Added the BunisHSPCData() getter function.

DESCRIPTION

@@ -9,7 +9,8 @@ Authors@R: c(
     person("Alan", "O'Callaghan", role="ctb"),
     person("Jens", "Preussner", role="ctb"),
     person("Charlotte", "Soneson", role="ctb"),
-    person("Stephany", "Orjuela", role="ctb"))
+    person("Stephany", "Orjuela", role="ctb"),
+    person("Daniel", "Bunis", role="ctb"))
 Description: Gene-level counts for a collection of public scRNA-seq datasets,
     provided as SingleCellExperiment objects with cell- and gene-level metadata.
 License: CC0

NAMESPACE

@@ -6,6 +6,7 @@ export(BacherTCellData)
 export(BaronPancreasData)
 export(BhaduriOrganoidData)
 export(BuettnerESCData)
+export(BunisHSPCData)
 export(CampbellBrainData)
 export(ChenBrainData)
 export(DarmanisBrainData)

R/BunisHSPCData.R

@@ -0,0 +1,62 @@
+#' Obtain the Bunis haematopoietic stem and progenitor cell data
+#'
+#' Obtain the human fetal, newborn, and adult haematopoietic stem and progenitor cell single-cell RNA-seq dataset from Bunis et al. (2021).
+#'
+#' @param filtered Logical scalar or "cells" indicating whether to:
+#' \itemize{
+#' \item \code{TRUE}: filter out cells that were not used by the authors.
+#' \item \code{"cells"}: filter out empty droplets as filtered out by cell ranger.
+#' \item \code{FALSE}: no filtering
+#' }
+#' 
+#' @details
+#' Column metadata is recreated from GEO using the author-supplied TSV of per-cell annotations, or retrieved from a processed version of the data shared by authors via figshare.
+#' This contains information such as the tissue & sample of origin, age group, likely cell type, and Developmental Stage Scoring.
+#' Within DevStageScoring element of the column metadata are the applied results ('<cell_type>_scores') of random forest regression trained on the fetal (score = 0) and adult (score = 1) cells of individual cell types indicated by ('<cell_type>_inTraining').
+#'
+#' If \code{filtered=TRUE}, only the cells used by the authors in their final analysis are returned.
+#' Otherwise, an additional \code{retained} field will be present in the \code{\link{colData}}, indicating whether the cell was retained by the authors.
+#'
+#' All data are downloaded from ExperimentHub and cached for local re-use.
+#' Specific resources can be retrieved by searching for \code{scRNAseq/bunis-hspc}.
+#'
+#' @return A \linkS4class{SingleCellExperiment} object with a single matrix of UMI counts.
+#'
+#' @author Daniel Bunis
+#'
+#' @references
+#' Bunis DG et al. (2021). 
+#' Single-Cell Mapping of Progressive Fetal-to-Adult Transition in Human Naive T Cells
+#' \emph{Cell Rep.} 34(1): 108573
+#' 
+#' @examples
+#' sce <- BunisHSPCData()
+#' 
+#' @export
+BunisHSPCData <- function(filtered=TRUE) {
+    version <- "2.6.0"
+
+    sce <- .create_sce(file.path("bunis-hspc", version), has.rowdata = TRUE, has.coldata = FALSE)
+
+    hub <- ExperimentHub()
+    colData.path <- file.path("scRNAseq", "bunis-hspc", version, "coldata.rds")
+    colData <- hub[hub$rdatapath==colData.path][[1]]
+
+    if (isTRUE(filtered)) {
+        keep <- colnames(sce) %in% rownames(colData)[colData$retained]
+        sce <- sce[,keep]
+        colData$retained <- NULL
+    } else if (identical(filtered, "cells")) {
+        keep <- colnames(sce) %in% rownames(colData)
+        sce <- sce[,keep]
+    }
+
+    # Weird performance issue when directly subsetting with rownames.
+    # Also, preserve names when filtered=FALSE, though this takes some time.
+    m <- match(colnames(sce), rownames(sce))
+    colData <- colData[m,, drop = FALSE]
+    rownames(colData) <- colnames(sce)
+    colData(sce) <- colData
+
+    sce
+}

inst/extdata/manifest.csv

@@ -6,6 +6,7 @@ Reference,Taxonomy,Part,Number,Call
 @baron2016singlecell,10090,pancreas,1886,BaronPancreasData('mouse')
 @bhaduri2020cell,9606,cortical organoids,242349,BhaduriOrganoidData()
 @buettner2015computational,10090,embryonic stem cells,288,BuettnerESCData()
+@bunis2021haematopoietic,9606,haematopoietic stem and progenitor,5183,BunisHSPCData()
 @campbell2017molecular,10090,brain,21086,CampbellBrainData()
 @chen2017singlecell,10090,brain,14437,ChenBrainData()
 @darmanis2015survey,9606,brain,466,DarmanisBrainData()

longtests/testthat/test-bunis-hspc.R

@@ -0,0 +1,17 @@
+# library(testthat); library(scRNAseq); source('test-bunis-hspc.R')
+
+test_that("Bunis HSPC getter works as expected", {
+  sce <- BunisHSPCData()
+  expect_s4_class(sce, "SingleCellExperiment")
+
+  sce2 <- BunisHSPCData(filtered = "cells")
+  expect_s4_class(sce2, "SingleCellExperiment")
+
+  sce3 <- BunisHSPCData(filtered=FALSE)
+
+  # Checks cell filtering and that colData is added all at once
+  expect_true( nrow(colData(sce)) < nrow(colData(sce2)) )
+  expect_true( nrow(colData(sce2)) < nrow(colData(sce3)) )
+
+  expect_true(all(grepl("^ENSG", rownames(sce))))
+})

tests/testthat/test-loading.R

@@ -21,6 +21,10 @@ test_that("BuettnerESCData works", {
     expect_s4_class(rowRanges(out), "GRangesList")
 })
 
+test_that("BunisHSPCData works", {
+    CHECK(BunisHSPCData())
+})
+
 test_that("CampbellBrainData works", {
     CHECK(CampbellBrainData(ensembl=TRUE))
 })