The objects need to have the same number of assays in order to work.
Usage
dataset_merge(
dataset_list,
name = "SimBu_dataset",
spike_in_col = NULL,
additional_cols = NULL,
filter_genes = TRUE,
variance_cutoff = 0,
type_abundance_cutoff = 0,
scale_tpm = TRUE
)Arguments
- dataset_list
(mandatory) list of SummarizedExperiment objects
- name
name of the new dataset
- spike_in_col
which column in annotation contains information on spike_in counts, which can be used to re-scale counts; mandatory for spike_in scaling factor in simulation
- additional_cols
list of column names in annotation, that should be stored as well in dataset object
- filter_genes
boolean, if TRUE, removes all genes with 0 expression over all samples & genes with variance below
variance_cutoff- variance_cutoff
numeric, is only applied if
filter_genesis TRUE: removes all genes with variance below the chosen cutoff- type_abundance_cutoff
numeric, remove all cells, whose cell-type appears less then the given value. This removes low abundant cell-types
- scale_tpm
boolean, if TRUE (default) the cells in tpm_matrix will be scaled to sum up to 1e6
Value
SummarizedExperiment object
Examples
counts <- Matrix::Matrix(matrix(stats::rpois(3e5, 5), ncol = 300), sparse = TRUE)
tpm <- Matrix::Matrix(matrix(stats::rpois(3e5, 5), ncol = 300), sparse = TRUE)
tpm <- Matrix::t(1e6 * Matrix::t(tpm) / Matrix::colSums(tpm))
colnames(counts) <- paste0("cell_", rep(1:300))
colnames(tpm) <- paste0("cell_", rep(1:300))
rownames(counts) <- paste0("gene_", rep(1:1000))
rownames(tpm) <- paste0("gene_", rep(1:1000))
annotation <- data.frame(
"ID" = paste0("cell_", rep(1:300)),
"cell_type" = c(rep("T cells CD4", 300))
)
ds1 <- SimBu::dataset(annotation = annotation, count_matrix = counts, tpm_matrix = tpm, name = "test_dataset1")
ds2 <- SimBu::dataset(annotation = annotation, count_matrix = counts, tpm_matrix = tpm, name = "test_dataset2")
ds_merged <- SimBu::dataset_merge(list(ds1, ds2))