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Load in Seurat to SingleCells

load_seurat.Rd

This function takes a Seurat object and generates a SingleCells class from it. The raw counts are extracted, written to the Rust binary format, and the metadata is loaded into the DuckDB.

Usage

load_seurat(
  object,
  seurat,
  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.

seurat

Seurat class you want to transform.

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{
# a Seurat object holds genes x cells, the loader transposes it
data <- generate_single_cell_test_data(
  syn_data_params = params_sc_synthetic_data(n_cells = 200L, n_genes = 40L)
)
seurat_obj <- Seurat::CreateSeuratObject(
  counts = Matrix::t(data$counts),
  meta.data = data.frame(data$obs, row.names = data$obs$cell_id)
)
#> Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
#> Warning: Data is of class dgRMatrix. Coercing to dgCMatrix.
dir_data <- tempfile("sc_seurat")
dir.create(dir_data, recursive = TRUE)
sc <- load_seurat(
  object = SingleCells(dir_data = dir_data),
  seurat = seurat_obj,
  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)
# }