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###  R Lab 2 5/14/10 - Flow Cytometry
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## Load the appropriate libraries

library(flowCore)
library(flowViz)
library(flowStats)

## Load in and summarize the data

file.name <- system.file("extdata", "0877408774.B08", package = "flowCore") 
x <- read.FCS(file.name, transformation = FALSE) 
summary(x)

## Linearize and scale the data when loading 

file.name <- system.file("extdata", "0877408774.B08", package = "flowCore") 
x <- read.FCS(file.name, transformation = "scale") 
summary(x)


# Make some plots

plot(x)
plot(x, c("FL1-H", "FL2-H"))
plot(x, "FL1-H", breaks = 256) 


# Read in a flowSet object with 5 experiments measuring the same parameters

fs <- read.flowSet(path = system.file("extdata", "compdata", 
 "data", package = "flowCore"), name.keyword = "SAMPLE ID", 
 phenoData = list(name = "SAMPLE ID", Filename = "$FIL"))

fs

# Do some gatting
rectGate <- rectangleGate(filterId = "Fluorescence Region", "FL1-H" = c(50,100), "FL2-H" = c(50, 100))
rectGate

# Apply to the first experiment
result = filter(fs[[1]], rectGate) 
result

summary(result)

# Number of events(cells)
summary(result)$n

# Number of events in the gate
summary(result)$true

# Percent of events in the gate
summary(result)$p

# Other types of gates are rectangleGate, polygonGate,polytopeGate


# Apply to all experiments:
filter(fs, rectGate)

# Set a gate that picks out the big ellipse in the
# FSC/SSC channels. 
morphGate <- norm2Filter(filterId = "MorphologyGate", "FSC-H", "SSC-H", scale = 2)

# Subset the data, in other words, select only the
# cells in the morphGate, and create a new
# object that only has the data for the cells in the gate
smaller <- Subset(fs, morphGate)

# Check that smaller has fewer events than fs
fs[[1]]
smaller[[1]]


# You can combine filters

rectGate & morphGate 
rectGate | morphGate 
!morphGate


# Make a contour plot with 20 levels
# on the data from the 1st experiment
# only the 1st 2 channels. 

contour(fs[[1]],1:2,nlevels=20)

# Make a flow plot
flowPlot(fs[[1]])