Merge pull request #75 from stemangiola/update-db-0.2

Update db to 0.2

R/query.R

@@ -11,6 +11,7 @@ assay_map <- c(
 )
 
 REMOTE_URL <- "https://swift.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/harmonised-human-atlas"
+COUNTS_VERSION <- "0.2"
 
 #' Given a data frame of HCA metadata, returns a SingleCellExperiment object
 #' corresponding to the samples in that data frame
@@ -79,9 +80,10 @@ get_SingleCellExperiment <- function(
     cli_alert_info("Realising metadata.")
     raw_data <- collect(data)
     inherits(raw_data, "tbl") |> assert_that()
-    has_name(raw_data, c(".cell", "file_id_db")) |> assert_that()
+    has_name(raw_data, c("_cell", "file_id_db")) |> assert_that()
 
-    cache_directory |> dir.create(showWarnings = FALSE)
+    versioned_cache_directory = file.path(cache_directory, COUNTS_VERSION)
+    versioned_cache_directory |> dir.create(showWarnings = FALSE, recursive = TRUE)
 
     subdirs <- assay_map[assays]
 
@@ -100,7 +102,7 @@ get_SingleCellExperiment <- function(
             as.character() |>
             sync_assay_files(
                 url = parsed_repo,
-                cache_dir = cache_directory,
+                cache_dir = versioned_cache_directory,
                 files = _,
                 subdirs = subdirs
             )
@@ -111,7 +113,7 @@ get_SingleCellExperiment <- function(
         imap(function(current_subdir, current_assay) {
             # Build up an SCE for each assay
             dir_prefix <- file.path(
-                cache_directory,
+                versioned_cache_directory,
                 current_subdir
             )
 
@@ -172,14 +174,14 @@ group_to_sce <- function(i, df, dir_prefix, features) {
     sce <- loadHDF5SummarizedExperiment(sce_path)
     # The cells we select here are those that are both available in the SCE
     # object, and requested for this particular file
-    cells <- colnames(sce) |> intersect(df$.cell)
+    cells <- colnames(sce) |> intersect(df$`_cell`)
     # We need to make the cell names globally unique, which we can guarantee
     # by adding a suffix that is derived from file_id_db, which is the grouping
     # variable
     new_cellnames <- paste0(cells, "_", i)
     new_coldata <- df |>
-        mutate(original_cell_id = .data$.cell, .cell = new_cellnames) |>
-        column_to_rownames(".cell") |>
+        mutate(original_cell_id = .data$`_cell`, `_cell` = new_cellnames) |>
+        column_to_rownames("_cell") |>
         as("DataFrame")
 
     features |>
@@ -364,10 +366,10 @@ get_seurat <- function(...) {
 #' @importFrom httr progress
 #' @importFrom cli cli_alert_info
 get_metadata <- function(
-    remote_url = "https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata-sqlite/metadata.parquet",
+    remote_url = "https://object-store.rc.nectar.org.au/v1/AUTH_06d6e008e3e642da99d806ba3ea629c5/metadata/metadata.0.2.2.parquet",
     cache_directory = get_default_cache_dir()
 ) {
-    db_path <- file.path(cache_directory, "metadata.parquet")
+    db_path <- file.path(cache_directory, "metadata.0.2.2.parquet")
     sync_remote_file(
         remote_url,
         db_path,

tests/testthat/test-query.R

@@ -2,7 +2,7 @@ library(CuratedAtlasQueryR)
 
 test_that("get_SingleCellExperiment() correctly handles duplicate cell IDs", {
     meta <- get_metadata() |>
-        dplyr::filter(.cell == "868417_1") |>
+        dplyr::filter(`_cell` == "868417_1") |>
         dplyr::collect()
     sce <- get_SingleCellExperiment(meta)
     # This query should return multiple cells, despite querying only 1 cell ID

vignettes/Introduction.Rmd

@@ -31,7 +31,7 @@ knitr::include_graphics(c(
   "../man/figures/svcf_logo.jpeg", 
   "../man/figures/czi_logo.png", 
   "../man/figures/bioconductor_logo.jpg",
-    "../man/figures/vca_logo.png"
+  "../man/figures/vca_logo.png"
 ))
 ```