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Read Vizgen MERFISH output as SpatialFeatureExperiment

Source: R/read.R
readVizgen.Rd

This function reads the standard Vizgen MERFISH output into an SFE object. The coordinates are in microns. Cell centroids are read into colGeometry "centroids", and cell segmentations are read into colGeometry "cellSeg". The image(s) (polyT, DAPI, and cell boundaries) are also read as SpatRaster objects so they are not loaded into memory unless necessary. Because the image's origin is the top left while the geometry's origin is bottom left, either the image or the geometry needs to be flipped. Because the image accompanying MERFISH datasets are usually very large, the coordinates will be flipped so the flipping operation won't load the entire image into memory. Large datasets with hundreds of thousands of cells can take a while to read if reading transcript spots as it takes a while to convert the spots to MULTIPOINT geometries.

Usage

readVizgen(
  data_dir,
  z = "all",
  sample_id = "sample01",
  min_area = 15,
  image = c("DAPI", "PolyT", "Cellbound"),
  flip = c("geometry", "image", "none"),
  max_flip = "50 MB",
  filter_counts = FALSE,
  add_molecules = FALSE,
  use_bboxes = FALSE,
  use_cellpose = TRUE,
  BPPARAM = SerialParam(),
  file_out = file.path(data_dir, "detected_transcripts.parquet"),
  z_option = c("3d", "split")
)

Arguments

data_dir

Top level output directory.

z

Integer, z index to read, or "all", indicating z-planes of the images and transcript spots to read. While cell segmentation seems to have multiple z-planes, the segmentation in all z-planes are the same so in effect the cell segmentatio is only in 2D.

sample_id

A character sample identifier, which matches the sample_id in imgData. The sample_id will also be stored in a new column in colData, if not already present. Default = sample01.

min_area

Minimum cell area in square microns. Anything smaller will be considered artifact or debris and removed.

image

Which image(s) to load, can be "DAPI", "PolyT", "Cellbound" or any combination of them.

flip

To flip the image, geometry coordinates, or none. Because the image has the origin at the top left while the geometry has origin at the bottom left, one of them needs to be flipped for them to match. If one of them is already flipped, then use "none". The image will not be flipped if it's GeoTIFF.

max_flip

Maximum size of the image allowed to flip the image. Because the image will be loaded into memory to be flipped. If the image is larger than this size then the coordinates will be flipped instead.

filter_counts

Logical, whether to keep cells with counts > 0.

add_molecules

Logical, whether to add transcripts coordinates to an object.

use_bboxes

If no segmentation output is present, use cell_metadata to make bounding boxes instead.

use_cellpose

Whether to read the parquet files from CellPose cell segmentation. If FALSE, cell segmentation will be read from the HDF5 files. Note that reading HDF5 files for numerous FOVs is very slow.

BPPARAM

A BiocParallelParam object specifying parallel processing backend and number of threads to use for parallelizable tasks:

  1. To load cell segmentation from HDF5 files from different fields of view (FOVs) with multiple cores. A progress bar can be configured in the BiocParallelParam object. When there are numerous FOVs, reading in the geometries can be time consuming, so we recommend using a server and larger number of threads. This argument is not used if use_cellpose = TRUE and the parquet file is present.

  2. To get the largest piece and see if it's larger than min_area when there are multiple pieces in the cell segmentation for one cell.

file_out

Name of file to save the geometry or raster to disk. Especially when the geometries are so large that it's unwieldy to load everything into memory. If this file (or directory for multiple files) already exists, then the existing file(s) will be read, skipping the processing. When writing the file, extensions supplied are ignored and extensions are determined based on `dest`.

z_option

What to do with z coordinates. "3d" is to construct 3D geometries. "split" is to create a separate 2D geometry for each z-plane so geometric operations are fully supported but some data wrangling is required to perform 3D analyses. When the z coordinates are not integers, 3D geometries will always be constructed since there are no z-planes to speak of. This argument does not apply when `spatialCoordsNames` has length 2.

Value

A SpatialFeatureExperiment object.

Note

Since the transcript spots file is often very large, we recommend only using add_molecules = TRUE on servers with a lot of memory. If reading all z-planes, conversion of transcript spot geometry to parquet file might fail due to arrow data length limit. In a future version, when the transcript spot geometry is large, it will be written to multiple separate parquet files which are then concatenated with DuckDB. Also, in a future version, the transcript spot processing function might be rewritten in C++ to stream the original CSV file so it's not entirely loaded into memory.

Examples

fp <- tempdir()
dir_use <- SFEData::VizgenOutput(file_path = file.path(fp, "vizgen_test"))
#> see ?SFEData and browseVignettes('SFEData') for documentation
#> loading from cache
#> The downloaded files are in /tmp/RtmpRqe7ue/vizgen_test/vizgen_cellbound 
sfe <- readVizgen(dir_use, z = 3L, image = "PolyT",
flip = "geometry")
#> >>> 1 `.parquet` files exist:
#> /tmp/RtmpRqe7ue/vizgen_test/vizgen_cellbound/cell_boundaries.parquet
#> >>> using -> /tmp/RtmpRqe7ue/vizgen_test/vizgen_cellbound/cell_boundaries.parquet
#> >>> Cell segmentations are found in `.parquet` file
#> Removing 35 cells with area less than 15
#> >>> filtering geometries to match 1023 cells with counts > 0

## Filtering of counts, and addition of molecule coordinates..
sfe <- readVizgen(dir_use, z = 3L, image = "PolyT", filter_counts = TRUE,
add_molecules = TRUE, flip = "geometry")
#> >>> 1 `.parquet` files exist:
#> /tmp/RtmpRqe7ue/vizgen_test/vizgen_cellbound/cell_boundaries.parquet
#> >>> using -> /tmp/RtmpRqe7ue/vizgen_test/vizgen_cellbound/cell_boundaries.parquet
#> >>> Cell segmentations are found in `.parquet` file
#> Removing 35 cells with area less than 15
#> >>> ..filtering `cell_metadata` - keep cells with `transcript_count` > 0
#> >>> filtering geometries to match 1023 cells with counts > 0
#> >>> Reading transcript coordinates
#> >>> Converting transcript spots to geometry
#> >>> Writing reformatted transcript spots to disk

unlink(dir_use, recursive = TRUE)