Convert a loom file to a Seurat object — LoomAsSeurat • LoadSciLooms

This function loads a transcriptome loom file and converts it into a Seurat object wrapped inside an lseurat container. It supports optional Gaussian mixture model (GMM)–based cell calling, automatic resolution of duplicate feature names, and annotation of cells using Odd-barcode–linked metadata.

Usage

LoomAsSeurat(
  loom_path,
  matrix_rowname_col = "Gene",
  matrix_colname_col = "CellID",
  gmm_cell_calling = FALSE,
  gmm_k = 1:6,
  gmm_verbose = TRUE,
  resolve_duplicates = FALSE,
  Add_Odd_bc_md = FALSE,
  Odd_barcode_md_file = NULL,
  full_barcode_col = "BC",
  condition_col = "condition",
  Odd_barcode_col = "Odd_barcode",
  Odd_barcode_position = 2,
  seurat_assay_name = "RNA",
  seurat_min_cells = 0,
  seurat_names_field = 1L,
  seurat_names_delim = "_",
  ...
)

Arguments

loom_path: A single-element character vector specifying the path to the transcriptome loom file.
matrix_rowname_col: A single-element character vector indicating the column in the row (gene) metadata of the loom file that should be used as feature names for the count matrix.
matrix_colname_col: A single-element character vector indicating the column in the column (cell) metadata of the loom file that should be used as cell names for the count matrix.
gmm_cell_calling: Logical value indicating whether GMM-based cell calling should be performed. Default is FALSE.
gmm_k: Integer vector specifying the number(s) of mixture components to test. The model with the lowest BIC is selected. Default is 1:6.
gmm_verbose: Logical value indicating whether information about the selected GMM (BIC and number of components) should be printed.
resolve_duplicates: Logical value indicating whether duplicate feature names should be resolved automatically. If TRUE, only the first occurrence of each duplicated feature is retained. If FALSE, the function errors when duplicates are detected. Default is FALSE.
Add_Odd_bc_md: Logical value indicating whether Odd-barcode metadata annotation should be performed. Default is FALSE.
Odd_barcode_md_file: A comma-separated file linking Odd barcode IDs to metadata (e.g. treatment or condition). Must contain at least two columns: one with Odd barcode identifiers and one with the corresponding condition.
full_barcode_col: Character string specifying the column in the Seurat metadata that contains the full cell barcode.
condition_col: Character string specifying the name of the metadata column in which to store condition labels.
Odd_barcode_col: Character string specifying the name of the metadata column in which to store extracted Odd barcodes.
Odd_barcode_position: Integer specifying the position of the Odd barcode after splitting the full barcode on underscores ("_").
seurat_assay_name: A single-element character vector specifying the name of the assay in the created Seurat object.
seurat_min_cells: Include only features detected in at least this many cells.
seurat_names_field: Integer specifying which substring of the cell name (after splitting by seurat_names_delim) is used as the sample name.
seurat_names_delim: A single-element character vector specifying the delimiter used to split cell names when extracting sample names.
...: Additional arguments passed to SeuratObject::CreateSeuratObject().

Value

An object of class lseurat. This is a named list containing:

seurat: A Seurat object containing the count matrix and metadata derived from the loom file.
params: A named list of parameters supplied to LoomAsSeurat().
gmm: The Mclust object used for cell calling, or NA if GMM-based cell calling was not performed.

Loom file input

Arguments: loom_path, matrix_rowname_col, matrix_colname_col.

Control how the loom file is read and how features and cells are identified.

Seurat object construction

Arguments: seurat_assay_name, seurat_min_cells, seurat_names_field, seurat_names_delim, ....

Control creation, filtering, and naming of the Seurat object.

Duplicate feature handling

Arguments: resolve_duplicates.

Control how duplicate feature names are detected and resolved.

GMM-based cell calling

Arguments: gmm_cell_calling, gmm_k, gmm_verbose.

Control Gaussian mixture model–based classification of cells.

Odd-barcode metadata annotation

Arguments: Add_Odd_bc_md, Odd_barcode_md_file, full_barcode_col, condition_col, Odd_barcode_col, Odd_barcode_position.

Control annotation of cells using Odd-barcode–linked metadata.

Examples

# Example 1: basic loom loading
lpath <- system.file("extdata", "i1_ES_subsample.loom",
  package = "LoadSciLooms")
lseurat <- LoomAsSeurat(
  lpath,
  matrix_rowname_col = "Gene",
  matrix_colname_col = "CellID",
  resolve_duplicates = FALSE,
  seurat_assay_name = "RNA",
  seurat_min_cells = 0,
  seurat_names_field = 1L,
  seurat_names_delim = "_"
)

# Example 2: loom loading with Odd-barcode metadata annotation
loom_file <- system.file("extdata", "i31_GSK126_subsample.loom",
  package = "LoadSciLooms")
odd_md_file <- system.file("extdata", "Odd_barcode_md.csv",
  package = "LoadSciLooms")
lseurat_odd <- LoomAsSeurat(
  loom_path = loom_file,
  matrix_rowname_col = "Gene",
  matrix_colname_col = "CellID",
  resolve_duplicates = FALSE,
  Add_Odd_bc_md = TRUE,
  Odd_barcode_md_file = odd_md_file,
  full_barcode_col = "BC"
)

# Resulting Seurat metadata will contain Odd-barcode metadata
head(lseurat_odd$seurat[[c("Odd_barcode", "condition")]])
#>                                  Odd_barcode   condition
#> i31-FV-GSK126-1K_multiome_100859    Odd2Bo60 Treatment_B
#> i31-FV-GSK126-1K_multiome_101271    Odd2Bo88 Treatment_C
#> i31-FV-GSK126-1K_multiome_102301    Odd2Bo62 Treatment_B
#> i31-FV-GSK126-1K_multiome_102545    Odd2Bo18        DMSO
#> i31-FV-GSK126-1K_multiome_103754    Odd2Bo75 Treatment_C
#> i31-FV-GSK126-1K_multiome_104        Odd2Bo9        DMSO