Compute Statistics - GatingSet Method

Source: R/cyto_stats_compute-methods.R

Calculate and export flow cytometry statistics for a GatingSet.

# S4 method for GatingSet
cyto_stats_compute(x, alias = NULL,
  parent = NULL, channels = NULL, trans = NULL, stat = "median",
  density_smooth = 1.5, save = TRUE)

Arguments

x

object of class GatingSet.
alias

name(s) of the population(s) for which the statistic should be calculated.
parent

name(s) of the parent population(s) used calculate population frequencies. The frequency of alias in each parent will be returned as a percentage.
channels

names of of channels for which statistic should be calculated, set to all channels by default.
trans

object of class transformList or transformerList generated by estimateLogicle used to transform the fluorescent channels of x. The transformation list is required to apply the inverse transformation such that the required statistics are returned on the original linear scale.
stat

name of the statistic to calculate, options include "count", "freq", "median", "mode", "mean", "geo mean", "CV", or "freq".
density_smooth

smoothing parameter passed to density when calculating mode, set to 1.5 by default.
save

logical indicating whether statistical results should be saved to a csv file.

See also

cyto_stats_compute,flowFrame-method

cyto_stats_compute,flowSet-method

Examples

library(CytoRSuiteData)

# Load in samples
fs <- Activation
gs <- GatingSet(fs)
#> .
#> .
#> .
#> .
#> done!

# Apply compensation
gs <- compensate(gs, fs[[1]]@description$SPILL)

# Transform fluorescent channels
trans <- estimateLogicle(gs[[4]], cyto_fluor_channels(gs))
gs <- transform(gs, trans)

# Gate using gate_draw
gt <- Activation_gatingTemplate
gating(gt, gs)
#> Preprocessing for 'Cells'
#> Gating for 'Cells'
#> done.
#> Preprocessing for 'Single Cells'
#> Gating for 'Single Cells'
#> done.
#> Preprocessing for 'Live Cells'
#> Gating for 'Live Cells'
#> done.
#> Preprocessing for 'Dendritic Cells'
#> Gating for 'Dendritic Cells'
#> done.
#> Preprocessing for 'T Cells'
#> Gating for 'T Cells'
#> done.
#> Preprocessing for 'CD8 T Cells'
#> Gating for 'CD8 T Cells'
#> done.
#> Preprocessing for 'CD69+ CD8 T Cells'
#> Gating for 'CD69+ CD8 T Cells'
#> done.
#> Preprocessing for 'CD4 T Cells'
#> Gating for 'CD4 T Cells'
#> done.
#> Preprocessing for 'CD69+ CD4 T Cells'
#> Gating for 'CD69+ CD4 T Cells'
#> done.
#> finished.

# Compute statistics
cyto_stats_compute(gs,
  alias = "T Cells",
  channels = c("Alexa Fluor 488-A", "PE-A"),
  stat = "median",
  save = FALSE
)
#> $`T Cells`
#>                 Alexa Fluor 488-A     PE-A
#> Activation1.fcs          4180.007 18403.25
#> Activation2.fcs          5425.652 18804.82
#> Activation3.fcs          6591.920 19982.24
#> Activation4.fcs          6640.713 20150.04
#> 

cyto_stats_compute(gs,
  alias = "T Cells",
  stat = "geo mean",
  save = FALSE
)
#> $`T Cells`
#>                    FSC-A    FSC-H    FSC-W    SSC-A    SSC-H    SSC-W
#> Activation1.fcs 63148.35 56239.63 73586.73 16843.02 15607.64 70723.31
#> Activation2.fcs 64327.43 57226.32 73668.25 17327.90 15965.97 71126.35
#> Activation3.fcs 63909.59 56645.13 73940.67 17530.46 16083.35 71432.66
#> Activation4.fcs 68037.33 59457.69 74992.73 19594.85 17684.49 72615.49
#>                 Alexa Fluor 488-A     PE-A PE-Texas Red-A  7-AAD-A PE-Cy7-A
#> Activation1.fcs          1217.322 17341.62      137.51647 317.9252 42.42112
#> Activation2.fcs          1385.521 17718.57       94.35408 363.7834 48.84971
#> Activation3.fcs          1656.352 18853.10       78.29665 423.6506 41.93059
#> Activation4.fcs          1790.204 18974.67       71.80785 640.7608 47.10880
#>                 Alexa Fluor 405-A Alexa Fluor 430-A Qdot 605-A
#> Activation1.fcs          57.90289          64.22004   513.5104
#> Activation2.fcs          64.14434          67.69243   543.4810
#> Activation3.fcs          69.36829          74.87729   608.3986
#> Activation4.fcs          77.96001          81.71592   613.3975
#>                 Alexa Fluor 647-A Alexa Fluor 700-A APC-Cy7-A     Time
#> Activation1.fcs          604.3573          391.3962  4.251436 2123.737
#> Activation2.fcs          615.4023          384.3474  4.379078 2005.908
#> Activation3.fcs          704.4741          347.3969  1.626191 1976.461
#> Activation4.fcs          949.2146          361.1098  6.565321 2086.956
#> 

cyto_stats_compute(gs,
  alias = c("CD4 T Cells", "CD8 T Cells"),
  stat = "count",
  save = FALSE
)
#> $`CD4 T Cells`
#>                 count
#> Activation1.fcs  1955
#> Activation2.fcs  2212
#> Activation3.fcs  2134
#> Activation4.fcs  2133
#> 
#> $`CD8 T Cells`
#>                 count
#> Activation1.fcs  2636
#> Activation2.fcs  3191
#> Activation3.fcs  3585
#> Activation4.fcs  3562
#> 

cyto_stats_compute(gs,
  alias = c("CD4 T Cells", "CD8 T Cells"),
  parent = c("Live Cells", "T Cells"),
  stat = "freq",
  save = FALSE
)
#> $`CD4 T Cells`
#>                 count Live Cells  T Cells
#> Activation1.fcs  1955   13.08392 38.85135
#> Activation2.fcs  2212   13.44926 37.67672
#> Activation3.fcs  2134   12.58255 34.16040
#> Activation4.fcs  2133   12.83239 33.71799
#> 
#> $`CD8 T Cells`
#>                 count Live Cells  T Cells
#> Activation1.fcs  2636   17.64155 52.38474
#> Activation2.fcs  3191   19.40171 54.35190
#> Activation3.fcs  3585   21.13797 57.38755
#> Activation4.fcs  3562   21.42943 56.30730
#>