Read 10X Visium data as SpatialFeatureExperiment — read10xVisiumSFE • SpatialFeatureExperiment

Read Space Ranger output as a SpatialFeatureExperiment object, where spots are represented with polygons in the colGeometry called "spotPoly". Other geometries can be added later after the dataset is read. If data = "filtered", then spatial neighborhood graphs of the spots are also computed and stored in the colGraph called "visium" in all samples for downstream spatial analyses.

Usage

read10xVisiumSFE(
  samples = "",
  dirs = file.path(samples, "outs"),
  sample_id = paste0("sample", sprintf("%02d", seq_along(samples))),
  bin_size = NULL,
  type = c("HDF5", "sparse"),
  data = c("filtered", "raw"),
  images = c("lowres", "hires"),
  unit = c("full_res_image_pixel", "micron"),
  add_Graph = TRUE,
  style = "W",
  zero.policy = NULL,
  load = FALSE,
  row.names = c("id", "symbol")
)

Arguments

samples: a character vector specifying one or more directories, each corresponding to a 10x Genomics Visium sample (see Details); if provided, names will be used as sample identifiers
dirs: Directory for each sample that contains the spatial and raw/filtered_featues_bc_matrix directories. By default, the outs directory under the directory specified in the samples argument, as in Space Ranger output. Change the dirs argument if you have moved or renamed the output directory.
sample_id: character string specifying unique sample identifiers, one for each directory specified via samples; ignored if !is.null(names(samples))
bin_size: c(character), use this only when loading VisiumHD. Specify which bin resolution to load, default is NULL which assumes that data is standard Visium. Eg, single resolution is c("8"), if to load all three resolutions, use c("2", "8", "16").
type: Either "HDF5", and the matrix will be represented as TENxMatrix, or "sparse", and the matrix will be read as a dgCMatrix.
data: character string specifying whether to read in filtered (spots mapped to tissue) or raw data (all spots).
images: character vector specifying which images to include. Valid values are "lowres", "hires", "fullres", "detected", "aligned"
unit: Whether to use pixels in full resolution image or microns as the unit. If using microns, then spacing between spots in pixels will be used to convert the coordinates into microns, as the spacing is known to be 100 microns. This is used to plot scale bar.
add_Graph: c(local), if to add spatial neighborhood graph for spots and only if c(data = "filtered"). Default is c(TRUE)
style: style can take values “W”, “B”, “C”, “U”, “minmax” and “S”
zero.policy: default NULL, use global option value; if FALSE stop with error for any empty neighbour sets, if TRUE permit the weights list to be formed with zero-length weights vectors
load: Not used, kept for backward compatibility.
row.names: String specifying whether to use Ensembl IDs ("ID") or gene symbols ("Symbol") as row names. If using symbols, the Ensembl ID will be appended to disambiguate in case the same symbol corresponds to multiple Ensembl IDs.

Value

A SpatialFeatureExperiment object. The images might need to be manually transposed and/or mirrored to match the spots in this version of this package.

Note

The as(<dgTMatrix>, "dgCMatrix") is deprecated warning comes from the DropletUtils package which is used by SpatialExperiment to read 10X outputs. This will be fixed when SpatialExperiment switches to TENxIO.

It is assumed that the images have not been cropped. Otherwise the images might not align with the spots.

Examples

dir <- system.file("extdata", package = "SpatialFeatureExperiment")

sample_ids <- c("sample01", "sample02")
samples <- file.path(dir, sample_ids)

list.files(samples[1])
#> [1] "outs"
list.files(file.path(samples[1], "spatial"))
#> character(0)
(sfe <- read10xVisiumSFE(samples, sample_id = sample_ids,
    type = "sparse", data = "filtered",
    load = FALSE
))
#> >>> 10X Visium data will be loaded: sample01
#> >>> Adding spatial neighborhood graph to sample01
#> >>> 10X Visium data will be loaded: sample02
#> >>> Adding spatial neighborhood graph to sample02
#> class: SpatialFeatureExperiment 
#> dim: 5 25 
#> metadata(0):
#> assays(1): counts
#> rownames(5): ENSG00000014257 ENSG00000142515 ENSG00000263639
#>   ENSG00000163810 ENSG00000149591
#> rowData names(14): symbol Feature.Type ...
#>   Median.Normalized.Average.Counts_sample02
#>   Barcodes.Detected.per.Feature_sample02
#> colnames(25): GTGGCGTGCACCAGAG-1 GGTCCCATAACATAGA-1 ...
#>   TGCAATTTGGGCACGG-1 ATGCCAATCGCTCTGC-1
#> colData names(10): in_tissue array_row ... channel3_mean channel3_stdev
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
#> imgData names(4): sample_id image_id data scaleFactor
#> 
#> unit: full_res_image_pixel
#> Geometries:
#> colGeometries: spotPoly (POLYGON) 
#> 
#> Graphs:
#> sample01: col: visium
#> sample02: col: visium

# load VisiumHD
# path to "binned_outputs" directory containing:
# └── binned_outputs
#     ├── square_002um
#     ├── square_008um
#     └── square_016um
dir_hd <- "~/Downloads/Visium_HD_Mouse_Brain/binned_outputs/"
# this is public dataset ->
# https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-mouse-brain-he
sfe_hd <-
read10xVisiumSFE(samples = list.files(dir_hd), # 1:3 resolutions
                 dirs = dir_hd,
                 bin_size = c("8", "16"), # this defines which of 1:3 resolutions to load
                 type = "HDF5", # Note, "sparse" -> takes longer to load
                 data = "filtered", # spots under tissue
                 images = c("lowres"), # for now low res. image only
                 add_Graph = FALSE # Note, if VisiumHD this can take time for 2 or 8µm res.
                 )