dataset_merge | R Documentation |
The objects need to have the same number of assays in order to work.
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
)
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 |
numeric, is only applied if |
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 |
SummarizedExperiment object
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))
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