Load data from PXL file into a Seurat object

This wrapper function can be used to load data from a PXL file, and returns a Seurat object.

Usage

ReadMPX_Seurat(
  filename,
  assay = "mpxCells",
  return_cellgraphassay = TRUE,
  load_cell_graphs = FALSE,
  load_polarity_scores = TRUE,
  load_colocalization_scores = TRUE,
  add_additional_assays = FALSE,
  edgelist_outdir = NULL,
  overwrite = FALSE,
  verbose = TRUE,
  ...
)

Arguments

filename

Path to a .pxl file

assay

Assay name

return_cellgraphassay

Should data be loaded as a CellGraphAssay object?

load_cell_graphs

Should the cellgraphs be loaded into the CellGraphAssay object?

load_polarity_scores, load_colocalization_scores

Logical specifying if the polarity and colocalization scores should be loaded. These parameters only have an effect if return_cellgraphassay = TRUE.

add_additional_assays

If other matrix representations are stored in the PXL file, for instance CLR-normalized counts or denoised, set this parameter to TRUE to load these in separate Assays.

edgelist_outdir

A directory where the edgelist should be stored

overwrite

Should edgelist_outdir be overwritten?

verbose

Print messages

...

Additional parameters passed to CreateSeuratObject

Value

An object of class Seurat

Details

By default, the MPX count matrix is returned in a CellGraphAssay object. Graphs are not loaded directly unless load_cell_graphs = TRUE. Graphs can also be loaded at a later stage with LoadCellGraphs.

When setting the global option Seurat.object.assay.version to "v5", the function will return a CellGraphAssay5 object instead.

See also

Other data-loaders: ReadMPX_item()

Examples

library(pixelatorR)

# Load example data as a Seurat object
pxl_file <- system.file("extdata/five_cells",
  "five_cells.pxl",
  package = "pixelatorR"
)
seur_obj <- ReadMPX_Seurat(pxl_file)
#> ✔ Created a 'Seurat' object with 5 cells and 80 targeted surface proteins
seur_obj
#> An object of class Seurat 
#> 80 features across 5 samples within 1 assay 
#> Active assay: mpxCells (80 features, 80 variable features)
#>  2 layers present: counts, data