Initiate an object of class SPATA2 from platform Visium

This function initiates a SPATA2 object with data generated using the 10x Genomics Visium platform.

initiateSpataObjectVisium(
  sample_name,
  directory_visium,
  mtr = "filtered",
  img_active = "lowres",
  img_ref = "lowres",
  verbose = TRUE
)

Arguments

sample_name

Character. The name of the sample.

directory_visium

Character. The directory containing the Visium output files.

mtr

Character. Specifies which matrix to use, either "filtered" or "raw". Default is "filtered".

img_active

Character. The active image to use, either "lowres" or "hires". Default is "lowres".

img_ref

Character. The reference image to use, either "lowres" or "hires". Default is "lowres".

verbose

Logical. If TRUE, progress messages are printed. Default is TRUE.

Value

A SPATA2 object containing data from the Visium platform. More precise, depending on the set up used to create the raw data it is of either spatial method:

  • VisiumSmall: Visium data set with capture area of 6.5mm x 6.5mm.

  • VisiumLarge: Visium data set with capture area of 11mm x 11m.

In any case, the output is an object of class SPATA2.

Details

The function requires a directory containing the output files from a 10x Genomics Visium experiment specified with the argument directory_visum. This directory (below denoted as ~) must include the following files and sub-directories:

  • ~/filtered_feature_bc_matrix.h5 or raw_feature_bc_matrix.h5: The HDF5 file containing the filtered or raw feature-barcode matrix, respectively.

  • ~/spatial/tissue_lowres_image.png or spatial/tissue_hires_image.png: The low-resolution or high-resolution tissue image.

  • ~/spatial/scalefactors_json.json: A JSON file containing the scale factors for the images.

  • ~/spatial/tissue_positions_list.csv or /~spatial/tissue_positions.csv: A CSV file containing the tissue positions and spatial coordinates.

The function will check for these files and process them to create a SPATA2 object. It reads the count matrix, loads the spatial data, and initializes the SPATA2 object with the necessary metadata and settings.

Gene and Protein Expression

This function also supports reading coupled gene expression and protein expression data. It expects the input directory to contain an HDF5 file that includes separate datasets for gene expression and protein expression. The function uses Seurat::Read10X_h5() with unique.features = TRUE, to read in data and, if the result is a list, it assumes that it contains gene and protein expression. This scenario is handled as follows:

  • Gene expression data is extracted from the "Gene Expression" dataset in the HDF5 file and stored in an assay named gene.

  • Protein expression data is extracted from the "Antibody Capture" dataset in the HDF5 file protein.

The function ensures that molecule names do not overlap by normalizing the names:

  • Gene expression molecule names remain in upper case (human data) or title case (mouse data).

  • Protein expression molecule names are forced to lowercase.

This naming convention prevents any overlap and ensures that each molecule type is uniquely identified in the resulting SPATA2 object, which contains two assays. One of molecular modality gene and of of molecular modality protein.