spotseg {spotSegmentation} | R Documentation |
Microarray spot segmentation via model-based clustering.
spotseg(chan1, chan2, rowcut, colcut, R=NULL, C=NULL, threshold=100, hc=FALSE, show=FALSE)
chan1 |
matrix of pixel intensities from the first channel. |
chan2 |
matrix of pixel intensities from the second channel. |
rowcut |
row delimiters for the spots. Entries are the
starting row location in the close of each spot, with
the last entry being one pixel beyond the border of the
last spot. For example, from the output of spotgrid . |
colcut |
column delimiters for the spots. Entries are the
starting column location in the close of each spot, with
the last entry being one pixel beyond the border of the
last spot. For example, from the output of spotgrid . |
R |
rows over which the spots are to be segmented. The default is to segment spots in all rows. |
C |
columns over which the spots are to be segmented. The default is to segment spots in all columns. |
threshold |
connected components of size smaller than threshold
are ignored. Default: threshold=100 . |
hc |
logical variable indicating whether or not EM should be
initialized by hierarchical clustering or quantiles
in model-based clustering.
The default is to use quantiles hc = FALSE , which
is more efficient both in terms of speed and memory usage. |
show |
logical variable indicating whether or not to display
the segmentation of each individual spot as it is processed.
The default is not to display the spots show = FALSE . |
There are plot
and summary
methods that can
be applied to the result.
An array of the same dimensions as the image in which the pixels are labeled according to their group within the spot area: 1=background,2=uncertain,3=sample.
The mclust
package is requiredfor clustering.
Q. Li, C. Fraley, R. Bumgarner, K. Y. Yeung, and A. Raftery\ Robust model-based segmentation of microarray images,\ Technical Report No.~473, Department of Statistics, University of Washington, January 2005.
summary.spotseg
, plot.spotseg
,
spotgrid
data(spotSegTest) # columns of spotSegTest: # 1 intensities from the Cy3 (green) channel # 2 intensities from the Cy5 (red) channel dataTransformation <- function(x) (256*256-1-x)^2*4.71542407E-05 chan1 <- matrix(dataTransformation(spotSegTest[,1]), 144, 199) chan2 <- matrix(dataTransformation(spotSegTest[,2]), 144, 199) Grid <- spotgrid( chan1, chan2, rows = 4, cols = 6, show = TRUE) library(mclust) Seg <- spotseg( chan1, chan2, Grid$rowcut, Grid$colcut) plot(Seg) spotSummary <- summary(Seg) spot11 <- spotseg( chan1, chan2, Grid$rowcut, Grid$colcut, R = 1, C = 1, show = TRUE)