send RBioFormats to Suggests

Author: Artur-man

DESCRIPTION

@@ -25,13 +25,13 @@ Imports:
   rhdf5,
   HDF5Array,
   Rarr,
-  RBioFormats,
   magick
 Suggests: 
   testthat (>= 3.0.0),
   knitr,
   ggplot2,
   shiny,
+  RBioFormats,
   BiocFileCache,
   BiocStyle
 Config/testthat/edition: 3

R/BFArray.R

@@ -12,20 +12,25 @@
 #'
 #' @name BFArray-methods
 #' @rdname BFArray-methods
-#' @importFrom RBioFormats read.metadata
 #'
 #' @export
 #' @return A BFArray object
 #' 
 #' @examples
 #' # get image
+#' library(RBioFormats)
 #' img.file <- system.file("extdata", 
 #'                         "xy_12bit__plant.ome.tiff", 
 #'                         package = "ImageArray")
 #' bfa <- BFArray(img.file, series = 1, resolution = 2)
 #' dim(bfa)
 #' type(bfa)
 BFArray <- function(image.file, series, resolution) {
+  
+  # check RBioFormats
+  if(!requireNamespace("RBioFormats"))
+    stop("Please install RBioFormats: BiocManager::install('RBioFormats')")
+  
   # get metadata
   meta <- RBioFormats::read.metadata(
     file = image.file,
@@ -108,9 +113,13 @@ setMethod("type", "BFArraySeed", function(x) x@type)
 ### extract_array()
 ###
 
-#' @importFrom RBioFormats read.metadata read.image
 #' @importFrom EBImage imageData
 .extract_array_from_BFArraySeed <- function(x, index) {
+  
+  # check RBioFormats
+  if(!requireNamespace("RBioFormats"))
+    stop("Please install RBioFormats: BiocManager::install('RBioFormats')")
+  
   # get metadata
   meta <- RBioFormats::read.metadata(
     file = x@filepath,

README.md

@@ -73,10 +73,6 @@ vertical flipping and negation.
 
 ```r
 imgarray <- rotate(imgarray, degrees = 90)
-imgarray <- flip(imgarray)
-imgarray <- flop(imgarray)
-imgarray  <- negate(imgarray)
-imgarray
 ```
 
 <br>
@@ -98,9 +94,10 @@ Level 2 (51,51)
 <br>
 
 You can also use an existing **OME-TIFF** (or any Bioformats image) to 
-create an ImageArray object. 
+create an ImageArray object which we use **RBioFormats** package too.
 
 ```r
+library(RBioFormats)
 ome_file <- system.file("extdata", 
                         "xy_12bit__plant.ome.tiff", 
                         package = "ImageArray")

vignettes/ImageArray.Rmd

@@ -14,19 +14,20 @@ vignette: |
 ---
 
 ```{r load-libs}
+library(ImageArray)
 library(BiocFileCache)
+library(RBioFormats)
 library(EBImage)
-library(ImageArray)
 library(ggplot2)
 library(shiny)
 ```
 
 # Introduction
 
-`r Biocpkg("ImageArray")` is a package for Pyramidal and non-pyramidal 
-images in `r Biocpkg("DelayedArray")` format.
+The *ImageArray* package provides a **unified, memory-efficient** framework for working with both **pyramidal** and **non-pyramidal** images in R, leveraging the DelayedArray infrastructure.  
+With ImageArray you can store large images on disk (HDF5 or Zarr), treat them as array-like objects, perform lazy/blocked operations (e.g., rotate, flip, crop) across all pyramid levels, and seamlessly integrate with standard R image workflows.
 
-## Installation
+# Installation
 
 You can install `r Biocpkg("ImageArray")` from Bioconductor with:
 
@@ -37,8 +38,20 @@ if (!requireNamespace("BiocManager", quietly = TRUE)) {
 BiocManager::install("ImageArray")
 ```
 
+# Why pyramidal images?
+
+Image pyramids are multi-resolution representations: starting from a full resolution image, you generate a series of down-sampled versions (e.g., half resolution, quarter resolution, etc). These are common in microscopy, digital pathology and large-scale imaging because:
+
+- They allow fast zooming and visualization (you don’t always need full resolution to inspect large images).  
+- They support scale-aware analysis (you may operate on coarse levels when doing overview tasks, fine levels for detail).  
+- Storing data in a pyramidal stack often reduces memory footprint and enables on-disk processing.
+
+In our context, an ImageArray object may contain **multiple series** (each a resolution level) internally, and most operations are applied *consistently* across all series.
+
 # Usage
 
+## EBImage
+
 The main usage of the `r Biocpkg("ImageArray")` is to deliver 
 `r Biocpkg("DelayedArray")` operations for images or a list of images in 
 Pyramidal format. You can also use
@@ -97,7 +110,7 @@ bfa.raster <- as.raster(imgarray_cropped, max.pixel.size = 300)
 plot(bfa.raster)
 ```
 
-# Using magick 
+## magick images
 
 ```{r read3}
 # make random EBImage image
@@ -116,7 +129,7 @@ bfa.raster <- as.raster(imgarray, max.pixel.size = 300)
 plot(bfa.raster)
 ```
 
-# Using OME.TIFF
+## OME.TIFF (Bio-Formats) images
 
 You can create `ImageArray` objects from OME.TIFF files with already defined
 layers. 
@@ -144,12 +157,15 @@ bfa.raster <- as.raster(img, max.pixel.size = 300)
 plot(bfa.raster)
 dim(bfa.raster)
 ```
-# Using with Shiny
+
+# Use cases
 
 The pyramid scheme introduced by `ImageArray` objects can also be used to 
 generate scalable plots and interactive Shiny application for visualizing 
 large images. 
 
+## Visualizing H&E images
+
 For this example, we will use a large H&E image used by 10x Genomics, 
 generated after a Xenium in Situ platform run. 
 
@@ -197,6 +213,8 @@ imgggplot <- ggplot2::ggplot(data.frame(x = 0, y = 0),
 imgggplot
 ```
 
+## Interactive Shiny Applications
+
 Now let us create a shiny application where we can interactively define 
 subsetting and visualize the image quickly.