Replace all mentions of ImageJ with Fiji. Closes #1

NAMESPACE

@@ -3,9 +3,9 @@
 export(add_array_coords)
 export(add_overlap_info)
 export(build_spe)
-export(prep_imagej_coords)
-export(prep_imagej_image)
-export(rescale_imagej_inputs)
+export(prep_fiji_coords)
+export(prep_fiji_image)
+export(rescale_fiji_inputs)
 export(spe_to_seurat)
 export(spot_plot)
 import(DropletUtils)

R/add_array_coords.R

@@ -21,7 +21,7 @@
 #' @param coords_dir A \code{character(1)} vector giving the directory
 #' containing sample directories each with \code{tissue_positions.csv},
 #' \code{scalefactors_json.json}, and \code{tissue_lowres_image.png} files
-#' produced from refinement with \code{prep_imagej_*()} functions
+#' produced from refinement with \code{prep_fiji_*()} functions
 #' @param overwrite A \code{logical(1)} vector indicating whether to overwrite
 #' \code{spatialCoords(spe)}, and \code{colData(spe)} columns \code{array_row},
 #' \code{array_col}, \code{pixel_row_in_fullres}, and
@@ -65,20 +65,20 @@
 #'     sr_dir, sample_info$capture_area, "outs", "spatial"
 #' )
 #'
-#' #   Add ImageJ-output-related columns
-#' imagej_dir <- tempdir()
+#' #   Add Fiji-output-related columns
+#' fiji_dir <- tempdir()
 #' temp <- unzip(
-#'     spatialLIBD::fetch_data("visiumStitched_brain_ImageJ_out"),
-#'     exdir = imagej_dir
+#'     spatialLIBD::fetch_data("visiumStitched_brain_Fiji_out"),
+#'     exdir = fiji_dir
 #' )
-#' sample_info$imagej_xml_path <- temp[grep("xml$", temp)]
-#' sample_info$imagej_image_path <- temp[grep("png$", temp)]
+#' sample_info$fiji_xml_path <- temp[grep("xml$", temp)]
+#' sample_info$fiji_image_path <- temp[grep("png$", temp)]
 #'
-#' sample_info <- rescale_imagej_inputs(sample_info, out_dir = tempdir())
+#' sample_info <- rescale_fiji_inputs(sample_info, out_dir = tempdir())
 #'
 #' spe_input_dir <- tempdir()
-#' prep_imagej_coords(sample_info, out_dir = spe_input_dir)
-#' prep_imagej_image(sample_info, out_dir = spe_input_dir)
+#' prep_fiji_coords(sample_info, out_dir = spe_input_dir)
+#' prep_fiji_image(sample_info, out_dir = spe_input_dir)
 #'
 #' ########################################################################
 #' #   Add array coordinates

R/build_spe.R

@@ -44,20 +44,20 @@
 #'     sr_dir, sample_info$capture_area, "outs", "spatial"
 #' )
 #'
-#' #   Add ImageJ-output-related columns
-#' imagej_dir <- tempdir()
+#' #   Add Fiji-output-related columns
+#' fiji_dir <- tempdir()
 #' temp <- unzip(
-#'     spatialLIBD::fetch_data("visiumStitched_brain_ImageJ_out"),
-#'     exdir = imagej_dir
+#'     spatialLIBD::fetch_data("visiumStitched_brain_Fiji_out"),
+#'     exdir = fiji_dir
 #' )
-#' sample_info$imagej_xml_path <- temp[grep("xml$", temp)]
-#' sample_info$imagej_image_path <- temp[grep("png$", temp)]
+#' sample_info$fiji_xml_path <- temp[grep("xml$", temp)]
+#' sample_info$fiji_image_path <- temp[grep("png$", temp)]
 #'
-#' sample_info <- rescale_imagej_inputs(sample_info, out_dir = tempdir())
+#' sample_info <- rescale_fiji_inputs(sample_info, out_dir = tempdir())
 #'
 #' spe_input_dir <- tempdir()
-#' prep_imagej_coords(sample_info, out_dir = spe_input_dir)
-#' prep_imagej_image(sample_info, out_dir = spe_input_dir)
+#' prep_fiji_coords(sample_info, out_dir = spe_input_dir)
+#' prep_fiji_image(sample_info, out_dir = spe_input_dir)
 #'
 #' ########################################################################
 #' #   Build the SpatialExperiment

R/prep_imagej_coords.R → R/prep_fiji_coords.R

@@ -1,14 +1,14 @@
-#' Apply transform info from ImageJ XML output
+#' Apply transform info from Fiji XML output
 #'
 #' Given a \code{tibble} of sample information (\code{sample_info}) with
-#' columns \code{capture_area}, \code{group}, and \code{imagej_xml_path},
-#' expected to have one unique path to ImageJ XML output per group, read in
+#' columns \code{capture_area}, \code{group}, and \code{fiji_xml_path},
+#' expected to have one unique path to Fiji XML output per group, read in
 #' the pixel coordinates from each capture area's \code{tissue_positions.csv}
 #' file from Spaceranger, and transform using the rotation matrix specified
-#' by ImageJ. Write one new \code{tissue_positions.csv} file per group.
+#' by Fiji. Write one new \code{tissue_positions.csv} file per group.
 #'
 #' @param sample_info A \code{tibble} with columns \code{capture_area},
-#' \code{group}, \code{imagej_xml_path}, \code{intra_group_scalar}, and
+#' \code{group}, \code{fiji_xml_path}, \code{intra_group_scalar}, and
 #' \code{group_hires_scalef}
 #' @param out_dir A character(1) vector giving a path to a directory to place
 #' the output pixel coordinates CSVs. Provided the parent exists, \code{out_dir}
@@ -40,23 +40,23 @@
 #'     sr_dir, sample_info$capture_area, "outs", "spatial"
 #' )
 #'
-#' #   Add ImageJ-output-related columns
-#' imagej_dir <- tempdir()
+#' #   Add Fiji-output-related columns
+#' fiji_dir <- tempdir()
 #' temp <- unzip(
-#'     spatialLIBD::fetch_data("visiumStitched_brain_ImageJ_out"),
-#'     exdir = imagej_dir
+#'     spatialLIBD::fetch_data("visiumStitched_brain_Fiji_out"),
+#'     exdir = fiji_dir
 #' )
-#' sample_info$imagej_xml_path <- temp[grep("xml$", temp)]
-#' sample_info$imagej_image_path <- temp[grep("png$", temp)]
+#' sample_info$fiji_xml_path <- temp[grep("xml$", temp)]
+#' sample_info$fiji_image_path <- temp[grep("png$", temp)]
 #'
-#' sample_info <- rescale_imagej_inputs(sample_info, out_dir = tempdir())
+#' sample_info <- rescale_fiji_inputs(sample_info, out_dir = tempdir())
 #'
 #' spe_input_dir <- tempdir()
-#' prep_imagej_coords(sample_info, out_dir = spe_input_dir)
+#' prep_fiji_coords(sample_info, out_dir = spe_input_dir)
 #'
 #' #    A file of spatial coordinates for the stitched Br2719 was produced
 #' list.files(spe_input_dir)
-prep_imagej_coords <- function(sample_info, out_dir) {
+prep_fiji_coords <- function(sample_info, out_dir) {
     ## For R CMD check
     group <- barcode <- key <- pxl_col_in_fullres <- pxl_row_in_fullres <- NULL
 
@@ -67,7 +67,7 @@ prep_imagej_coords <- function(sample_info, out_dir) {
 
     #   State assumptions about columns expected to be in sample_info
     expected_cols <- c(
-        "capture_area", "group", "imagej_xml_path", "intra_group_scalar",
+        "capture_area", "group", "fiji_xml_path", "intra_group_scalar",
         "group_hires_scalef"
     )
     if (!all(expected_cols %in% colnames(sample_info))) {
@@ -79,8 +79,8 @@ prep_imagej_coords <- function(sample_info, out_dir) {
         )
     }
 
-    if (!all(file.exists(sample_info$imagej_xml_path))) {
-        stop("All files in 'sample_info$imagej_xml_path' must exist.")
+    if (!all(file.exists(sample_info$fiji_xml_path))) {
+        stop("All files in 'sample_info$fiji_xml_path' must exist.")
     }
 
     dir.create(out_dir, recursive = TRUE, showWarnings = FALSE)
@@ -89,13 +89,13 @@ prep_imagej_coords <- function(sample_info, out_dir) {
         this_sample_info <- sample_info |>
             dplyr::filter(group == this_group)
 
-        if (length(unique(this_sample_info$imagej_xml_path)) > 1) {
-            stop("Expected one unique path for 'imagej_xml_path' per group in 'sample_info'.")
+        if (length(unique(this_sample_info$fiji_xml_path)) > 1) {
+            stop("Expected one unique path for 'fiji_xml_path' per group in 'sample_info'.")
         }
 
         #   Find all XML elements containing input image paths and
         #   transformation matrices
-        transform_nodes <- this_sample_info$imagej_xml_path[1] |>
+        transform_nodes <- this_sample_info$fiji_xml_path[1] |>
             read_xml() |>
             suppressWarnings() |>
             xml_find_all(".//t2_patch")
@@ -107,14 +107,14 @@ prep_imagej_coords <- function(sample_info, out_dir) {
             this_sample_info$capture_area, function(x) grep(x, input_paths)
         )
         if (length(input_paths) != nrow(this_sample_info) || any(is.na(input_indices))) {
-            stop("Expected each capture area to be present exactly once in the input filenames to ImageJ for each group.")
+            stop("Expected each capture area to be present exactly once in the input filenames to Fiji for each group.")
         }
 
         coords_list <- list()
 
         #   Loop through all capture areas in this group
         for (i in seq(nrow(this_sample_info))) {
-            #   Parse the rotation matrix from the ImageJ XML, and scale
+            #   Parse the rotation matrix from the Fiji XML, and scale
             #   translations from high to fullres
             rot <- transform_nodes[input_indices[i]] |>
                 xml_attr("transform") |>

R/prep_imagej_image.R → R/prep_fiji_image.R

@@ -1,14 +1,14 @@
-#' Create low-res images and scale factors from high-res ImageJ output images
+#' Create low-res images and scale factors from high-res Fiji output images
 #'
-#' After stitching all groups in \code{sample_info} with ImageJ, images of
+#' After stitching all groups in \code{sample_info} with Fiji, images of
 #' various resolutions (pixel dimensions) are left. This function creates copies
 #' of each image whose largest dimension is \code{lowres_max_size} pixels. It
 #' also creates a corresponding \code{scalefactors_json.json} file much like
-#' Spaceranger's. In conjunction with \code{prep_imagej_image()}, this function
+#' Spaceranger's. In conjunction with \code{prep_fiji_image()}, this function
 #' prepares for building the \code{SpatialExperiment} with \code{build_spe()}.
 #'
 #' @param sample_info A \code{tibble} with columns \code{capture_area},
-#' \code{group}, \code{imagej_image_path}, \code{spaceranger_dir},
+#' \code{group}, \code{fiji_image_path}, \code{spaceranger_dir},
 #' \code{intra_group_scalar}, and \code{group_hires_scalef}
 #' @param out_dir A character(1) vector giving a path to a directory to place
 #' the output image(s) and scale factors. Provided the parent exists, \code{out_dir}
@@ -41,19 +41,19 @@
 #'     sr_dir, sample_info$capture_area, "outs", "spatial"
 #' )
 #'
-#' #   Add ImageJ-output-related columns
-#' imagej_dir <- tempdir()
+#' #   Add Fiji-output-related columns
+#' fiji_dir <- tempdir()
 #' temp <- unzip(
-#'     spatialLIBD::fetch_data("visiumStitched_brain_ImageJ_out"),
-#'     exdir = imagej_dir
+#'     spatialLIBD::fetch_data("visiumStitched_brain_Fiji_out"),
+#'     exdir = fiji_dir
 #' )
-#' sample_info$imagej_xml_path <- temp[grep("xml$", temp)]
-#' sample_info$imagej_image_path <- temp[grep("png$", temp)]
+#' sample_info$fiji_xml_path <- temp[grep("xml$", temp)]
+#' sample_info$fiji_image_path <- temp[grep("png$", temp)]
 #'
-#' sample_info <- rescale_imagej_inputs(sample_info, out_dir = tempdir())
+#' sample_info <- rescale_fiji_inputs(sample_info, out_dir = tempdir())
 #'
 #' spe_input_dir <- tempdir()
-#' prep_imagej_image(
+#' prep_fiji_image(
 #'     sample_info,
 #'     out_dir = spe_input_dir, lowres_max_size = 1000
 #' )
@@ -64,13 +64,13 @@
 #'     file.path(spe_input_dir, "Br2719", "tissue_lowres_image.png")
 #' )
 #' dim(this_image)
-prep_imagej_image <- function(sample_info, out_dir, lowres_max_size = 1200) {
+prep_fiji_image <- function(sample_info, out_dir, lowres_max_size = 1200) {
     ## For R CMD check
     group <- NULL
 
     #   State assumptions about columns expected to be in sample_info
     expected_cols <- c(
-        "capture_area", "group", "imagej_image_path", "intra_group_scalar",
+        "capture_area", "group", "fiji_image_path", "intra_group_scalar",
         "group_hires_scalef", "spaceranger_dir"
     )
     if (!all(expected_cols %in% colnames(sample_info))) {
@@ -82,8 +82,8 @@ prep_imagej_image <- function(sample_info, out_dir, lowres_max_size = 1200) {
         )
     }
 
-    if (!all(file.exists(sample_info$imagej_image_path))) {
-        stop("All files in 'sample_info$imagej_image_path' must exist.")
+    if (!all(file.exists(sample_info$fiji_image_path))) {
+        stop("All files in 'sample_info$fiji_image_path' must exist.")
     }
 
     dir.create(out_dir, showWarnings = FALSE)
@@ -92,11 +92,11 @@ prep_imagej_image <- function(sample_info, out_dir, lowres_max_size = 1200) {
         this_sample_info <- sample_info |>
             dplyr::filter(group == this_group)
 
-        if (length(unique(this_sample_info$imagej_image_path)) > 1) {
-            stop("Expected one unique path for 'imagej_image_path' per group in 'sample_info'.")
+        if (length(unique(this_sample_info$fiji_image_path)) > 1) {
+            stop("Expected one unique path for 'fiji_image_path' per group in 'sample_info'.")
         }
 
-        this_image <- imager::load.image(this_sample_info$imagej_image_path[1])
+        this_image <- imager::load.image(this_sample_info$fiji_image_path[1])
 
         #   Combine info about the original scalefactors of the first capture
         #   area with group-related scalars to form a new scalefactors JSON

R/rescale_imagej_inputs.R → R/rescale_fiji_inputs.R

@@ -1,8 +1,8 @@
-#' Write same-scale hires images for input to ImageJ
+#' Write same-scale hires images for input to Fiji
 #'
 #' Given a \code{tibble} of sample information (\code{sample_info}) with
 #' columns \code{capture_area}, \code{group}, and \code{spaceranger_dir},
-#' Write new high-resolution images for use as input to ImageJ. Particularly
+#' Write new high-resolution images for use as input to Fiji. Particularly
 #' when capture areas come from different slides, there is a risk of significant
 #' scale differences among Spaceranger's \code{tissue_hires_image.png} images;
 #' that is, the physical distance represented by a pixel from each capture area
@@ -48,25 +48,25 @@
 #'     sr_dir, sample_info$capture_area, "outs", "spatial"
 #' )
 #'
-#' #   Add ImageJ-output-related columns
-#' imagej_dir <- tempdir()
+#' #   Add Fiji-output-related columns
+#' fiji_dir <- tempdir()
 #' temp <- unzip(
-#'     spatialLIBD::fetch_data("visiumStitched_brain_ImageJ_out"),
-#'     exdir = imagej_dir
+#'     spatialLIBD::fetch_data("visiumStitched_brain_Fiji_out"),
+#'     exdir = fiji_dir
 #' )
-#' sample_info$imagej_xml_path <- temp[grep("xml$", temp)]
-#' sample_info$imagej_image_path <- temp[grep("png$", temp)]
+#' sample_info$fiji_xml_path <- temp[grep("xml$", temp)]
+#' sample_info$fiji_image_path <- temp[grep("png$", temp)]
 #'
 #' out_dir <- tempdir()
-#' sample_info_new <- rescale_imagej_inputs(sample_info, out_dir = out_dir)
+#' sample_info_new <- rescale_fiji_inputs(sample_info, out_dir = out_dir)
 #'
 #' #    Scale factors are computed that are necessary downstream (i.e. with
-#' #    prep_imagej_*() functions)
+#' #    prep_fiji_*() functions)
 #' sample_info_new[, setdiff(colnames(sample_info_new), colnames(sample_info))]
 #'
 #' #    Image are produced that are ready for alignment in Fiji
 #' list.files(out_dir)
-rescale_imagej_inputs <- function(sample_info, out_dir) {
+rescale_fiji_inputs <- function(sample_info, out_dir) {
     ## For R CMD check
     group <- spot_diameter_fullres <- intra_group_scalar <- tissue_hires_scalef <- intra_group_scalar_image <- NULL