Select Traits and Strains for Response

Select Traits and Strains for Response

Summary of traitSolos object

Summary of traitSolos object

Usage

traitSolos(
  traitData,
  traitSignal = partition(traitData),
  traitnames = trait_names(traitData),
  response = c("value", "normed", "cellmean", "signal"),
  strains = names(foundr::CCcolors),
  abbrev = FALSE,
  sep = ": "
)

summary_traitSolos(object, customSettings = NULL, ...)

# S3 method for class 'traitSolos'
summary(object, ...)

Arguments

traitData

data frame

traitSignal

data frame

traitnames

names of `dataset: trait` combinations to subset

response

name of response to return

strains

names of strains to subset

abbrev

abbreviate names if `TRUE`

object

object of class `traitSolos`

customSettings

list of custom settings (including "condition_name" and "dataset")

...

additional parameters

Value

data frame

summary table

summary table

See also

ggplot_traitSolos

Examples

out <- traitSolos(sampleData)
summary(out)
#> # A tibble: 16 × 12
#>    dataset sex   condition trait      AJ      B6   `129`     NOD     NZO    CAST
#>    <fct>   <chr> <chr>     <fct>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#>  1 sample  F     Y         C      0.752   0.599  -0.487   0.342   0.258   0.316 
#>  2 sample  F     X         C      1.67   -0.0951 -0.307   0.432  -0.812   1.58  
#>  3 sample  M     X         C      0.625  -1.62   -0.648  -0.681  -0.114   1.25  
#>  4 sample  M     Y         C      0.508  -1.00   -0.626  -0.380  -0.254  -0.0314
#>  5 sample  F     Y         A      0.0706 -0.708   0.0666 -0.589  -0.217   0.334 
#>  6 sample  F     X         A     -0.115  -0.459   0.256  -0.351   0.136   0.114 
#>  7 sample  M     X         A     -0.420   0.733   0.258   0.167  -1.34    0.494 
#>  8 sample  M     Y         A      0.792  -0.365   0.264  -0.510  -0.225   0.354 
#>  9 sample  F     Y         D     -0.798  -1.04   -0.0708 -1.09    0.130   0.826 
#> 10 sample  F     X         D     -0.763  -1.48    0.0542 -0.723   0.225   0.174 
#> 11 sample  M     X         D      0.161  -0.356   0.673  -0.888   0.105   0.416 
#> 12 sample  M     Y         D      0.0674 -0.569   0.324   0.0306 -0.527   0.542 
#> 13 sample  F     Y         B      1.03    1.35    0.930  -0.216   0.0136 -0.604 
#> 14 sample  F     X         B      1.22    1.49    0.738   0.384  -0.0764 -0.956 
#> 15 sample  M     X         B      0.712   0.686   0.552  -0.0174 -1.42   -1.65  
#> 16 sample  M     Y         B      0.434   0.382   0.576  -0.712  -0.714  -0.912 
#> # ℹ 2 more variables: PWK <dbl>, WSB <dbl>
plot(out)