
Load multiple h5ad files into a single SingleCells
load_multi_h5ad.RdTakes a pre-scan result from prescan_h5ad_files() and loads
all files into a single experiment with global gene QC and sequential
cell indexing.
Usage
load_multi_h5ad(
object,
prescan_result,
sc_qc_param = params_sc_min_quality(),
cell_id_col = NULL,
streaming = 1L,
batch_size = 1000L,
max_genes_in_memory = 2000L,
cell_batch_size = 100000L,
.verbose = TRUE
)Arguments
- object
SingleCellsclass.- prescan_result
Output of
prescan_h5ad_files().- sc_qc_param
List. Output of
params_sc_min_quality().- cell_id_col
Optional string. Column name for cell identifiers in obs.
- streaming
Integer.
0L-> in-memory,1L-> light streaming,2L-> heavy streaming with memory upper boundaries. Defaults to1L.- batch_size
Integer. Cell batch size when
streaming = 1L. Defaults to1000L.- max_genes_in_memory
Integer. Maximum genes held in memory at once when
streaming = 2L. Defaults to2000L.- cell_batch_size
Integer. Cell batch size when
streaming = 2L. Defaults to100000L.- .verbose
Boolean.
Examples
# two files into one experiment, cells tagged by exp_id
files <- c(a = tempfile(fileext = ".h5ad"), b = tempfile(fileext = ".h5ad"))
for (i in seq_along(files)) {
data <- generate_single_cell_test_data(
syn_data_params = params_sc_synthetic_data(
n_cells = 200L,
n_genes = 40L
),
seed = i
)
write_h5ad_sc(files[i], data$counts, data$obs, data$var, .verbose = FALSE)
}
tasks <- prescan_h5ad_files(h5_paths = files, .verbose = FALSE)
dir_data <- tempfile("sc_multi_h5ad")
dir.create(dir_data, recursive = TRUE)
sc <- load_multi_h5ad(
object = SingleCells(dir_data = dir_data),
prescan_result = tasks,
sc_qc_param = params_sc_min_quality(
min_unique_genes = 5L,
min_lib_size = 25L,
min_cells = 5L
),
streaming = 0L,
.verbose = FALSE
)
table(sc[["exp_id"]])
#> exp_id
#> a b
#> 200 200
unlink(c(files, dir_data), recursive = TRUE, force = TRUE)