Median polish estimates of e.g. protein abundances for entire data.frame

Median polish estimates of e.g. protein abundances for entire data.frame

medpolish_estimate_df(pdata, response, feature, sampleName)

Arguments

pdata

data.frame

response

column name with intensities

feature

column name e.g. peptide ids

samples

column name e.g. sampleName

Value

data.frame

See also

medpolish_estimate

Other aggregation: INTERNAL_FUNCTIONS_BY_FAMILY, aggregate_intensity_topN(), estimate_intensity(), intensity_summary_by_hkeys(), medpolish_estimate(), medpolish_estimate_dfconfig(), medpolish_protein_estimates(), plot_estimate(), plot_hierarchies_add_quantline(), plot_hierarchies_line(), plot_hierarchies_line_df(), rlm_estimate(), rlm_estimate_dfconfig()

Other plotting: ContrastsPlotter, INTERNAL_FUNCTIONS_BY_FAMILY, UpSet_interaction_missing_stats(), UpSet_missing_stats(), missigness_histogram(), missingness_per_condition(), missingness_per_condition_cumsum(), plot_NA_heatmap(), plot_estimate(), plot_heatmap(), plot_heatmap_cor(), plot_hierarchies_add_quantline(), plot_hierarchies_boxplot_df(), plot_hierarchies_line(), plot_hierarchies_line_df(), plot_intensity_distribution_violin(), plot_pca(), plot_raster(), plot_sample_correlation(), plot_screeplot()

Examples

bb <- prolfqua_data('data_ionstar')$filtered()
#> Column added : nr_peptide_Id_IN_protein_Id
bb$config <- old2new(bb$config)
stopifnot(nrow(bb$data) == 25780)
conf <- bb$config
data <- bb$data

conf$table$hierarchyDepth = 1
xnested <- data |>
  dplyr::group_by_at(conf$table$hierarchy_keys_depth()) |> tidyr::nest()

feature <- base::setdiff(conf$table$hierarchy_keys(),
  conf$table$hierarchy_keys_depth())
x <- xnested$data[[1]]
bb <- medpolish_estimate_df(x,
 response = conf$table$get_response(),
  feature = feature,
   sampleName = conf$table$sampleName)
prolfqua:::.reestablish_condition(x,bb, conf)
#> # A tibble: 20 × 6
#>    sampleName dilution. run_Id raw.file                        isotope medpolish
#>    <chr>      <chr>     <chr>  <chr>                           <chr>       <dbl>
#>  1 b~02       b         02     b03_02_150304_human_ecoli_b_3u… light   53281919.
#>  2 c~03       c         03     b03_03_150304_human_ecoli_c_3u… light   53108622.
#>  3 d~04       d         04     b03_04_150304_human_ecoli_d_3u… light   51768575 
#>  4 e~05       e         05     b03_05_150304_human_ecoli_e_3u… light   48923212.
#>  5 e~06       e         06     b03_06_150304_human_ecoli_e_3u… light   46757962.
#>  6 d~07       d         07     b03_07_150304_human_ecoli_d_3u… light   43139562.
#>  7 c~08       c         08     b03_08_150304_human_ecoli_c_3u… light   49301991.
#>  8 b~09       b         09     b03_09_150304_human_ecoli_b_3u… light   46173388.
#>  9 a~10       a         10     b03_10_150304_human_ecoli_a_3u… light   47789438.
#> 10 a~11       a         11     b03_11_150304_human_ecoli_a_3u… light   61505569.
#> 11 b~12       b         12     b03_12_150304_human_ecoli_b_3u… light   57534412.
#> 12 c~13       c         13     b03_13_150304_human_ecoli_c_3u… light   50100838.
#> 13 d~14       d         14     b03_14_150304_human_ecoli_d_3u… light   58970725 
#> 14 e~15       e         15     b03_15_150304_human_ecoli_e_3u… light   61280338.
#> 15 e~16       e         16     b03_16_150304_human_ecoli_e_3u… light   56780612.
#> 16 d~17       d         17     b03_17_150304_human_ecoli_d_3u… light   59887225 
#> 17 c~18       c         18     b03_18_150304_human_ecoli_c_3u… light   57788409.
#> 18 b~19       b         19     b03_19_150304_human_ecoli_b_3u… light   60040838.
#> 19 a~20       a         20     b03_20_150304_human_ecoli_a_3u… light   57032150 
#> 20 a~21       a         21     b03_21_150304_human_ecoli_a_3u… light   55993412.