LoomLoading: Loom-file Loading
In mojaveazure/seurat-disk: Interfaces for HDF5-Based Single Cell File Formats

LoomLoading

R Documentation

Loom-file Loading

Description

Version-specific loom-file loading functions

Usage

LoadLoom0.1(
  file,
  assay = NULL,
  cells = "col_atts/CellID",
  features = "row_attrs/Gene",
  normalized = NULL,
  scaled = NULL,
  filter = c("cells", "features", "all", "none"),
  verbose = TRUE
)

LoadLoom3.0(
  file,
  assay = NULL,
  cells = "col_attrs/CellID",
  features = "row_attrs/Gene",
  normalized = NULL,
  scaled = NULL,
  filter = c("cells", "features", "all", "none"),
  verbose = TRUE
)

Arguments

`assay`	Name of assay to store expression data as; if `NULL`, will search for an HDF5 attribute named `SEURAT_ASSAY` or an attribute dataset named `/attrs/SEURAT_ASSAY` for assay name. If not found, defaults to “RNA”
`cells`	Name of dataset in `/col_attrs` with cell names
`features`	Name of dataset in `/row_attrs` with feature names
`normalized`	Name of matrix in `/layers` to store normalized data as; pass “/matrix” to store `/matrix` as normalized data instead of raw counts
`scaled`	Name of dataset in `/layers` to store scaled data as
`filter`	Keep only selected cells and/or features as specified by `/col_attrs/Valid` and `/row_attrs/Valid`, respectively
`verbose`	Show progress updates

Details

LoadLoom will try to automatically fill slots of a Seurat object based on data presence or absence in a given loom file. This method varies by loom specification version. For version-specific details, see sections below