replace equals signs

episodes/cell_type_annotation.Rmd

@@ -650,7 +650,7 @@ Use `BiocParallel` and the `BPPARAM` argument! This example will set it to use f
 
 library(BiocParallel)
 
-my_bpparam = MulticoreParam(workers = 4)
+my_bpparam <- MulticoreParam(workers = 4)
 
 res2 <- SingleR(test = sce.mat, 
                 ref = ref.mat,

episodes/eda_qc.Rmd

@@ -72,8 +72,8 @@ bcrank <- barcodeRanks(counts(sce))
 # Only showing unique points for plotting speed.
 uniq <- !duplicated(bcrank$rank)
 
-line_df = data.frame(cutoff = names(metadata(bcrank)),
-                     value  = unlist(metadata(bcrank)))
+line_df <- data.frame(cutoff = names(metadata(bcrank)),
+                      value  = unlist(metadata(bcrank)))
 
 ggplot(bcrank[uniq,], aes(rank, total)) + 
     geom_point() + 
@@ -92,6 +92,8 @@ A simple approach would be to apply a threshold on the total count to only retai
 
 ::: callout
 Depending on your data source, identifying and discarding empty droplets may not be necessary. Some academic institutions have research cores dedicated to single cell work that perform the sample preparation and sequencing. Many of these cores will also perform empty droplet filtering and other initial QC steps. If the sequencing outputs were provided to you by someone else, make sure to communicate with them about what pre-processing steps have been performed, if any.
+
+<!-- TODO: cite official 10x CellRanger docs -->
 :::
 
 :::: challenge 
@@ -671,7 +673,7 @@ e.out <- emptyDrops(counts(sce))
 sce <- sce[,which(e.out$FDR <= 0.001)]
 
 # Thankfully the data come with gene symbols, which we can use to identify mitochondrial genes:
-is.mito = grepl("^MT-", rowData(sce)$Symbol) 
+is.mito <- grepl("^MT-", rowData(sce)$Symbol) 
 
 # QC metrics ----
 df <- perCellQCMetrics(sce, subsets = list(Mito = is.mito))

episodes/hca.Rmd

@@ -179,7 +179,7 @@ data.
 For the sake of demonstration, we'll focus this small subset of samples:
 
 ```{r}
-sample_subset = metadata |>
+sample_subset <- metadata |>
     filter(
         ethnicity == "African" &
         grepl("10x", assay) &

episodes/intro-sce.Rmd

@@ -234,9 +234,9 @@ The `SingleCellExperiment` constructor function can be used to create a new `Sin
 ::: solution
 
 ```{r}
-mat = matrix(runif(30), ncol = 5)
+mat <- matrix(runif(30), ncol = 5)
 
-my_sce = SingleCellExperiment(assays = list(logcounts = mat))
+my_sce <- SingleCellExperiment(assays = list(logcounts = mat))
 
 my_sce$my_col_info = runif(5)
 
@@ -260,7 +260,7 @@ sce  <- WTChimeraData(samples = 5)
 
 sce6 <- WTChimeraData(samples = 6)
 
-combined_sce = cbind(sce, sce6)
+combined_sce <- cbind(sce, sce6)
 
 combined_sce
 ```

episodes/large_data.Rmd

@@ -388,8 +388,8 @@ reducedDim(r.out, "PCA") = -1 * reducedDim(r.out, "PCA")
 From there we can visualize the error with a histogram:
 
 ```{r}
-error = reducedDim(r.out, "PCA")[,"PC1"] - 
-        reducedDim(e.out, "PCA")[,"PC1"]
+error <- reducedDim(r.out, "PCA")[,"PC1"] - 
+         reducedDim(e.out, "PCA")[,"PC1"]
 
 data.frame(approx_error = error) |> 
   ggplot(aes(approx_error)) + 
@@ -569,15 +569,15 @@ function for writing to HDF5 from the `r Biocpkg("HDF5Array")` package.
 
 ```{r}
 
-wt_out = tempfile(fileext = ".h5")
+wt_out <- tempfile(fileext = ".h5")
 
-wt_counts = counts(WTChimeraData())
+wt_counts <- counts(WTChimeraData())
 
 writeHDF5Array(wt_counts,
                name = "wt_counts",
                file = wt_out)
 
-oom_wt = HDF5Array(wt_out, "wt_counts")
+oom_wt <- HDF5Array(wt_out, "wt_counts")
 
 object.size(wt_counts)
 
@@ -607,7 +607,7 @@ Use the function `system.time` to obtain the runtime of each job.
 
 ```{r eval=FALSE}
 
-sce.brain = logNormCounts(sce.brain)
+sce.brain <- logNormCounts(sce.brain)
 
 system.time({i.out <- runPCA(sce.brain, 
                              ncomponents = 20,