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Load in data directly from R objects.

load_r_data.Rd

This function loads in data directly from R objects. The counts matrix must be a dgRMatrix (rows = cells, columns = genes).

Usage

load_r_data(
  object,
  counts,
  obs,
  var,
  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.

counts

Sparse matrix. The cells represent the rows, the genes the columns. Needs to be a "dgRMatrix".

obs

data.table. Cell metadata. Must have one row per cell in the same order as counts.

var

data.table. Feature metadata. Must have one row per gene in the same order as 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

# straight from a dgRMatrix in memory onto disk
data <- generate_single_cell_test_data(
  syn_data_params = params_sc_synthetic_data(n_cells = 200L, n_genes = 40L)
)
dir_data <- tempfile("sc_r_data")
dir.create(dir_data, recursive = TRUE)
sc <- load_r_data(
  object = SingleCells(dir_data = dir_data),
  counts = data$counts,
  obs = data$obs,
  var = data$var,
  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)