Export Seurat object to Cerebro.

This function allows to export a Seurat object to visualize in Cerebro.

exportFromSeurat(
  object,
  assay = "RNA",
  slot = "data",
  file,
  experiment_name,
  organism,
  groups,
  cell_cycle = NULL,
  nUMI = "nUMI",
  nGene = "nGene",
  add_all_meta_data = TRUE,
  use_delayed_array = FALSE,
  verbose = FALSE
)

Arguments

object	Seurat object.
assay	Assay to pull expression values from; defaults to RNA.
slot	Slot to pull expression values from; defaults to data. It is recommended to use sparse data (such as log-transformed or raw counts) instead of dense data (such as the scaled slot) to avoid performance bottlenecks in the Cerebro interface.
file	Where to save the output.
experiment_name	Experiment name.
organism	Organism, e.g. hg (human), mm (mouse), etc.
groups	Names of grouping variables in meta data (object@meta.data), e.g. c("sample","cluster"); at least one must be provided; defaults to NULL.
cell_cycle	Names of columns in meta data (object@meta.data) that contain cell cycle information, e.g. c("Phase"); defaults to NULL.
nUMI	Column in object@meta.data that contains information about number of transcripts per cell; defaults to nUMI.
nGene	Column in object@meta.data that contains information about number of expressed genes per cell; defaults to nGene.
add_all_meta_data	If set to TRUE, all further meta data columns will be extracted as well.
use_delayed_array	When set to TRUE, the expression matrix will be stored as an RleMatrix (see DelayedArray package). This can be useful for very large data sets, as the matrix won't be loaded into memory and instead values will be read from the disk directly, at the cost of performance. Note that it is necessary to install the DelayedArray package. If set to FALSE (default), the expression matrix will be copied from the input object as is. It is recommended to use a sparse format, such as dgCMatrix from the Matrix package.
verbose	Set this to TRUE if you want additional log messages; defaults to FALSE.

Value

No data returned.

Examples

pbmc <- readRDS(system.file("extdata/v1.3/pbmc_seurat.rds",
  package = "cerebroApp"))
exportFromSeurat(
  object = pbmc,
  file = 'pbmc_Seurat.crb',
  experiment_name = 'PBMC',
  organism = 'hg',
  groups = c('sample','seurat_clusters'),
  nUMI = 'nCount_RNA',
  nGene = 'nFeature_RNA',
  use_delayed_array = FALSE,
  verbose = TRUE
)
#> [17:53:24] Initializing Cerebro object...
#> [17:53:24] Collecting available meta data...
#> [17:53:24] Extracting all meta data columns...
#> [17:53:24] Extracting dimensional reductions...
#> [17:53:24] Will export the following dimensional reductions: UMAP
#> [17:53:24] Extracting tables of marker genes...
#> [17:53:24] No trajectories to extract...
#> [17:53:24] Overview of Cerebro object:
#> class: Cerebro_v1.3
#> cerebroApp version: 1.3.1
#> experiment name: PBMC
#> organism: hg
#> date of analysis: 
#> date of export: 2021-03-12
#> number of cells: 80
#> number of genes: 230
#> grouping variables (2): sample, seurat_clusters
#> cell cycle variables (0): 
#> projections (1): UMAP
#> trees (0): 
#> most expressed genes: 
#> marker genes:
#>   - cerebro_seurat (2): sample, seurat_clusters
#> enriched pathways:
#> trajectories:
#> extra material:
#> [17:53:24] Saving Cerebro object to: pbmc_Seurat.crb
#> [17:53:24] Done!