flowClust.R
In flowClust: Clustering for Flow Cytometry

## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, fig.width = 8, fig.height = 6)

## ----stage0, results = 'hide', message = FALSE--------------------------------
library(flowClust)
library(flowCore)

## ----stage1-------------------------------------------------------------------
data(rituximab)
summary(rituximab)
res1 <-
	flowClust(
		rituximab,
		varNames = c("FSC.H", "SSC.H"),
		K = 1,
		B = 100
	)

## ----stage2-------------------------------------------------------------------
rituximab2 <- rituximab[rituximab %in% res1, ]
res2 <-
	flowClust(
		rituximab2,
		varNames = c("FL1.H", "FL3.H"),
		K = 1:6,
		B = 100
	)

## ----stage2Result-------------------------------------------------------------
criterion(res2, "BIC")
summary(res2[[3]])

## ----stage2ChangeRule1--------------------------------------------------------
ruleOutliers(res2[[3]]) <- list(level = 0.95)
summary(res2[[3]])

## ----stage2ChangeRule2--------------------------------------------------------
ruleOutliers(res2[[3]]) <- list(z.cutoff = 0.6)
summary(res2[[3]])

## ----stage2Alternative--------------------------------------------------------
flowClust(
	rituximab2,
	varNames = c("FL1.H", "FL3.H"),
	K = 2,
	B = 100,
	min = c(0, 0),
	max = c(400, 800)
)

## ----stage2Scatter------------------------------------------------------------
plot(res2[[3]],
	  data = rituximab2,
	  level = 0.8,
	  z.cutoff = 0)

## ----stage2Contour------------------------------------------------------------
res2.den <- density(res2[[3]], data = rituximab2)
plot(res2.den)

## ----stage2Image--------------------------------------------------------------
plot(res2.den, type = "image")

## ----stage2Hist---------------------------------------------------------------
hist(res2[[3]], data = rituximab2, subset = "FL1.H")

## ----stage2Hist2--------------------------------------------------------------
hist(res2[[3]], data = rituximab2, subset = 1)

## ----stage2f------------------------------------------------------------------
s2filter <- tmixFilter("s2filter", c("FL1.H", "FL3.H"), K = 3, B = 100)
res2f <- filter(rituximab2, s2filter)

## ----stage2fSubsetting, warning=FALSE-----------------------------------------
Subset(rituximab2, res2f)
split(rituximab2, res2f, population = list(sc1 = 1:2, sc2 = 3))

## ----stage2fRectGate----------------------------------------------------------
rectGate <-
	rectangleGate(
		filterId = "rectRegion",
		"FL1.H" = c(0, 400),
		"FL3.H" = c(0, 800)
	)
MBCfilter <- tmixFilter("MBCfilter", c("FL1.H", "FL3.H"), K = 2, B = 100)
filter(rituximab2, MBCfilter %subset% rectGate)

## ----prior--------------------------------------------------------------------
set.seed(100)
prior <- flowClust2Prior(res2[[2]], kappa = 1, Nt = 5000)
prior2 <- prior
prior2$Mu0[1, ] <- rep(box(200, prior2$lambda), 2)
prior2$Lambda0 <- prior2$Lambda0 / 2
pfit2 <-
	flowClust(
		rituximab2,
		varNames = c("FL1.H", "FL3.H"),
		K = 2,
		prior = prior2,
		usePrior = "yes"
	)

par(mfrow = c(1, 2))
plot(res2[[2]], data = rituximab2)
plot(pfit2, data = rituximab2)