Add table connection cache

Author: multimeric

NAMESPACE

@@ -9,6 +9,7 @@ import(Seurat)
 import(dbplyr)
 importFrom(BiocGenerics,cbind)
 importFrom(DBI,dbConnect)
+importFrom(DBI,dbDisconnect)
 importFrom(HDF5Array,HDF5RealizationSink)
 importFrom(HDF5Array,loadHDF5SummarizedExperiment)
 importFrom(S4Vectors,DataFrame)
@@ -55,6 +56,7 @@ importFrom(purrr,pmap_chr)
 importFrom(purrr,reduce)
 importFrom(purrr,set_names)
 importFrom(purrr,transpose)
+importFrom(purrr,walk)
 importFrom(rlang,.data)
 importFrom(stats,setNames)
 importFrom(tibble,column_to_rownames)

R/query.R

@@ -350,101 +350,4 @@ as.sparse.DelayedMatrix <- function(x) {
 #'
 get_seurat <- function(...) {
     get_SingleCellExperiment(...) |> as.Seurat(data = NULL)
-}
-
-#' Gets the Curated Atlas metadata as a data frame.
-#' 
-#' Downloads a parquet database of the Human Cell Atlas metadata to a local 
-#' cache, and then opens it as a data frame. It can then be filtered and 
-#' passed into [get_SingleCellExperiment()] 
-#' to obtain a [`SingleCellExperiment::SingleCellExperiment-class`]
-#'
-#' @param remote_url Optional character vector of length 1. An HTTP URL pointing
-#'   to the location of the parquet database.
-#' @param cache_directory Optional character vector of length 1. A file path on
-#'   your local system to a directory (not a file) that will be used to store
-#'   metadata.parquet
-#' @return A lazy data.frame subclass containing the metadata. You can interact
-#'   with this object using most standard dplyr functions. For string matching,
-#'   it is recommended that you use `stringr::str_like` to filter character
-#'   columns, as `stringr::str_match` will not work.
-#' @export
-#' @examples
-#' library(dplyr)
-#' filtered_metadata <- get_metadata() |>
-#'     filter(
-#'         ethnicity == "African" &
-#'             assay %LIKE% "%10x%" &
-#'             tissue == "lung parenchyma" &
-#'             cell_type %LIKE% "%CD4%"
-#'     )
-#'
-#' @importFrom DBI dbConnect
-#' @importFrom duckdb duckdb
-#' @importFrom dplyr tbl
-#' @importFrom httr progress
-#' @importFrom cli cli_alert_info
-#' 
-#' @details 
-#' 
-#' The metadata was collected from the Bioconductor package `cellxgenedp`. it's vignette `using_cellxgenedp` provides an overview of the columns in the metadata.
-#' The data for which the column `organism_name` included "Homo sapiens" was collected collected from `cellxgenedp`.
-#' 
-#' The columns `dataset_id` and `file_id` link the datasets explorable through `CuratedAtlasQueryR` and `cellxgenedp`to the CELLxGENE portal.
-#' 
-#'  Our representation, harmonises the metadata at dataset, sample and cell levels, in a unique coherent database table.
-#' 
-#' Dataset-specific columns (definitions available at cellxgene.cziscience.com)
-#' `cell_count`, `collection_id`, `created_at.x`, `created_at.y`, `dataset_deployments`, `dataset_id`, `file_id`, `filename`, `filetype`, `is_primary_data.y`, `is_valid`, `linked_genesets`, `mean_genes_per_cell`, `name`, `published`, `published_at`, `revised_at`, `revision`, `s3_uri`, `schema_version`, `tombstone`, `updated_at.x`, `updated_at.y`, `user_submitted`, `x_normalization`
-#' 
-#' Sample-specific columns (definitions available at cellxgene.cziscience.com)
-#' 
-#' `sample_`, `.sample_name`, `age_days`, `assay`, `assay_ontology_term_id`, `development_stage`, `development_stage_ontology_term_id`, `ethnicity`, `ethnicity_ontology_term_id`, `experiment___`, `organism`, `organism_ontology_term_id`, `sample_placeholder`, `sex`, `sex_ontology_term_id`, `tissue`, `tissue_harmonised`, `tissue_ontology_term_id`, `disease`, `disease_ontology_term_id`, `is_primary_data.x`
-#' 
-#' Cell-specific columns (definitions available at cellxgene.cziscience.com)
-#' 
-#' `cell_`, `cell_type`, `cell_type_ontology_term_idm`, `cell_type_harmonised`, `confidence_class`, `cell_annotation_azimuth_l2`, `cell_annotation_blueprint_singler` 
-#' 
-#' Through harmonisation and curation we introduced custom column, not present in the original CELLxGENE metadata
-#' 
-#' - `tissue_harmonised`: a coarser tissue name for better filtering
-#' - `age_days`: the number of days corresponding to the age
-#' - `cell_type_harmonised`: the consensus call identity (for immune cells) using the original and three novel annotations using Seurat Azimuth and SingleR
-#' - `confidence_class`: an ordinal class of how confident `cell_type_harmonised` is. 1 is complete consensus, 2 is 3 out of four and so on.             
-#' - `cell_annotation_azimuth_l2`: Azimuth cell annotation
-#' - `cell_annotation_blueprint_singler`: SingleR cell annotation using Blueprint reference
-#' - `cell_annotation_blueprint_monaco`: SingleR cell annotation using Monaco reference
-#' - `sample_id_db`: Sample subdivision for internal use
-#' - `file_id_db`: File subdivision for internal use
-#' - `sample_`: Sample ID
-#' - `.sample_name`: How samples were defined
-#' 
-#' 
-#' **Possible cache path issues**
-#' 
-#' If your default R cache path includes non-standard characters (e.g. dash because of your user or organisation name), the following error can manifest
-#' 
-#' Error in `db_query_fields.DBIConnection()`:
-#' ! Can't query fields.
-#' Caused by error:
-#' ! Parser Error: syntax error at or near "/"
-#' LINE 2: FROM /Users/bob/Library/Cach...
-#' 
-#' The solution is to choose a different cache, for example
-#' 
-#' get_metadata(cache_directory = path.expand('~'))
-#' 
-get_metadata <- function(
-    remote_url = "https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/metadata.0.2.3.parquet",
-    cache_directory = get_default_cache_dir()
-) {
-    db_path <- file.path(cache_directory, "metadata.0.2.3.parquet")
-    sync_remote_file(
-        remote_url,
-        db_path,
-        progress(type = "down", con = stderr())
-    )
-    duckdb() |>
-        dbConnect(drv = _, read_only = TRUE) |>
-        tbl(db_path)
-}
+}

man/get_metadata.Rd

@@ -183,3 +183,10 @@ test_that("get_unharmonised_metadata() returns the appropriate data", {
         nrow(unharmonised)
     )
 })
+
+test_that("get_metadata() is cached", {
+    table = get_metadata()
+    table_2 = get_metadata()
+    
+    identical(table, table_2) |> expect_true()
+})