coscore | R Documentation |
Compute Manders overlap coefficient (MOC), and Manders colocalization coefficients (M1 and M2), and Dice similarity coefficient.
coscore(x, y, threshold = NA)
x , y |
Images to be compared. These can be numeric or logical. If numeric, then the "overlap" is defined where both images are nonzero. |
threshold |
The intensity threshold to use when comparing the images. If |
The Dice coefficient and Manders overlap coefficient are symmetric between images, while M1 and M2 measure the overlap relative to x
and y
respectively.
A numeric vector with elements named "MOC", "M1", "M2", and "Dice", and an attribute named "threshold" giving the numeric thresholds (if applicable) for converting each image to a logical mask.
Kylie A. Bemis
K. W. Dunn, M. M. Kamocka, and J. H. McDonald. “A practical guide to evaluating colocalization in biological microscop.” American Journal of Physiology: Cell Physiology, vol. 300, no. 4, pp. C732-C742, 2011.
S. V. Costes, D. Daelemans, E. H. Cho, Z. Dobbin, G. Pavlakis, and S. Lockett. “Automatic and Quantitative Measurement of Protein-Protein Colocalization in Live Cells.” Biophysical Journal, vol. 86, no. 6, pp. 3993-4003, 2004.
K. H. Zou, S. K. Warfield, A. Bharatha, C. M. C. Tempany, M. R. Kaus, S. J. Haker, W. M. Wells, III, F. A. Jolesz, and R. Kikinis. “Statistical Validation of Image Segmentation Quality Based on a Spatial Overlap Index.” Academic Radiology, vol. 11, issue 2, pp. 178-189, 2004.
set.seed(1)
y <- x <- matrix(0, nrow=32, ncol=32)
x[5:16,5:16] <- 1
x[17:28,17:28] <- 1
x <- x + runif(length(x))
y[4:15,4:15] <- 1
y[18:29,18:29] <- 1
y <- y + runif(length(y))
xl <- x > median(x)
yl <- y > median(y)
coscore(x, x)
coscore(x, y)
coscore(x, y, threshold=median)
coscore(xl, yl)
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