tmixFilter-class {flowClust} | R Documentation

## 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)

[Package flowClust version 3.30.0 Index]