Apply a Function over values in a flowSet

fsApply, like many of the apply-style functions in R, acts as an iterator for flowSet objects, allowing the application of a function to either the flowFrame or the data matrix itself. The output can then be reconstructed as either a flowSet, a list, or a matrix depending on options and the type of objects returned.

fsApply(x, FUN, ..., simplify=TRUE, use.exprs=FALSE)

Arguments

x	flowSet to be used
FUN	the function to be applied to each element of x
...	optional arguments to FUN.
simplify	logical (default: TRUE); if all true and all objects are flowFrame objects, a flowSet object will be constructed. If all of the values are of the same type there will be an attempt to construct a vector or matrix of the appropriate type (e.g. all numeric results will return a matrix).
use.exprs	logical (default: FALSE); should the FUN be applied on the flowFrame object or the expression values.

See also

apply, sapply

Examples

fcs.loc <- system.file("extdata",package="flowCore")
file.location <- paste(fcs.loc, dir(fcs.loc), sep="/")
samp <- read.flowSet(file.location[1:3])

#Get summary information about each sample.
fsApply(samp,summary)
#>             FSC-H     SSC-H       FL1-H       FL2-H       FL3-H     FL1-A
#> Min.      85.0000   11.0000    1.000000    1.000000    1.000000    0.0000
#> 1st Qu.  385.0000  141.0000    8.131230   12.079007    2.246790    0.0000
#> Median   441.0000  189.0000  135.164850   22.467901    5.674221   26.0000
#> Mean     491.9644  277.9105  157.794167  105.986366    8.464880   34.0766
#> 3rd Qu.  518.0000  270.0000  241.441822   50.936752   10.746078   51.0000
#> Max.    1023.0000 1023.0000 3651.741273 9910.458562 3278.121151 1023.0000
#> Min.      86.0000   25.0000    1.000000    1.000000    1.000000    0.0000
#> 1st Qu.  380.0000  141.0000  170.007762    7.233942    1.684855   34.0000
#> Median   439.0000  191.0000  289.026391   12.298262    3.924190   63.0000
#> Mean     490.9320  274.2957  353.876012   15.741184    8.454397   80.8601
#> 3rd Qu.  541.0000  287.0000  441.094101   19.282185    7.914755  102.0000
#> Max.    1023.0000 1023.0000 9560.239011  632.093392 9910.458562 1023.0000
#> Min.      86.0000   18.0000    1.000000    1.000000    1.000000    0.0000
#> 1st Qu.  375.0000  139.0000    6.208244   90.579778    1.625315    0.0000
#> Median   430.0000  180.0000   10.000000  632.093392    5.139697    0.0000
#> Mean     461.7478  250.6101   32.455961  841.672178   24.906182    4.5589
#> 3rd Qu.  493.2500  244.0000   16.399996 1357.727142   13.097473    0.0000
#> Max.    1023.0000 1023.0000 9910.458562 9910.458562 9910.458562 1023.0000
#>              FL4-H    Time
#> Min.       1.00000   1.000
#> 1st Qu.    6.61169 122.000
#> Median    12.29826 288.000
#> Mean     140.39784 294.770
#> 3rd Qu.   33.37625 457.500
#> Max.    9821.71889 626.000
#> Min.       1.00000   1.000
#> 1st Qu.   24.58244  96.000
#> Median    42.93510 226.000
#> Mean     108.16628 233.622
#> 3rd Qu.   78.43886 363.000
#> Max.    9910.45856 504.000
#> Min.       1.00000   7.000
#> 1st Qu.   25.48297  82.000
#> Median    44.70901 197.000
#> Mean    1028.22774 209.972
#> 3rd Qu.  105.54496 333.250
#> Max.    9910.45856 464.000

#Obtain the median of each parameter in each frame.
fsApply(samp,each_col,median)
#>                FSC-H SSC-H    FL1-H     FL2-H    FL3-H FL1-A    FL4-H Time
#> 0877408774.B08   441   189 135.1649  22.46790 5.674221    26 12.29826  288
#> 0877408774.E07   439   191 289.0264  12.29826 3.924190    63 42.93510  226
#> 0877408774.F06   430   180  10.0000 632.09339 5.139697     0 44.70901  197