RBioFormats: an R interface to the Bio-Formats library (original) (raw)
Introduction
RBioFormats provides an interface from R to the OMEBio-Formats Java library. Bio-Formats is a solution for reading data of various image types, including many popular in life sciences as well as proprietary microscopy image formats. It supports over 150 file formats from domains such as High Content Screening, time lapse imaging, digital pathology and other complex multidimensional image formats.
Image pixel data is typically complemented by image metadata containing, for example, technical and temporal parameters of the acquisition in the case of microscopy images. Such annotation can be an invaluable source of additional insight helpful during postprocessing or analyzing of the image data.
The package builds on top of the infrastructure provided by_EBImage_ by extending its class abstracting image data. The primary motivation behind developing RBioFormats was to fill the gap between data acquisition and analysis by providing a tool which allows to directly read the acquired images without the need of any tedious image format conversion in between.
The following chapters provide some practical examples illustrating the use of the package. Along the way the classes used for representing image data and metadata are described too.
Getting started
RBioFormats is an R package distributed as part of theBioconductor project. To install the package, start R (version 4.2 or higher) and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("RBioFormats")Once RBioFormats is installed, it can be loaded by the following command.
library("RBioFormats")## BioFormats library version 7.3.0Reading images
Images can be loaded into R with the help of the package function read.image. The following examples illustrates how to load a sample grayscale image
f <- system.file("images", "sample.png", package = "EBImage")
img <- read.image(f)
img## AnnotatedImage
## colorMode : Grayscale
## storage.mode : double
## dim : 768 512
## dimorder : x y
## frames.total : 1
## frames.render: 1
##
## imageData(object)[1:5,1:6]
## y
## x [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.4470588 0.4627451 0.4784314 0.4980392 0.5137255 0.5294118
## [2,] 0.4509804 0.4627451 0.4784314 0.4823529 0.5058824 0.5215686
## [3,] 0.4627451 0.4666667 0.4823529 0.4980392 0.5137255 0.5137255
## [4,] 0.4549020 0.4666667 0.4862745 0.4980392 0.5176471 0.5411765
## [5,] 0.4627451 0.4627451 0.4823529 0.4980392 0.5137255 0.5411765
##
## metadata
## $ coreMetadata:List of 18or an RGB image.
f <- system.file("images", "sample-color.png", package = "EBImage")
img <- read.image(f)
print(img, short = TRUE)## AnnotatedImage
## colorMode : Color
## storage.mode : double
## dim : 768 512 3
## dimorder : x y c
## frames.total : 3
## frames.render: 1
##
## metadata
## $ coreMetadata:List of 18Note the use of short = TRUE argument to print in the example above for displaying object summary without the image data preview. There is also a convenience function to query just for the order of dimensions.
dimorder(img)## [1] "x" "y" "c"The AnnotatedImage class
RBioFormats stores image data in an AnnotatedImage class which extends the Image class from EBImage.
getClassDef("AnnotatedImage")## Class "AnnotatedImage" [package "RBioFormats"]
##
## Slots:
##
## Name: .Data metadata colormode
## Class: array ImageMetadata integer
##
## Extends:
## Class "Image", directly
## Class "array", by class "Image", distance 2
## Class "structure", by class "Image", distance 3
## Class "vector", by class "Image", distance 4, with explicit coerceCompared to the original Image class the AnnotatedImage class features an additional metadata slot containing image metadata.
meta <- metadata(img)
meta## ImageMetadata
## $ coreMetadata:List of 18
## ..$ sizeX : int 768
## ..$ sizeY : int 512
## ..$ sizeZ : int 1
## ..$ sizeC : int 3
## ..$ sizeT : int 1
## ..$ pixelType: chr "uint8"
## .. [list output truncated]To alter the length of the printed output use the list.len attribute toprint.
print(meta, list.len = 99L)## ImageMetadata
## $ coreMetadata:List of 18
## ..$ sizeX : int 768
## ..$ sizeY : int 512
## ..$ sizeZ : int 1
## ..$ sizeC : int 3
## ..$ sizeT : int 1
## ..$ pixelType : chr "uint8"
## ..$ bitsPerPixel : int 8
## ..$ imageCount : int 3
## ..$ dimensionOrder : chr "XYCZT"
## ..$ orderCertain : logi TRUE
## ..$ rgb : logi FALSE
## ..$ littleEndian : logi FALSE
## ..$ interleaved : logi TRUE
## ..$ falseColor : logi FALSE
## ..$ metadataComplete: logi FALSE
## ..$ thumbnail : logi FALSE
## ..$ series : int 1
## ..$ resolutionLevel : int 1Working with large data sets
The read.metadata function allows to access image metadata without loading the corresponding pixel data.
f <- system.file("images", "nuclei.tif", package = "EBImage")
metadata <- read.metadata(f)
metadata## ImageMetadata
## $ coreMetadata :List of 18
## ..$ sizeX : int 510
## ..$ sizeY : int 510
## ..$ sizeZ : int 1
## ..$ sizeC : int 1
## ..$ sizeT : int 4
## ..$ pixelType: chr "uint8"
## .. [list output truncated]
## $ globalMetadata:List of 19
## ..$ Document Name : chr "out.tif"
## ..$ ImageLength : int 510
## ..$ MetaDataPhotometricInterpretation: chr "Monochrome"
## ..$ PhotometricInterpretation : chr "BlackIsZero"
## ..$ XResolution : num 72
## ..$ NewSubfileType : num 2
## .. [list output truncated]This approach is especially useful when working with image series and/or stacks which have high memory requirements. Information from the metadata can be used as input to functions which read and process the data chunk-wise rather than loading it all at once. For this purpose the subset argument to read.imagecomes in handy. Just to give you an idea the following toy example iterates over individual time frames. Similarly a region if interest from within individual image frames could be extracted by providing ranges on the X and Y planar dimensions. To subset image series specify them in the series argument.
for(t in seq_len(coreMetadata(metadata)$sizeT)) {
frame <- read.image(f, subset = list(T = t))
# perform some operations on each `frame`
}OME-XML representation
The OME-XML DOM tree representation of the metadata can be accessed using tools from the XML or xml2 package. For details on working with XML data in R see the corresponding package’s documentation.
library("xml2")
omexml <- read.omexml(f)
read_xml(omexml)## {xml_document}
## <OME schemaLocation="http://www.openmicroscopy.org/Schemas/OME/2016-06 http://www.openmicroscopy.org/Schemas/OME/2016-06/ome.xsd" xmlns="http://www.openmicroscopy.org/Schemas/OME/2016-06" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
## [1] <Image ID="Image:0" Name="nuclei.tif">\n <Pixels BigEndian="false" Dimen ...
## [2] <StructuredAnnotations>\n <XMLAnnotation ID="Annotation:0" Namespace="op ...Session info
Here is the output of sessionInfo() on the system on which this document was compiled:
## R version 4.5.0 RC (2025-04-04 r88126)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=en_US.UTF-8
## [9] LC_ADDRESS=en_US.UTF-8 LC_TELEPHONE=en_US.UTF-8
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] xml2_1.3.8 RBioFormats_1.8.0 BiocStyle_2.36.0
##
## loaded via a namespace (and not attached):
## [1] cli_3.6.4 knitr_1.50 rlang_1.1.6
## [4] xfun_0.52 png_0.1-8 tiff_0.1-12
## [7] generics_0.1.3 jsonlite_2.0.0 rJava_1.0-11
## [10] S4Vectors_0.46.0 RCurl_1.98-1.17 htmltools_0.5.8.1
## [13] stats4_4.5.0 sass_0.4.10 locfit_1.5-9.12
## [16] rmarkdown_2.29 grid_4.5.0 evaluate_1.0.3
## [19] jquerylib_0.1.4 abind_1.4-8 bitops_1.0-9
## [22] fastmap_1.2.0 yaml_2.3.10 lifecycle_1.0.4
## [25] bookdown_0.43 BiocManager_1.30.25 compiler_4.5.0
## [28] htmlwidgets_1.6.4 fftwtools_0.9-11 EBImage_4.50.0
## [31] lattice_0.22-7 digest_0.6.37 R6_2.6.1
## [34] bslib_0.9.0 jpeg_0.1-11 tools_4.5.0
## [37] BiocGenerics_0.54.0 cachem_1.1.0Appendix A: Working with test images
For development purposes it is useful to have images of a specific size or pixel type for testing. Mock files containing gradient images can be generated with
f <- mockFile(sizeX = 256, sizeY = 256)
img <- read.image(f)
library("EBImage")
display(img, method = "raster")Note that the native image data range is different depending on pixel type.
Image data returned by RBioFormats is by default scaled to the [0:1] range. This behavior can be controlled using the normalize argument toread.image.
sapply(types, function(t) {
img <- read.image(mockFile(sizeX = 65536, sizeY = 11, pixelType = t), normalize = FALSE)
if (typeof(img)=="raw")
img <- readBin(img, what = "int", n = length(img), size = 1L)
setNames(range(img), c("min", "max"))
})## int8 uint8 int16 uint16 int32 uint32 float double
## min -128 0 -32768 0 -2147483648 0 0 0
## max 127 255 32767 65535 0 65535 65535 65535Appendix B: Compared to EBImage
Loading images using RBioFormats should give the same results as using the EBImage package.
library("EBImage")
f <- system.file("images", "sample-color.png", package = "EBImage")
identical(readImage(f), as.Image(read.image(f)))## [1] TRUE