# Sample processing of FCS .csv file
# efg, 6 July 2005, Stowers Institute.

fcs <- read.csv("http://research.stowers-institute.org/efg/ScientificSoftware/Utility/FCSExtract/CC4_067_BM.csv")
dim(fcs)
names(fcs)

# 1.  Histogram
hist(fcs$PE.Cy7, breaks=128,
  xlab="Channel", ylab="Count",
  main="Histogram (12-bit) of PE.Cy7")

# 2.  Histogram as line graph
h <- hist(round(fcs$PE.Cy7), breaks=128, plot=FALSE)
plot(h$mids, h$counts, type="l",
  xlab="Channel", ylab="Count",
  main="PE.Cy7")

# 3. PE.Cy7 Vs FITC scatterplot
plot(fcs$FITC, fcs$PE.Cy7, pch=".",
  main="PE.Cy7 Vs FITC")


# 4. Use color coding to show "density" of frequencies

# Force from 0:4095 (12-bit) to 0:255 (8-bit)
i <- 1 + trunc(fcs$PE.Cy7 / 16)
j <- 1 + trunc(fcs$FITC / 16)

# Use 256-by-256 matrix to form image
m <- matrix(0, 256, 256)

for (k in 1:length(i))
{
  m[j[k], i[k]] <- m[j[k], i[k]] + 1
}

image(m, col=c("white", rainbow(16)), axes=F,
    xlab="FITC", ylab="PE.Cy7",
    main="PE.Cy7 Vs FITC")

axis(SOUTH<-1, at=seq(0,1,length=9), labels=256*seq(0,1,length=9))
axis(WEST <-2, at=seq(0,1,length=9), labels=256*seq(0,1,length=9))