Compute, export and save statistics

Source: R/cyto_stats_compute.R

Compute, export and save statistics

# S3 method for GatingSet
cyto_stats_compute(
  x,
  alias = NULL,
  parent = NULL,
  channels = NULL,
  trans = NA,
  stat = "median",
  format = "long",
  save_as = NULL,
  select = NULL,
  density_smooth = 0.6,
  ...
)

# S3 method for GatingHierarchy
cyto_stats_compute(
  x,
  alias = NULL,
  parent = NULL,
  channels = NULL,
  trans = NA,
  stat = "median",
  format = "long",
  save_as = NULL,
  density_smooth = 0.6,
  ...
)

# S3 method for flowSet
cyto_stats_compute(
  x,
  channels = NULL,
  trans = NA,
  stat = "median",
  gate = NA,
  format = "long",
  select = NULL,
  density_smooth = 0.6,
  ...
)

# S3 method for flowFrame
cyto_stats_compute(
  x,
  channels = NULL,
  trans = NA,
  stat = "median",
  gate = NA,
  format = "long",
  density_smooth = 0.6,
  ...
)

Arguments

x

object of class flowFrame, flowSet, GatingHierarchy or GatingSet.
alias

name(s) of the population(s) for which the statistic should be calculated when a GatingHierarchy or GatingSet is supplied.
parent

name(s) of the parent population(s) used calculate population frequencies when a GatingHierarchy or GatingSet object is supplied. 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 transformerList used to transfom the channels of the supplied data. The transformerList is required to return the data to the original linear scale when calculating statistics.
stat

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

indicates whether the data should be returned in the "wide" or "long" format, set to the "long" format by default.
save_as

name of a csv file to which the statistical results should be saved.
select

named list containing experimental variables to be used to select samples using cyto_select when a flowSet or GatingSet is supplied. Refer to cyto_select for more details.
density_smooth

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

not in use.
gate

object of class rectangleGate, polygonGate or ellipsoidGate to apply to flowFrame or flowSet objects prior to computing statistics.

Value

a tibble containing the computed statistics in the wide or long format.

Author

Dillon Hammill, Dillon.Hammill@anu.edu.au

Examples

library(CytoExploreRData)

# Load in samples
fs <- Activation
gs <- GatingSet(fs)

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

# Transform fluorescent channels
trans <- estimateLogicle(gs[[32]], cyto_fluor_channels(gs))
gs <- transform(gs, trans)

# Gate using cyto_gate_draw
gt <- Activation_gatingTemplate
gt_gating(gt, gs)

#> Preprocessing for 'Cells'
#> Gating for 'Cells'
#> done!
#> done.
#> Preprocessing for 'Single Cells'
#> Gating for 'Single Cells'
#> done!
#> done.
#> Preprocessing for 'Dead Cells'
#> Gating for 'Dead Cells'
#> done!
#> done.
#> Live Cells gating...
#> done!
#> done.
#> Preprocessing for 'T Cells'
#> Gating for 'T Cells'
#> done!
#> done.
#> Preprocessing for 'CD8 T Cells'
#> Gating for 'CD8 T Cells'
#> done!
#> done.
#> Preprocessing for 'CD69+ CD8 T Cells'
#> Gating for 'CD69+ CD8 T Cells'
#> done!
#> done.
#> Preprocessing for 'CD4 T Cells'
#> Gating for 'CD4 T Cells'
#> done!
#> done.
#> Preprocessing for 'CD69+ CD4 T Cells'
#> Gating for 'CD69+ CD4 T Cells'
#> done!
#> done.
#> Preprocessing for 'Dendritic Cells'
#> Gating for 'Dendritic Cells'
#> done!
#> done.
#> finished.

# Compute statistics - median
cyto_stats_compute(gs,
  alias = "T Cells",
  channels = c("Alexa Fluor 488-A", "PE-A"),
  stat = "median",
  save = FALSE
)

#> # A tibble: 66 x 6
#>    name             OVAConc Treatment Population Marker  MedFI
#>    <fct>            <chr>   <chr>     <chr>      <chr>   <dbl>
#>  1 Activation_1.fcs 0       Stim-A    T Cells    CD8     1501.
#>  2 Activation_1.fcs 0       Stim-A    T Cells    Va2    17651.
#>  3 Activation_2.fcs 0       Stim-A    T Cells    CD8     2238.
#>  4 Activation_2.fcs 0       Stim-A    T Cells    Va2    18608.
#>  5 Activation_3.fcs 5       Stim-A    T Cells    CD8     1541.
#>  6 Activation_3.fcs 5       Stim-A    T Cells    Va2    18946.
#>  7 Activation_4.fcs 5       Stim-A    T Cells    CD8      567.
#>  8 Activation_4.fcs 5       Stim-A    T Cells    Va2    19885.
#>  9 Activation_5.fcs 50      Stim-A    T Cells    CD8     1074.
#> 10 Activation_5.fcs 50      Stim-A    T Cells    Va2    20104.
#> # ... with 56 more rows

# Compute statistics for experimental group
cyto_stats_compute(gs,
  alias = "T Cells",
  channels = c("Alexa Fluor 488-A", "PE-A"),
  stat = "median",
  save = FALSE,
  select = list(Treatment = "Stim-A")
)

#> # A tibble: 16 x 6
#>    name             OVAConc Treatment Population Marker  MedFI
#>    <fct>            <chr>   <chr>     <chr>      <chr>   <dbl>
#>  1 Activation_1.fcs 0       Stim-A    T Cells    CD8     1501.
#>  2 Activation_1.fcs 0       Stim-A    T Cells    Va2    17651.
#>  3 Activation_2.fcs 0       Stim-A    T Cells    CD8     2238.
#>  4 Activation_2.fcs 0       Stim-A    T Cells    Va2    18608.
#>  5 Activation_3.fcs 5       Stim-A    T Cells    CD8     1541.
#>  6 Activation_3.fcs 5       Stim-A    T Cells    Va2    18946.
#>  7 Activation_4.fcs 5       Stim-A    T Cells    CD8      567.
#>  8 Activation_4.fcs 5       Stim-A    T Cells    Va2    19885.
#>  9 Activation_5.fcs 50      Stim-A    T Cells    CD8     1074.
#> 10 Activation_5.fcs 50      Stim-A    T Cells    Va2    20104.
#> 11 Activation_6.fcs 50      Stim-A    T Cells    CD8     2222.
#> 12 Activation_6.fcs 50      Stim-A    T Cells    Va2    20075.
#> 13 Activation_7.fcs 500     Stim-A    T Cells    CD8     4823.
#> 14 Activation_7.fcs 500     Stim-A    T Cells    Va2    20493.
#> 15 Activation_8.fcs 500     Stim-A    T Cells    CD8     4369.
#> 16 Activation_8.fcs 500     Stim-A    T Cells    Va2    20926.

# Compute population frequencies and save to csv file
cyto_stats_compute(gs,
  alias = c("CD4 T Cells", "CD8 T Cells"),
  parent = c("Live Cells", "T Cells"),
  stat = "freq",
  save_as = "Population-Frequencies"
)

#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> New names:
#> * count -> count...1
#> * count -> count...2
#> # A tibble: 132 x 6
#>    name             OVAConc Treatment Population  Parent     Frequency
#>    <fct>            <chr>   <chr>     <chr>       <chr>          <dbl>
#>  1 Activation_1.fcs 0       Stim-A    CD4 T Cells Live Cells      13.3
#>  2 Activation_1.fcs 0       Stim-A    CD8 T Cells Live Cells      16.1
#>  3 Activation_1.fcs 0       Stim-A    CD4 T Cells T Cells         41.2
#>  4 Activation_1.fcs 0       Stim-A    CD8 T Cells T Cells         49.8
#>  5 Activation_2.fcs 0       Stim-A    CD4 T Cells Live Cells      15.0
#>  6 Activation_2.fcs 0       Stim-A    CD8 T Cells Live Cells      18.3
#>  7 Activation_2.fcs 0       Stim-A    CD4 T Cells T Cells         41.2
#>  8 Activation_2.fcs 0       Stim-A    CD8 T Cells T Cells         50.2
#>  9 Activation_3.fcs 5       Stim-A    CD4 T Cells Live Cells      14.9
#> 10 Activation_3.fcs 5       Stim-A    CD8 T Cells Live Cells      18.1
#> # ... with 122 more rows