Bioconductor reviewer update #1

DESCRIPTION

@@ -33,14 +33,14 @@ Suggests:
     testthat (>= 3.0.0),
     BiocSingular,
     bluster,
-    qs,
     scater,
     scran,
     Seurat,
     SingleCellExperiment,
-    BiocStyle
+    BiocStyle,
+    magick,
+    qs2
 VignetteBuilder: knitr
 Config/testthat/edition: 3
 URL: https://phipsonlab.github.io/SuperCellCyto/
 BugReports: https://github.com/phipsonlab/SuperCellCyto/issues
-

R/runSuperCellCyto.R

@@ -257,7 +257,7 @@ runSuperCellCyto <- function(
     if (n_pc > length(markers)) {
         warning(
             sprintf(
-                "n_pc (%d) > number of markers (%d). Setting n_pc to %d.",
+                "Requested n_pc (%d) > markers (%d). Setting n_pc to %d.",
                 n_pc, length(markers), length(markers)
             )
         )

inst/README.md

@@ -0,0 +1,21 @@
+# README
+
+Data in `extdata` folder are obtained from the following sources:
+
+* `Data23_Panel3_base_NR4_Patient9.fcs` and 
+`Data23_Panel3_base_R5_Patient15.fcs` are mass cytometry data from anti-PD1 
+  immunotherapy study by [Krieg et. al](https://doi.org/10.1038/nm.4466).
+  FCS files are available from 
+  [FlowRepository](
+  http://flowrepository.org/public_experiment_representations/1124).
+* `Levine_32dim_H1_sub.csv` and `Levine_32dim_H2_sub.csv` are mass cytometry
+  data from AML study by 
+  [Levine et. al](https://doi.org/10.1016/j.cell.2015.05.047).
+* `Levine_32dim_seurat_sub.qs2` and `Levine_32dim_sce_sub.qs2` are
+  Seurat object and SingleCellExperiment object containing subsampled
+  mass cytometry data from AML study by 
+  [Levine et. al](https://doi.org/10.1016/j.cell.2015.05.047).
+
+Scripts to generate the different formats of `Levine_32dim` dataset is stored 
+in the `inst/scripts` directory of 
+[SuperCellCyto](https://github.com/phipsonlab/SuperCellCyto) repo.

inst/scripts/make_levine_32dim.R

@@ -0,0 +1,66 @@
+library(HDCytoData)
+library(data.table)
+library(qs2)
+
+
+sce <- Levine_32dim_SE()
+
+# isolate data from H1 and H2 patients
+sce_H1 <- sce[rowData(sce)$patient_id == "H1", ]
+sce_H2 <- sce[rowData(sce)$patient_id == "H2", ]
+
+# remove unassigned
+sce_H1 <- sce_H1[rowData(sce_H1)$population_id != "unassigned", ]
+sce_H2 <- sce_H2[rowData(sce_H2)$population_id != "unassigned", ]
+
+# randomly select 200 cells from each patient
+set.seed(42)
+sce_H1_sub <- sce_H1[sample(nrow(sce_H1), 200), ]
+sce_H2_sub <- sce_H2[sample(nrow(sce_H2), 200), ]
+
+# convert to data.table
+dt_H1_sub <- data.table(assay(sce_H1_sub, "exprs"))
+dt_H2_sub <- data.table(assay(sce_H2_sub, "exprs"))
+
+# save them as csv files
+fwrite(dt_H1_sub, "inst/extdata/Levine_32dim_H1_sub.csv")
+fwrite(dt_H2_sub, "inst/extdata/Levine_32dim_H2_sub.csv")
+
+
+# create the sce object
+dt <- data.table(assay(sce, "exprs"))
+dt[, population_id := rowData(sce)$population_id]
+dt[, sample := rowData(sce)$patient_id]
+
+# get 50 cells from each population and sample
+set.seed(42)
+n_cells <- 50
+sampled_dt <- dt[, .SD[sample(.N, min(.N, n_cells))], by = c("population_id", "sample")]
+
+# assign cell id
+sampled_dt[, cell_id := paste0("cell_", .I)]
+
+# create SCE object
+exprs_mat <- t(as.matrix(sampled_dt[, !c("population_id", "sample", "cell_id"), with = FALSE]))
+sce_sampled <- SingleCellExperiment::SingleCellExperiment(assays = list(counts = exprs_mat))
+SummarizedExperiment::colData(sce_sampled)$population <- sampled_dt$population_id
+SummarizedExperiment::colData(sce_sampled)$sample <- sampled_dt$sample
+SummarizedExperiment::colData(sce_sampled)$cell_id <- sampled_dt$cell_id
+colnames(sce_sampled) <- sampled_dt$cell_id
+
+qs_save(sce_sampled, "inst/extdata/Levine_32dim_sce_sub.qs2")
+
+
+# create Seurat object
+seurat_obj <- Seurat::CreateSeuratObject(
+    counts = exprs_mat,
+    assay = "originalexp",
+)
+seurat_obj <- Seurat::AddMetaData(seurat_obj, metadata = sampled_dt[, .(population_id, sample, cell_id)])
+# rename patient_id to sample
+
+
+qs_save(seurat_obj, "inst/extdata/Levine_32dim_seurat_sub.qs2")
+
+
+

vignettes/how_to_create_supercells.Rmd → vignettes/SuperCellCyto.Rmd

@@ -27,6 +27,42 @@ library(BiocParallel)
 This vignette describes the steps to generate supercells for 
 cytometry data using SuperCellCyto R package.
 
+Briefly, supercells are "mini" clusters of cells that are similar in their 
+marker expressions. 
+The motivation behind supercells is that instead of analysing millions of 
+individual cells, you can analyse thousands of supercells, 
+making downstream analysis much faster while maintaining 
+biological interpretability.
+
+
+See other vignettes for how to:
+
+* [Prepare your data for SuperCellCyto](how_to_prepare_data.html)
+* [Use SuperCellCyto for downsampling](
+    using_supercellcyto_for_stratified_summarising.html)
+* [Integrate SuperCellCyto output with SingleCellExperiment objects](
+    interoperability_with_sce.html)
+* [Integrate SuperCellCyto output with Seurat objects](
+    interoperability_with_seurat.html)
+
+## Installation
+
+You can install stable version of SuperCellCyto from Bioconductor using:
+
+```{r, eval=FALSE}
+if (!requireNamespace("BiocManager", quietly = TRUE))
+    install.packages("BiocManager")
+BiocManager::install("SuperCellCyto")
+```
+
+For the latest development version, you can install it from GitHub using `pak`:
+
+```{r, eval=FALSE}
+if (!requireNamespace("pak", quietly = TRUE))
+    install.packages("pak")
+pak::install_github("phipsonlab/SuperCellCyto")
+```
+
 ## Preparing your dataset
 
 The function which creates supercells is called `runSuperCellCyto`, and it

vignettes/how_to_prepare_data.Rmd

@@ -129,12 +129,6 @@ create supercells.
 Loading CSV files into a `data.table` object is straightforward.
 We can use the `fread` function from the `data.table` package.
 
-Here's how to install it:
-
-```{r install_datatable, eval=FALSE}
-install.packages("data.table")
-```
-
 For this example, let's load two CSV files containing subsampled data from the
 `Levine_32dim` dataset we used in SuperCellCyto manuscript.
 Each file represents a sample (H1 and H2), with the sample name appended 
@@ -143,13 +137,10 @@ to the file name:
 ```{r load_csv_data}
 library(data.table)
 
-csv_files <- c(
-    system.file(
-        "extdata", "Levine_32dim_H1_sub.csv", package = "SuperCellCyto"
-    ),
-    system.file(
-        "extdata", "Levine_32dim_H2_sub.csv", package = "SuperCellCyto"
-    )
+csv_files <- system.file(
+    "extdata",
+    c("Levine_32dim_H1_sub.csv", "Levine_32dim_H2_sub.csv"),
+    package = "SuperCellCyto"
 )
 
 samples <- c("H1", "H2")
@@ -207,7 +198,8 @@ library(data.table)
 
 fs <- read.flowSet(
     path = system.file(
-        "extdata", package = "SuperCellCyto"
+        "extdata",
+        package = "SuperCellCyto"
     ),
     pattern = "\\.fcs$"
 )
@@ -309,10 +301,14 @@ dat[, (new_cols) := lapply(.SD, function(x) asinh(x / cf)), .SDcols = markers]
 After transformation, new columns with "_asinh" appended indicate the 
 transformed markers.
 
+```{r}
+head(dat)
+```
+
 With your data now transformed, you're ready to create supercells 
 using SuperCellCyto.
 Please refer to 
-[How to create supercells](how_to_create_supercells.html) vignette 
+[How to create supercells](SuperCellCyto.html) vignette 
 for detailed instructions.
 
 ## Session information

vignettes/interoperability_with_sce.Rmd

@@ -27,7 +27,7 @@ illustrate how to create supercells and conduct downstream analyses.
 
 ```{r setup, message=FALSE, warning=FALSE}
 library(SuperCellCyto)
-library(qs)
+library(qs2)
 library(scran)
 library(BiocSingular)
 library(scater)
@@ -38,13 +38,17 @@ library(data.table)
 ## Preparing SCE object
 
 We first load the subsampled Levine_32dim data, stored as a 
-[qs](https://cran.r-project.org/web/packages/qs/index.html)
-using the `qread` function.
-
-```{r load_sce_object}
-sce <- qread(system.file(
-    "extdata", "Levine_32dim_sce_sub.qs", package = "SuperCellCyto"
-))
+[qs2](https://cran.r-project.org/web/packages/qs2/index.html)
+using the `qs_read` function.
+
+```{r load_sce_obj}
+sce <- qs_read(
+    system.file(
+        "extdata", 
+        "Levine_32dim_sce_sub.qs2", 
+        package = "SuperCellCyto"
+    )
+)
 sce
 ```
 The data is stored in the `counts` assay.

vignettes/interoperability_with_seurat.Rmd

@@ -29,7 +29,7 @@ We will show how to create supercells from this data and analyse them
 using [Seurat](https://satijalab.org/seurat/).
 
 ```{r setup, message=FALSE, warning=FALSE}
-library(qs)
+library(qs2)
 library(Seurat)
 library(data.table)
 library(SuperCellCyto)
@@ -39,15 +39,18 @@ set.seed(42)
 
 ## Preparing Seurat object
 
-The subsampled Levine_32dim data is stored as a 
-[qs](https://cran.r-project.org/web/packages/qs/index.html) object.
-We begin by loading this data using the `qread` function from the qs package.
-
+We first load the subsampled Levine_32dim data, stored as a 
+[qs2](https://cran.r-project.org/web/packages/qs2/index.html)
+using the `qs_read` function.
 
 ```{r load_seurat_obj}
-seurat_obj <- qread(system.file(
-    "extdata", "Levine_32dim_seurat_sub.qs", package = "SuperCellCyto"
-))
+seurat_obj <- qs_read(
+    system.file(
+        "extdata", 
+        "Levine_32dim_seurat_sub.qs2", 
+        package = "SuperCellCyto"
+        )
+    )
 seurat_obj
 ```
 The data is stored in the `originalexp` assay, with both counts and data 
@@ -57,7 +60,7 @@ Before running SuperCellCyto, we will first:
 
 1. Subset this data to retain only the markers we need to perform 
 downstream analysis.
-2. Perform arcsinh transformation, and store the transformed data in 
+1. Perform arcsinh transformation, and store the transformed data in 
 the `data` slot of the `originalexp` assay.
 
 ```{r subset_and_transform}

vignettes/using_supercellcyto_for_stratified_summarising.Rmd

@@ -16,7 +16,7 @@ knitr::opts_chunk$set(
 ```
 
 Have you been following the vignette on 
-[how to create supercells](how_to_create_supercells.html),
+[how to create supercells](SuperCellCyto.html),
 and wonder whether it is possible to use `SuperCellCyto` as a replacement for 
 stratified sampling to avoid overcrowding UMAP/tSNE plot?