spotseg: Microarray Spot Segmentation

In spotSegmentation: Microarray Spot Segmentation and Gridding for Blocks of Microarray Spots

Description

Microarray spot segmentation via model-based clustering.

Usage

spotseg(chan1, chan2, rowcut, colcut, R=NULL, C=NULL, 
                  threshold=100, hc=FALSE, show=FALSE)

Arguments

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.

Details

There are plot and summary methods that can be applied to the result.

Value

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.

Note

The mclust package is requiredfor clustering.

References

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.

See Also

summary.spotseg, plot.spotseg, spotgrid

Examples

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)

Example output

Loading required package: mclust
Package 'mclust' version 5.3
Type 'citation("mclust")' for citing this R package in publications.
[1] 1 1

spotSegmentation documentation built on April 28, 2020, 9:18 p.m.