Median polish estimates of e.g. protein abundances for entire data.frame
Source:R/tidyMS_aggregation.R
medpolish_estimate_dfconfig.RdMedian polish estimates of e.g. protein abundances for entire data.frame
Usage
medpolish_estimate_dfconfig(
pdata,
response,
hierarchy_keys,
hierarchy_keys_depth,
sample_name,
name = FALSE
)See also
Examples
bb <- sim_lfq_data_peptide_config(Nprot = 20)
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
conf <- bb$config
data <- bb$data
conf$hierarchy_depth <- 1
xnested <- data |>
dplyr::group_by(across(all_of(conf$hierarchy_keys_depth()))) |>
tidyr::nest()
feature <- base::setdiff(conf$hierarchy_keys(), conf$hierarchy_keys_depth())
x <- xnested$data[[1]]
bb <- medpolish_estimate_dfconfig(x, conf$get_response(),
conf$hierarchy_keys(), conf$hierarchy_keys_depth(), conf$sample_name)
prolfqua:::.reestablish_condition(x, bb, conf$sample_name,
conf$factor_keys(), conf$file_name, conf$isotope_label)
#> # A tibble: 12 × 5
#> sampleName group_ sample isotopeLabel medpolish
#> <chr> <chr> <chr> <chr> <dbl>
#> 1 A_V1 A A_V1 light 30.0
#> 2 A_V2 A A_V2 light 29.8
#> 3 A_V3 A A_V3 light 29.1
#> 4 A_V4 A A_V4 light 29.9
#> 5 B_V1 B B_V1 light 32.1
#> 6 B_V2 B B_V2 light 32.4
#> 7 B_V3 B B_V3 light 31.7
#> 8 B_V4 B B_V4 light 33.5
#> 9 Ctrl_V1 Ctrl Ctrl_V1 light 22.1
#> 10 Ctrl_V2 Ctrl Ctrl_V2 light 18.5
#> 11 Ctrl_V3 Ctrl Ctrl_V3 light 19.8
#> 12 Ctrl_V4 Ctrl Ctrl_V4 light 21.4