Create low-res images and scale factors from high-res Fiji output images

After stitching all groups in sample_info with Fiji, images of various resolutions (pixel dimensions) are left. This function creates copies of each image whose largest dimension is lowres_max_size pixels. It also creates a corresponding scalefactors_json.json file much like SpaceRanger's. In conjunction with prep_fiji_coords(), this function prepares for building the SpatialExperiment-class with build_spe().

prep_fiji_image(sample_info, out_dir, lowres_max_size = 1200)

Arguments

sample_info

A data.frame() with columns capture_area, group, fiji_xml_path, fiji_image_path, spaceranger_dir, intra_group_scalar, and group_hires_scalef. The last two are made by rescale_fiji_inputs().

out_dir

A character(1) vector giving a path to a directory to place the output image(s) and scale factors. Provided the parent directory exists, out_dir will be created if necessary.

lowres_max_size

An integer(1) vector: the resolution (number of pixels) of the larger dimension of the output image(s), considered to be "low resolution". The default value of 1200 assumes that you are stitching together at most a 2 by 2 grid of Visium capture areas, where each has at most 600 pixels on the longest dimension (as is the default in SpaceRanger).

Value

This function returns character() with the file paths to the tissue_lowres_image.png and scalefactors_json.json files it created.

Author

Nicholas J. Eagles

Examples

if (file.exists("sample_info.rds")) {
    sample_info <- readRDS("sample_info.rds")
} else {
    sample_info <- dplyr::tibble(
        group = "Br2719",
        capture_area = c("V13B23-283_A1", "V13B23-283_C1", "V13B23-283_D1")
    )
    #   Add 'spaceranger_dir' column
    sr_dir <- tempdir()
    temp <- unzip(
        spatialLIBD::fetch_data("visiumStitched_brain_spaceranger"),
        exdir = sr_dir
    )
    sample_info$spaceranger_dir <- file.path(
        sr_dir, sample_info$capture_area, "outs", "spatial"
    )

    #   Add Fiji-output-related columns
    fiji_dir <- tempdir()
    temp <- unzip(
        spatialLIBD::fetch_data("visiumStitched_brain_Fiji_out"),
        exdir = fiji_dir
    )
    sample_info$fiji_xml_path <- temp[grep("xml$", temp)]
    sample_info$fiji_image_path <- temp[grep("png$", temp)]

    ## Re-size images and add more information to the sample_info
    sample_info <- rescale_fiji_inputs(sample_info, out_dir = tempdir())

    saveRDS(sample_info, "sample_info.rds")
}

spe_input_dir <- tempdir()
out_paths <- prep_fiji_image(
    sample_info,
    out_dir = spe_input_dir, lowres_max_size = 1000
)

#    A "low resolution" stitched image was produced, which has 1000
#    pixels in its largest dimension
this_image <- imager::load.image(
    file.path(spe_input_dir, "Br2719", "tissue_lowres_image.png")
)
dim(this_image)
#> [1]  461 1000    1    3
library("imager")
#> Loading required package: magrittr
#> 
#> Attaching package: ‘magrittr’
#> The following object is masked from ‘package:GenomicRanges’:
#> 
#>     subtract
#> 
#> Attaching package: ‘imager’
#> The following object is masked from ‘package:magrittr’:
#> 
#>     add
#> The following objects are masked from ‘package:SummarizedExperiment’:
#> 
#>     resize, width
#> The following object is masked from ‘package:Biobase’:
#> 
#>     channel
#> The following objects are masked from ‘package:GenomicRanges’:
#> 
#>     resize, width
#> The following objects are masked from ‘package:IRanges’:
#> 
#>     resize, width
#> The following object is masked from ‘package:S4Vectors’:
#> 
#>     width
#> The following object is masked from ‘package:BiocGenerics’:
#> 
#>     width
#> The following objects are masked from ‘package:stats’:
#> 
#>     convolve, spectrum
#> The following object is masked from ‘package:graphics’:
#> 
#>     frame
#> The following object is masked from ‘package:base’:
#> 
#>     save.image
plot(this_image)


#    In total, an image and scalefactors were written
out_paths
#> [1] "/tmp/RtmpAA9v23/Br2719/tissue_lowres_image.png"
#> [2] "/tmp/RtmpAA9v23/Br2719/scalefactors_json.json"