Colocalization in cellular immunofluorescence images with R

Colocalization of two proteins is often used in biology to predict functional relationship between them. In addition, we want to look at the colocalization protein with some intracellular organelle to access the intracellular location of the proteins. Recently, I came across such a problem in my work and decided to learn analysis of cellular immunoflourescence images. Then I stumbled upon the EBImage bioconductor package. Using this package, I found it very easy and convienient to do the image analysis. Here is the short tutorial of how to find strength of colocalization of two colours in an Image.

Colocalize <- function(Image, colors = c("red", "green"), progressbar = FALSE, plot = FALSE) {
suppressPackageStartupMessages(library(EBImage))
library(ggplot2)
library(ggthemes)
## ==================== Checking the Input arguments========================= ###
if (!is.Image(Image)) {
stop("Enter an Image object as input argument")
}
if (colors[1] == "red" && colors[2] == "green" | colors[1] == "green" && colors[2] == "red") {
x <- as.vector(imageData(Image)[, , 1])
y <- as.vector(imageData(Image)[, , 2])
} else if (colors[1] == "red" && colors[2] == "blue" | colors[1] == "blue" && colors[2] == "red") {
x <- as.vector(imageData(Image)[, , 1])
y <- as.vector(imageData(Image)[, , 3])
} else if (colors[1] == "green" && colors[2] == "blue" | colors[1] == "blue" && colors[2] == "green") {
x <- as.vector(imageData(Image)[, , 2])
y <- as.vector(imageData(Image)[, , 3])
} else {
stop("Enter Valid colours")
}
## ==================== Pearson Correlation ========================##
PCC <- cor.test(x, y)$estimate
names(PCC) <- NULL
### =================== Manders coeffients =========================##
M1 <- sum(x[y > 0])/sum(x)
M2 <- sum(y[x > 0])/sum(y)
Manders <- list(M1, M2)
names(Manders) <- colors
## ==================== Costes Approach ============================###
vect <- rep(0, 100)
if (progressbar) {
## Whether to show progress bar or not
pb <- txtProgressBar(min = 0, max = 100, style = 3) ## sets progress bar while running function
}
for (i in 1:100) {
sample_x <- sample(x, length(x), replace = FALSE)
sample_y <- sample(y, length(y), replace = FALSE)
vect[i] <- cor.test(sample_x, sample_y)$estimate
if (progressbar) {
setTxtProgressBar(pb, i)
}
}
if (progressbar) {
close(pb)
}
Costes <- sum(abs(vect) < abs(PCC))/100
## ===================== Visualization =============================##
if (plot) {
dat <- data.frame(Red = x, Green = y)
lines <- data.frame(x = seq(0, 1, by = 0.01), y = seq(0, 1, by = 0.01))
p <- ggplot(dat, aes(Red, Green)) + geom_point(colour = "blue", size = 0.8) + geom_line(data = lines,
aes(x, y), colour = "red") + theme_gdocs() + labs(title = "Scatter Plot of Pixel Intensities")
print(p)
}
## ===================== OUTPUT ====================================##
return(list(PCC = PCC, Costes = Costes, Manders = Manders))
}

The following example demonstrates the usage of the function. This is a trial image from internet. First, lets manually examine the image. It seems more likely that where ever red is present green is also there and viceversa. So, this is a good image to start with the colocalization analysis. Now read only the merged portion of this image into R workspace as shown below and we can find the strength of colocalization by using the above function.

Colocalization using R

library(EBImage)
Img <- readImage("Trial1.jpg")
Colocalize(Img, colors=c("red","green"),progressbar=FALSE,plot=TRUE)

Colocalization using R

$PCC [1] 0.9364601

$Costes [1] 1

Manders$red [1] 0.9989995

green [1] 0.8590056

The coefficients shown above are Pearson correlation coefficient(PCC) for the intensities between two colors. Costes coefficient is PCC done 100 times with randomized images and calculating how many times the original one is greater than randomized one. Manders cofficients are calculated for two colors seperately. For further details visit this link.