exportFromSeurat: Export Seurat object to Cerebro.
In romanhaa/cerebroApp: Interactive visualization of scRNA-seq data with Cerebro

Description Usage Arguments Value Examples

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
)

`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`.

No data returned.

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
)