generate_summarized_experiment: Generate SummarizedExperiment using multiple parameters
In omnideconv/SimBu: Simulate Bulk RNA-seq Datasets from Single-Cell Datasets

generate_summarized_experiment

R Documentation

Generate SummarizedExperiment using multiple parameters

Description

Generate SummarizedExperiment using multiple parameters

Usage

generate_summarized_experiment(
  annotation,
  count_matrix,
  tpm_matrix,
  name,
  spike_in_col,
  additional_cols,
  filter_genes,
  variance_cutoff,
  type_abundance_cutoff,
  scale_tpm
)

Arguments

`annotation`	(mandatory) dataframe; needs columns 'ID' and 'cell_type'; 'ID' needs to be equal with cell_names in count_matrix
`count_matrix`	(mandatory) sparse count matrix; raw count data is expected with genes in rows, cells in columns
`tpm_matrix`	sparse count matrix; TPM like count data is expected with genes in rows, cells in columns
`name`	name of the dataset; will be used for new unique IDs of cells
`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_genes` is 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