op
## [x] "and"
##
## $value
## op field
## 2 = project.project_core.project_short_name
## 21 >= genes_detected
## value
## 2 Single cell transcriptome analysis of human pancreas
## 21 300In order to send the query with the appropriate filters, we simply use
the provided loadHCAMatrix function along with the API object that contains
the filter structure. See the following section for more information.
The matrix service allows you to request three different file formats:
These can be requested using the format argument in the loadHCAMatrix
function:
The loom format is supported by the LoomExperiment package in Bioconductor.
(loomex <- loadHCAMatrix(hcafilt, format = "loom"))## HCAMatrixBrowser cache directory set to:
## C:\Users\biocbuild\AppData\Local/R/cache/R/HCAMatrixBrowser## class: LoomExperiment
## dim: 58347 2544
## metadata(0):
## assays(1): matrix
## rownames: NULL
## rowData names(9): Accession Gene ... genus_species isgene
## colnames(2544): 00ca0d37-b787-41a4-be59-2aff5b13b0bd
## 0103aed0-29c2-4b29-a02a-2b58036fe875 ...
## fe0d170e-af6e-4420-827b-27b125fec214
## 3d22b32b-2bab-47e9-9933-acf5c59225c0
## colData names(44): CellID analysis_protocol.protocol_core.protocol_id
## ... specimen_from_organism.provenance.document_id total_umis
## rowGraphs(0): NULL
## colGraphs(0): NULLFor the mtx format, we represent the data as a SingleCellExperiment class:
(mtmat <- loadHCAMatrix(hcafilt, format = "mtx"))## class: SingleCellExperiment
## dim: 58347 2544
## metadata(0):
## assays(1): counts
## rownames(58347): ENSG00000000003 ENSG00000000005 ... ENSG00000284747
## ENSG00000284748
## rowData names(8): V2 V3 ... V8 V9
## colnames(2544): 00ca0d37-b787-41a4-be59-2aff5b13b0bd
## 0103aed0-29c2-4b29-a02a-2b58036fe875 ...
## fe0d170e-af6e-4420-827b-27b125fec214
## 3d22b32b-2bab-47e9-9933-acf5c59225c0
## colData names(43): genes_detected file_uuid ...
## analysis_protocol.protocol_core.protocol_id
## analysis_working_group_approval_status
## reducedDimNames(0):
## altExpNames(0):The csv format option gives the user the option to obtain the data
as a list of tibble data.frames from the output of the readr package in the
‘tidyverse’.
Note. Loading multiple CSV files may take considerable time depending on system configuration.
(tib <- loadHCAMatrix(hcafilt, format = "csv"))## This may take a while...##
## -- Column specification --------------------------------------------------------
## cols(
## featurekey = col_character(),
## featurename = col_character(),
## featuretype = col_character(),
## chromosome = col_character(),
## featurestart = col_double(),
## featureend = col_double(),
## isgene = col_logical(),
## genus_species = col_character()
## )##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## cellkey = col_character()
## )
## i Use `spec()` for the full column specifications.##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_character(),
## genes_detected = col_double(),
## file_version = col_datetime(format = ""),
## total_umis = col_logical(),
## emptydrops_is_cell = col_logical(),
## barcode = col_logical(),
## bundle_version = col_datetime(format = "")
## )
## i Use `spec()` for the full column specifications.## $genes.csv
## # A tibble: 58,347 x 8
## featurekey featurename featuretype chromosome featurestart featureend isgene
## <chr> <chr> <chr> <chr> <dbl> <dbl> <lgl>
## 1 ENSG00000~ TSPAN6 protein_co~ chrX 100627109 100639991 TRUE
## 2 ENSG00000~ TNMD protein_co~ chrX 100584802 100599885 TRUE
## 3 ENSG00000~ DPM1 protein_co~ chr20 50934867 50958555 TRUE
## 4 ENSG00000~ SCYL3 protein_co~ chr1 169849631 169894267 TRUE
## 5 ENSG00000~ C1orf112 protein_co~ chr1 169662007 169854080 TRUE
## 6 ENSG00000~ FGR protein_co~ chr1 27612064 27635277 TRUE
## 7 ENSG00000~ CFH protein_co~ chr1 196651878 196747504 TRUE
## 8 ENSG00000~ FUCA2 protein_co~ chr6 143494811 143511690 TRUE
## 9 ENSG00000~ GCLC protein_co~ chr6 53497341 53616970 TRUE
## 10 ENSG00000~ NFYA protein_co~ chr6 41072945 41099976 TRUE
## # ... with 58,337 more rows, and 1 more variable: genus_species <chr>
##
## $expression.csv
## # A tibble: 2,544 x 58,348
## cellkey ENSG00000000003 ENSG00000000005 ENSG00000000419 ENSG00000000457
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 00ca0d~ 0 0 11 0
## 2 0103ae~ 0 0 0 0
## 3 01a5dd~ 0 0 37 0
## 4 020d39~ 0 0 0 0
## 5 025836~ 0 0 213 0
## 6 041637~ 0 0 0 6
## 7 044472~ 0 0 0 0
## 8 046c1a~ 127 0 58 105
## 9 04f60c~ 0 0 0 0
## 10 061f92~ 0 0 5 0
## # ... with 2,534 more rows, and 58,343 more variables: ENSG00000000460 <dbl>,
## # ENSG00000000938 <dbl>, ENSG00000000971 <dbl>, ENSG00000001036 <dbl>,
## # ENSG00000001084 <dbl>, ENSG00000001167 <dbl>, ENSG00000001460 <dbl>,
## # ENSG00000001461 <dbl>, ENSG00000001497 <dbl>, ENSG00000001561 <dbl>,
## # ENSG00000001617 <dbl>, ENSG00000001626 <dbl>, ENSG00000001629 <dbl>,
## # ENSG00000001630 <dbl>, ENSG00000001631 <dbl>, ENSG00000002016 <dbl>,
## # ENSG00000002079 <dbl>, ENSG00000002330 <dbl>, ENSG00000002549 <dbl>,
## # ENSG00000002586 <dbl>, ENSG00000002586_PAR_Y <dbl>, ENSG00000002587 <dbl>,
## # ENSG00000002726 <dbl>, ENSG00000002745 <dbl>, ENSG00000002746 <dbl>,
## # ENSG00000002822 <dbl>, ENSG00000002834 <dbl>, ENSG00000002919 <dbl>,
## # ENSG00000002933 <dbl>, ENSG00000003056 <dbl>, ENSG00000003096 <dbl>,
## # ENSG00000003137 <dbl>, ENSG00000003147 <dbl>, ENSG00000003249 <dbl>,
## # ENSG00000003393 <dbl>, ENSG00000003400 <dbl>, ENSG00000003402 <dbl>,
## # ENSG00000003436 <dbl>, ENSG00000003509 <dbl>, ENSG00000003756 <dbl>,
## # ENSG00000003987 <dbl>, ENSG00000003989 <dbl>, ENSG00000004059 <dbl>,
## # ENSG00000004139 <dbl>, ENSG00000004142 <dbl>, ENSG00000004399 <dbl>,
## # ENSG00000004455 <dbl>, ENSG00000004468 <dbl>, ENSG00000004478 <dbl>,
## # ENSG00000004487 <dbl>, ENSG00000004534 <dbl>, ENSG00000004660 <dbl>,
## # ENSG00000004700 <dbl>, ENSG00000004766 <dbl>, ENSG00000004776 <dbl>,
## # ENSG00000004777 <dbl>, ENSG00000004779 <dbl>, ENSG00000004799 <dbl>,
## # ENSG00000004809 <dbl>, ENSG00000004838 <dbl>, ENSG00000004846 <dbl>,
## # ENSG00000004848 <dbl>, ENSG00000004864 <dbl>, ENSG00000004866 <dbl>,
## # ENSG00000004897 <dbl>, ENSG00000004939 <dbl>, ENSG00000004948 <dbl>,
## # ENSG00000004961 <dbl>, ENSG00000004975 <dbl>, ENSG00000005001 <dbl>,
## # ENSG00000005007 <dbl>, ENSG00000005020 <dbl>, ENSG00000005022 <dbl>,
## # ENSG00000005059 <dbl>, ENSG00000005073 <dbl>, ENSG00000005075 <dbl>,
## # ENSG00000005100 <dbl>, ENSG00000005102 <dbl>, ENSG00000005108 <dbl>,
## # ENSG00000005156 <dbl>, ENSG00000005175 <dbl>, ENSG00000005187 <dbl>,
## # ENSG00000005189 <dbl>, ENSG00000005194 <dbl>, ENSG00000005206 <dbl>,
## # ENSG00000005238 <dbl>, ENSG00000005243 <dbl>, ENSG00000005249 <dbl>,
## # ENSG00000005302 <dbl>, ENSG00000005339 <dbl>, ENSG00000005379 <dbl>,
## # ENSG00000005381 <dbl>, ENSG00000005421 <dbl>, ENSG00000005436 <dbl>,
## # ENSG00000005448 <dbl>, ENSG00000005469 <dbl>, ENSG00000005471 <dbl>,
## # ENSG00000005483 <dbl>, ENSG00000005486 <dbl>, ENSG00000005513 <dbl>, ...
##
## $cells.csv
## # A tibble: 2,544 x 44
## cellkey genes_detected file_uuid file_version total_umis
## <chr> <dbl> <chr> <dttm> <lgl>
## 1 00ca0d~ 6924 6770c8ea~ 2019-05-15 01:06:43 NA
## 2 0103ae~ 3171 68efbcbe~ 2019-05-30 21:24:07 NA
## 3 01a5dd~ 3838 209d615a~ 2019-05-14 16:08:18 NA
## 4 020d39~ 4111 143a7005~ 2019-05-14 21:41:52 NA
## 5 025836~ 5834 45e82c84~ 2019-05-14 16:15:19 NA
## 6 041637~ 2564 cb0b32b2~ 2019-05-14 21:16:51 NA
## 7 044472~ 3152 2b5338c5~ 2019-05-14 14:38:01 NA
## 8 046c1a~ 6375 66515cc6~ 2019-05-14 13:35:49 NA
## 9 04f60c~ 4650 45e89c83~ 2019-05-14 13:09:27 NA
## 10 061f92~ 4420 476dc6ce~ 2019-05-14 12:04:56 NA
## # ... with 2,534 more rows, and 39 more variables: emptydrops_is_cell <lgl>,
## # barcode <lgl>, cell_suspension.provenance.document_id <chr>,
## # specimen_from_organism.provenance.document_id <chr>,
## # derived_organ_ontology <chr>, derived_organ_label <chr>,
## # derived_organ_parts_ontology <chr>, derived_organ_parts_label <chr>,
## # cell_suspension.genus_species.ontology <chr>,
## # cell_suspension.genus_species.ontology_label <chr>,
## # donor_organism.provenance.document_id <chr>,
## # donor_organism.human_specific.ethnicity.ontology <chr>,
## # donor_organism.human_specific.ethnicity.ontology_label <chr>,
## # donor_organism.diseases.ontology <chr>,
## # donor_organism.diseases.ontology_label <chr>,
## # donor_organism.development_stage.ontology <chr>,
## # donor_organism.development_stage.ontology_label <chr>,
## # donor_organism.sex <chr>, donor_organism.is_living <chr>,
## # specimen_from_organism.organ.ontology <chr>,
## # specimen_from_organism.organ.ontology_label <chr>,
## # specimen_from_organism.organ_parts.ontology <chr>,
## # specimen_from_organism.organ_parts.ontology_label <chr>,
## # library_preparation_protocol.provenance.document_id <chr>,
## # library_preparation_protocol.input_nucleic_acid_molecule.ontology <chr>,
## # library_preparation_protocol.input_nucleic_acid_molecule.ontology_label <chr>,
## # library_preparation_protocol.library_construction_method.ontology <chr>,
## # library_preparation_protocol.library_construction_method.ontology_label <chr>,
## # library_preparation_protocol.end_bias <chr>,
## # library_preparation_protocol.strand <chr>,
## # project.provenance.document_id <chr>,
## # project.project_core.project_short_name <chr>,
## # project.project_core.project_title <chr>,
## # analysis_protocol.provenance.document_id <chr>, dss_bundle_fqid <chr>,
## # bundle_uuid <chr>, bundle_version <dttm>,
## # analysis_protocol.protocol_core.protocol_id <chr>,
## # analysis_working_group_approval_status <chr>To use a lower level API endpoint, we can use the API object to select a particular endpoint. This will usually show you a description of the endpoint and the parameters for the query.
hca$matrix.lambdas.api.v0.core.post_matrix## matrix.lambdas.api.v0.core.post_matrix
## matrix.lambdas.api.v0.core.post_matrix
## Description:
## Prepares a single expression matrix combining all expression matrices
## belonging to the specified analysis bundles. On success, this request
## will asynchronously start a job to prepare the expression matrix and
## return with the request ID of the job. The request ID can be used to
## retrieve the status and results of the job from the GET endpoint.
##
## Parameters:
## bundle_fqids (array[string])
## bundle_fqids_url (string)
## format (v0_MatrixFormat)In the above example, there are three parameters required:
bundle_fqidsbundle_fqids_urlformat