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Load in data from a SingleCellExperiment

load_sce.Rd

Brings a Bioconductor SingleCellExperiment into a SingleCells object. colData becomes the obs table, rowData becomes the var table, and the chosen assay goes through the same Rust quality control and normalisation every other loader uses.

The assay has to hold raw counts. Plenty of objects in the wild ship only logcounts, and a negative binomial cannot model those, so pick the right one rather than letting the default find whatever is there.

reducedDims and altExps are not carried over. Run the embedding on this side, and use SingleCellsMultiModal() for ADT.

Usage

load_sce(
  object,
  sce,
  assay_name = "counts",
  sc_qc_param = params_sc_min_quality(),
  streaming = 1L,
  batch_size = 1000L,
  max_genes_in_memory = 2000L,
  cell_batch_size = 100000L,
  .verbose = TRUE
)

Arguments

object

SingleCells class.

sce

SingleCellExperiment class you want to transform.

assay_name

String. Which assay holds the raw counts. Defaults to "counts".

sc_qc_param

List. Output of params_sc_min_quality(). A list with the following elements:

  • min_unique_genes - Integer. Minimum number of genes to be detected in the cell to be included.

  • min_lib_size - Integer. Minimum library size in the cell to be included.

  • min_cells - Integer. Minimum number of cells a gene needs to be detected to be included.

  • target_size - Float. Target size to normalise to. Defaults to 1e5.

streaming

Integer. CSR-to-CSC conversion mode. 0L -> in-memory (fastest, highest memory), 1L -> light streaming with cell batching, 2L -> heavy streaming with memory upper boundaries. Defaults to 1L.

batch_size

Integer. Cell batch size when streaming = 1L. Defaults to 1000L.

max_genes_in_memory

Integer. Maximum genes held in memory at once when streaming = 2L. Defaults to 2000L.

cell_batch_size

Integer. Cell batch size when streaming = 2L. Defaults to 100000L.

.verbose

Boolean. Controls the verbosity of the function.

Value

It will populate the files on disk and return the class with updated shape information.

Examples

# \donttest{
# colData becomes obs, rowData becomes var, the counts assay gets normalised
data <- generate_single_cell_test_data(
  syn_data_params = params_sc_synthetic_data(n_cells = 200L, n_genes = 40L)
)
sce <- SingleCellExperiment::SingleCellExperiment(
  assays = list(counts = as(Matrix::t(data$counts), "CsparseMatrix")),
  colData = data.frame(data$obs, row.names = data$obs$cell_id),
  rowData = data.frame(data$var, row.names = data$var$gene_id)
)
dir_data <- tempfile("sc_sce")
dir.create(dir_data, recursive = TRUE)
sc <- load_sce(
  object = SingleCells(dir_data = dir_data),
  sce = sce,
  sc_qc_param = params_sc_min_quality(
    min_unique_genes = 5L,
    min_lib_size = 25L,
    min_cells = 5L
  ),
  streaming = 0L,
  .verbose = FALSE
)
dim(sc)
#> [1] 200  40

unlink(dir_data, recursive = TRUE, force = TRUE)
# }