Save a Seurat object to a Loom file

Export a Seurat object to Loom format (HDF5-based file format optimized for storing annotated matrices). Loom files are compatible with the loompy Python package and other tools in the bioinformatics community. This format is useful for sharing data with Python-based analysis workflows or archiving analysis results.

Usage

SaveLoom(object, filename, overwrite = FALSE, verbose = TRUE, ...)

as.loom(x, ...)

# Default S3 method
SaveLoom(object, filename, overwrite = FALSE, verbose = TRUE, ...)

# S3 method for class 'Seurat'
SaveLoom(
  object,
  filename = paste0(Project(object = object), ".loom"),
  overwrite = FALSE,
  verbose = TRUE,
  ...
)

# Default S3 method
as.loom(x, filename, overwrite = FALSE, verbose = TRUE, ...)

# S3 method for class 'H5File'
as.loom(x, ...)

# S3 method for class 'Seurat'
as.loom(
  x,
  filename = paste0(Project(object = x), ".loom"),
  overwrite = FALSE,
  verbose = TRUE,
  ...
)

Arguments

object: A Seurat object to save
filename: Name of file to save the object to. If not provided, defaults to <ProjectName>.loom. The .loom extension is added automatically if not present.
overwrite: Logical; if TRUE, overwrite an existing file. Default is FALSE.
verbose: Logical; if TRUE (default), show progress updates
...: Arguments passed to other methods

Value

SaveLoom: Invisibly returns the filename of the saved file

as.loom: A loom object

Details

The Loom format organizes data as follows:

/matrix: Main expression matrix (features x cells)
/row_attrs: Feature/gene-level annotations
/col_attrs: Cell/sample-level metadata (cell names, cluster assignments, etc.)
/layers: Additional expression layers if present

When saving a Seurat object:

Default assay data becomes the main matrix
Cell metadata and feature annotations are preserved
Dimensional reductions are stored in col_attrs
The SEURAT_ASSAY attribute stores the assay name for roundtrip loading

Examples

if (FALSE) { # \dontrun{
library(Seurat)
library(SeuratDisk)

# Create a Seurat object (or use an existing one)
seurat_obj <- CreateSeuratObject(counts = pbmc_small$RNA@counts)

# Save to Loom format
SaveLoom(seurat_obj, filename = "my_data.loom")

# Save with overwrite if needed
SaveLoom(seurat_obj, filename = "my_data.loom", overwrite = TRUE)

# Load it back
loaded_obj <- LoadLoom("my_data.loom")

# For sharing with Python tools
SaveLoom(seurat_obj, filename = "data_for_python.loom")
# Use in Python with: adata = loompy.connect("data_for_python.loom")
} # }