pcomb: Pair Correlation of Molecular Brightness

Description

Performs the pair correlation of molecular brightness (pCOMB) analysis.

Usage

pcomb(img, nPoints = 25000, one.col = FALSE, dr = 1, w = 100)

Arguments

img

The image to analyze.

nPoints

The size of the sub-vectors in which the input vectors will be divided. This number must be less than N/2.

one.col

By default FALSE. If TRUE the correlation will be performed in the fixed colum mode, else the distance mode.

Is the distance between the two columns that will be correlated. For a value of deltar = 3, the columns are correlated as follows: (1,4), (2,5), ..., (n-3, n), with n as the last column.

Range value that is used to calculate the brightness in the image.

Value

A list containing the Brightness Carpet and the Pair Correlation of that carpet

Details

With the Pair Correlation of Molecular Brightness (pCOMB) method, one can distinguish between different homo-oligomeric species of the same molecule coexisting in the same microenvironment, while separately and specifically tracking each species' moblity across the cellular compartments. This technique amplifies the signal from the brightest species present and filters the dynamics of the extracted oligomeric population based on arrival time between two locations. This method is suitable for mapping the impact oligomerization on transcription factor dynamics. The resulting intensity fluctuations, pCF, are transformed into brightness fluctuations using B = (sigma^2)/mean, and the pair correlation analysis is then performed on the brightness fluctuations along the line scan , at a distance (delta(r)).

If the pcf is set as FALSE the pComb data will not be generated and will be NULL. In order to generate that data the pcf function must be used on the BCarpet data.

Examples

Run this code

# NOT RUN {
### Load the FCSlib package

library(FCSlib)

# As an example, we will use a data set that corresponds to a population of Venus dimers
# diffusing in HEK-293 cells. Use the readFileTiff() function to extract the information
# from the '.tiff' files.

dmv2 <- data.matrix(V2)
pC <- pcomb(dmv2[1:32,1:2001], nPoints = 1000, type = 'd', dr = 10, w = 2, pcf = FALSE)
dmv2 <- data.matrix(v2DataSet)
pC <- pcomb(dmv2, nPoints = 5000, type = 'd', dr = 10, w = 100)
di <- dim(pC$pComb)
tau <- (1:(di[2]))

# Plot the result
library("fields")
image.plot( x = 1:di[1], y = log10(tau), z = pC$pComb, main = "pComb",
xlab = "Pixel", ylab = "Logarithmic tau",
cex.lab = 1.2, cex.main = 1.2, cex.axis = 1)
# }