flowClust (version 3.10.1)

tmixFilter: Creating Filters and Filtering Flow Cytometry Data

Description

The tmixFilter function creates a filter object which is then passed to the filter method that performs filtering on a flowFrame object. This method pair is provided to let flowClust integrate with the flowCore package.

Usage

tmixFilter(filterId="tmixFilter", parameters="", ...)

Arguments

filterId

A character string that identifies the filter created.

parameters

A character vector specifying the variables to be used in filtering. When it is left unspecified, all the variables of the flowFrame object are used when running filter. Note that its content will be passed to the varNames argument of flowClust when running filter.

...

Other arguments passed to the flowClust function when running filter, namely, expName, K, B, tol, nu, lambda, nu.est, trans, min.count, max.count, min, max, level, u.cutoff, z.cutoff, randomStart, B.init, tol.init, seed and criterion. All arguments are optional except K that specifies the number of clusters.

Value

The tmixFilter function returns an object of class tmixFilter that stores all the settings required for performing the filter operations.The filter method is defined in package flowCore and returns an object of class tmixFilterResult (or tmixFilterResultList if filter@K has a length >1) that stores the filtering results.

References

Lo, K., Brinkman, R. R. and Gottardo, R. (2008) Automated Gating of Flow Cytometry Data via Robust Model-based Clustering. Cytometry A 73, 321-332.

See Also

flowClust, summary, plot, density, hist, Subset, split, ruleOutliers, Map

Examples

### The example below largely resembles the one in the flowClust
### man page.  The main purpose here is to demonstrate how the
### entire cluster analysis can be done in a fashion highly
### integrated into flowCore.


data(rituximab)

### create a filter object
s1filter <- tmixFilter("s1", c("FSC.H", "SSC.H"), K=1)
### cluster the data using FSC.H and SSC.H
res1 <- filter(rituximab, s1filter)

### remove outliers before proceeding to the second stage
# %in% operator returns a logical vector indicating whether each
# of the observations lies inside the gate or not
rituximab2 <- rituximab[rituximab %in% res1,]
# a shorthand for the above line
rituximab2 <- rituximab[res1,]
# this can also be done using the Subset method
rituximab2 <- Subset(rituximab, res1)

### cluster the data using FL1.H and FL3.H (with 3 clusters)
s2filter <- tmixFilter("s2", c("FL1.H", "FL3.H"), K=3)
res2 <- filter(rituximab2, s2filter)

show(s2filter)
show(res2)
summary(res2)

# to demonstrate the use of the split method
split(rituximab2, res2)
split(rituximab2, res2, population=list(sc1=c(1,2), sc2=3))

# to show the cluster assignment of observations
table(Map(res2))

# to show the cluster centres (i.e., the mean parameter estimates
# transformed back to the original scale) and proportions
getEstimates(res2)

### demonstrate the use of various plotting methods
# a scatterplot
plot(rituximab2, res2, level=0.8)
plot(rituximab2, res2, level=0.8, include=c(1,2), grayscale=TRUE,
    pch.outliers=2)
# a contour / image plot
res2.den <- density(res2, data=rituximab2)
plot(res2.den)
plot(res2.den, scale="sqrt", drawlabels=FALSE)
plot(res2.den, type="image", nlevels=100)
plot(density(res2, include=c(1,2), from=c(0,0), to=c(400,600)))
# a histogram (1-D density) plot
plot(rituximab2, res2, "FL1.H")

### to demonstrate the use of the ruleOutliers method
summary(res2)
# change the rule to call outliers
ruleOutliers(res2) <- list(level=0.95)
# augmented cluster boundaries lead to fewer outliers
summary(res2)

# the following line illustrates how to select a subset of data 
# to perform cluster analysis through the min and max arguments;
# also note the use of level to specify a rule to call outliers
# other than the default
s2t <- tmixFilter("s2t", c("FL1.H", "FL3.H"), K=3, B=100, 
    min=c(0,0), max=c(400,800), level=0.95, z.cutoff=0.5)
filter(rituximab2, s2t)