Merge pull request #63 from stemangiola/reuse-connection

Reuse connection

DESCRIPTION

@@ -36,7 +36,6 @@ Imports:
     methods,
     rlang,
     stats,
-    RSQLite,
     S4Vectors,
     tibble,
     utils,

R/query.R

@@ -12,6 +12,9 @@ assay_map <- c(
 
 #' Used in a pipeline to run one or more expressions with side effects, but
 #' return the input value as the output value unaffected
+#' @noRd
+#' @param x The value to return
+#' @param ... Expressions to evaluate
 aside <- function(x, ...) {
     # Courtesy of Hadley: https://fosstodon.org/@hadleywickham/109558265769090930
     list(...)

vignettes/Introduction.Rmd

@@ -21,7 +21,8 @@ knitr::opts_chunk$set(
 ```
 
 ```{r, echo=FALSE, out.height = "139px", out.width = "120px"}
-knitr::include_graphics("inst/logo.png")
+system.file("logo.png", package="CuratedAtlasQueryR") |>
+    knitr::include_graphics()
 ```
 
 ## Load the package
@@ -37,13 +38,15 @@ library(stringr)
 ### Load the metadata
 
 ```{r}
-get_metadata()
+meta <- get_metadata()
 ```
 
+The `meta` variable can then be re-used for all subsequent queries.
+
 ### Explore the tissue 
 
 ```{r, eval=FALSE}
-get_metadata() |>
+meta |>
     dplyr::distinct(tissue, file_id) 
 ```
 
@@ -74,7 +77,7 @@ get_metadata() |>
 ```{r}
 
 single_cell_counts = 
-	get_metadata() |>
+	meta |>
     dplyr::filter(
         ethnicity == "African" &
         stringr::str_like(assay, "%10x%") &
@@ -92,7 +95,7 @@ This is helpful if just few genes are of interest, as they can be compared acros
 
 ```{r}
 single_cell_counts = 
-	get_metadata() |>
+	meta |>
     dplyr::filter(
         ethnicity == "African" &
         stringr::str_like(assay, "%10x%") &
@@ -108,7 +111,7 @@ single_cell_counts
 
 ```{r}
 single_cell_counts = 
-	get_metadata() |>
+	meta |>
     dplyr::filter(
         ethnicity == "African" &
         stringr::str_like(assay, "%10x%") &
@@ -127,7 +130,7 @@ This convert the H5 SingleCellExperiment to Seurat so it might take long time an
 
 ```{r}
 single_cell_counts = 
-	get_metadata() |>
+	meta |>
     dplyr::filter(
         ethnicity == "African" &
         stringr::str_like(assay, "%10x%") &
@@ -147,7 +150,7 @@ We can gather all natural killer cells and plot the distribution of CD56 (NCAM1)
 library(tidySingleCellExperiment)
 library(ggplot2)
 
-get_metadata() |> 
+meta |> 
     
   # Filter and subset
   filter(cell_type_harmonised=="nk") |> 
@@ -172,7 +175,8 @@ get_metadata() |>
 ```
 
 ```{r, echo=FALSE, message=FALSE, warning=FALSE}
-knitr::include_graphics("inst/NCAM1_figure.png")
+system.file("NCAM1_figure.png", package="CuratedAtlasQueryR") |>
+    knitr::include_graphics()
 ```
 
 # Cell metadata