data(flu)Canine kidney cells were infected with human influenza, Udorn strain, either the wild type or a mutant which encodes a defective M2 protein. At twelve hours post-infection, membrane sheets were prepared and stained for viral proteins, using two antibodies conjugated to gold particles of two sizes (6 nanometre and 12 nanometre diameter) enabling localisation of two different proteins on each sheet. The 6 nm particles were stained for M2 (ion channel protein), while the 12 nm particles were stained either for M1 (matrix protein) or for HA (hemagglutinin). Membrane sheets were visualised in electron microscopy. Experimental technique and spatial analysis of the membranes stained for M2 and M1 is reported in Chen et al (2008). Analysis of the membranes stained for M2 and HA is reported in Rossman et al (2010). The M2-HA data shows a stronger association between the two proteins which has also been observed biochemically and functionally (Rossman et al, 2010).
  The dataset flu is a hyperframe
  with one row for each membrane sheet. The column named pattern
  contains the spatial point patterns of gold particle locations,
  with two types of points (either M1 and M2 or
  HA and M2). The column named virustype
  is a factor identifying the virus: either wild type wt
  or mutant mut1. The column named stain is a factor
  identifying whether the membrane was stained for
  M1 and M2 (stain="M2-M1") or stained for HA and M2
  (stain="M2-HA").
  The row names of the hyperframe are a succinct summary of
  the experimental conditions and can be used as labels
  in plots. See the Examples.
Rossman, J.S., Jing, X.H., Leser, G.P. and Lamb, R.A. (2010) Influenza virus M2 protein mediates ESCRT-independent membrane scission Cell 142, 902--913.
data(flu)
flu
Y <- flu$pattern[10]
Y <- flu[10, 1, drop=TRUE]
wildM1 <- with(flu, virustype == "wt" & stain == "M2-M1")
plot(flu[wildM1, 1, drop=TRUE], 
     main=c("flu data", "wild type virus, M2-M1 stain"),
     pch=c(3,16), cex=0.4, cols=2:3)Run the code above in your browser using DataLab