tmixFilter: Creating Filters and Filtering Flow Cytometry Data

In RGLab/flowClust: Clustering for Flow Cytometry

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 = "", ...)

## S4 method for signature 'ANY,flowClust'
x %in% table

## S4 method for signature 'flowFrame,tmixFilterResult'
x %in% table

## S4 method for signature 'flowFrame,tmixFilter'
x %in% table

## S4 method for signature 'ANY,tmixFilterResult'
x %in% table

## S4 method for signature 'ANY,flowClustList'
x %in% table

## S4 method for signature 'ANY,tmixFilterResultList'
x %in% table

## S4 method for signature 'flowFrame,flowClust'
x[i, j, ..., drop = FALSE]

## S4 method for signature 'flowFrame,tmixFilterResult'
x[i, j, ..., drop = FALSE]

## S4 method for signature 'flowFrame,flowClustList'
x[i, j, ..., drop = FALSE]

## S4 method for signature 'flowFrame,tmixFilterResultList'
x[i, j, ..., drop = FALSE]

## S4 method for signature 'tmixFilterResultList,ANY'
x[[i, j, ..., exact = TRUE]]

## S4 method for signature 'tmixFilterResultList'
length(x)

## S4 method for signature 'tmixFilterResult,tmixFilter'
summarizeFilter(result, filter)

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. 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 has a length >1) that stores the filtering results. The tmixFilter function returns an object of class tmixFilter that extends the virtual parent filter class in the flowCore package. Hence, the filter operators, namely, &, |, ! and subset, also work for the tmixFilter class. If filter is of length 1, the filter method returns an object of class tmixFilterResult. This class extends both the multipleFilterResult class (in the flowCore package) and the flowClust class. Operations defined for the multipleFilterResult class, like %in%, Subset and split, also work for the tmixFilterResult class. Likewise, methods or functions designed to retrieve filtering (clustering) information from a flowClust object can also be applied on a tmixFilterResult object. These include criterion, ruleOutliers, ruleOutliers<-, Map, posterior, importance, uncertainty and getEstimates. Various functionalities for plotting the filtering results are also available (see the links below). If filter has a length >1, the function returns an object of class tmixFilterResultList. This class extends both the flowClustList class and the multipleFilterResult class. Note that when a tmixFilterResultList object is used in place of a tmixFilterResult object, in most cases the list element corresponding to the best model will be extracted and passed to the method/function call.
x	flowFrame
table	tmixFilterResult
i	tmixFilterResult or tmixFilterResultList
j, drop, exact	not used
result	tmixFilterResult
filter	tmixFilter

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)
library(flowCore)

### 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)

RGLab/flowClust documentation built on Jan. 31, 2024, 11:26 p.m.