World’s largest Bioinformatics project (est. 2001)

• Analysis and comprehension of high-throughput genomic data
• Open source, open development
• > 20,000 papers in PubMed Central

Primarily a software repository

• 1383 R packages

Additionally

• Data and repository
• Publisher of supplementary materials
• Bioinformatics support forum
• Tutorials and instructional documentation

Provide a compelling user experience

• Package documentation (vignettes)
• Workflows

Turn users into developers

• Training on software development & programming paradigms
• Distributed development by domain experts

Scientific software vs. scientific publications

• Reproducible
• Open to peer-review
• Easy to access by other researchers & society
• Builds on the work of others

Image processing and analysis toolbox for R

• reading and writing of image files
• image manipulation, transformation and filtering
• object detection and feature extraction
• interactive image viewer

Since Bioconductor 1.8 (2006)

Original developers: Oleg Sklyar Wolfgang Huber Mike Smith Gregoire Pau

Contributors: Joseph Barry Bernd Fischer Ilia Kats Philip A. Marais

library("EBImage")

img <- readImage(system.file("images", "sample.png", package="EBImage"))
display(img)

Reading images

• local files or URLs
• supported file formats: JPEG, PNG and TIFF

For proprietary microscopy image data and metadata use aoles/RBioFormats (148 formats)

Displaying images

• interactive JavaScript viewer
• R's build-in plotting device

display(img, method = "raster")
text(x = 20, y = 20, label = "Parrots", adj = c(0,1), col = "orange", cex = 3)

dev.print(png, filename = "img.png", width = dim(img)[1], height = dim(img)[2])

writeImage(img, "img.jpeg", quality = 85)
files <- list.files(pattern = "image\\.")
data.frame(row.names = files, size = file.size(files))

##              size
## image.jpeg  49688
## image.png  361131

img

## Image 
##   colorMode    : Grayscale 
##   storage.mode : double 
##   dim          : 768 512 
##   frames.total : 1 
##   frames.render: 1 
## 
## imageData(object)[1:5,1:6]
##           [,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

str(img)

## Formal class 'Image' [package "EBImage"] with 2 slots
##   ..@ .Data    : num [1:768, 1:512] 0.447 0.451 0.463 0.455 0.463 ...
##   ..@ colormode: int 0

Algebraic operations: \(\alpha + \beta x^{\gamma}\)

x

x + 0.3

2 * x

x^2

1 – x

Spatial transformations

x[99:199,60:159]

rotate(x, 30)

resize(x, 192)

flop(x)

2D linear convolution (using FFT)

original

low-pass

high-pass

Median filter [1]

x

medianFilter(x)

[1] S. Perreault and P. Hebert (2007) Median Filtering in Constant Time, IEEE Trans Image Process 16 (9)

Thresholding

x

x > otsu(x)

thresh(x)

Object identification

bwlabel

watershed

propagate[1]

[1] T. Jones, A. Carpenter and P. Golland (2005) Voronoi-Based Segmentation of Cells on Image Manifolds, CVBIA05

Using multivariate analysis methods we can:

• detect cell subpopulations (clustering)
• classify cells into pre-defined cell types or phenotypes (classification)
• based on the frequencies of the subpopulations compare different biological conditions

Fluorescent microscopy images of perturbed HeLa cells [1]

dna <- readImage("nuclei.tif")
tub <- readImage("cells.tif")
rgb <- rgbImage(green=1.5*tub, blue=dna)

dna

tub

rgb

[1] Fuchs, F. et al. Clustering phenotype populations by genome-wide RNAi and multiparametric imaging. Molecular Systems Biology 6 (2010).

• Adaptive thresholding of the DNA channel
• Morphological opening and filling of holes
• Distance map computation and watershed transformation

nmaskt <- thresh(dna, w=15, h=15, offset=0.05)
nmaskf <- opening(nmaskt, makeBrush(5, shape='disc'))
nmask  <- watershed( distmap(nmaskf) )

nmaskt

nmaskf

colorLabels(nmask)

Identification of cell bodies by Voronoi partitioning using the segmented nuclei as seeds

cmaskt <- closing( gblur(tub, 1) > 0.105, makeBrush(5, shape='disc') )
cmask  <- propagate(tub, seeds=nmask, mask=cmaskt, lambda=0.001)
segmented <- paintObjects(cmask, rgb, col="magenta")
segmented <- paintObjects(nmask, segmented, col="yellow")

cmaskt

colorLabels(cmask)

segmented

display(stackObjects(cmask, rgb), all = TRUE, nx = 11)

Quantitative cellular descriptors

• shape characteristics (area, perimeter, radius)
• moments (center of mass, eccentricity, …)
• basic statistics on pixel intensities
• Haralick[1] textural features

head( computeFeatures.shape(cmask, tub) )

##   s.area s.perimeter s.radius.mean s.radius.sd s.radius.min s.radius.max
## 1   4216         408      43.52530   14.254634     16.13319     73.48543
## 2   3079         263      34.60167    9.061043     19.14618     53.49633
## 3   3088         211      30.99363    4.630083     16.81697     38.91545
## 4   2377         209      29.37053    8.030861     16.27813     44.85961
## 5   2643         277      34.75310   13.817002     15.03601     62.17141
## 6   2095         181      26.82256    6.948619     16.26868     40.91585

[1] R M Haralick, K Shanmugam and Its'Hak Deinstein (1979) Textural Features for Image Classification, IEEE Transactions on Systems, Man and Cybernetics.

Quantitative cellular descriptors

• shape characteristics (area, perimeter, radius)
• moments (center of mass, eccentricity, …)
• basic statistics on pixel intensities
• Haralick[1] textural features

Each of the 1820 nodes represents a perturbation and is characterized by a phenoprint derived from phenotypic profiles, comprising relative proportions of each cell class and population summaries of cellular descriptors. Groups of nodes with small distances form clusters and are colored according to their most prominent cell subpopulations.

Huber Group @ Heidelberg

rpubs.com/aoles/whyR

@andrzejkoles