Estimate e.g. protein abundance from peptides using MASS:rlm

Estimate e.g. protein abundance from peptides using MASS:rlm

rlm_estimate(pdata, response, feature, samples, maxIt = 20)

Arguments

pdata

data

response

intensities

feature

e.g. peptideIDs.

sample

e.g. sampleName

See also

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

Examples

xx <- data.frame(response = rnorm(20,0,10), feature = rep(LETTERS[1:5],4), samples= rep(letters[1:4],5))

bb <- rlm_estimate(xx , "response", "feature", "samples", maxIt = 20)

xx2 <- data.frame(log2Area = rnorm(20,0,10), peptide_Id = rep(LETTERS[1:5],4), sampleName = rep(letters[1:4],5))
rlm_estimate(xx2, "log2Area", "peptide_Id", "sampleName")
#>   sampleName mean.log2Area    weights     lmrob
#> 1          a    -0.5323711 0.01276912 -1.674944
#> 2          b    -3.5622039 0.01458104 -3.383001
#> 3          c     2.9286124 0.02616560  2.928612
#> 4          d    -1.8694154 0.02666118 -1.869415
rlm_estimate(prolfqua_data('data_checksummarizationrobust87'),"log2Area", "peptide_Id", "sampleName")
#>   sampleName mean.log2Area weights      lmrob
#> 1          a    -7.1898238       1 -7.1898238
#> 2          b    -6.3420890       1 -6.3420890
#> 3          c            NA      NA         NA
#> 4          d     0.4191734       1  0.4191734
rlm_estimate(prolfqua_data('data_checksummarizerobust69'),"log2Area", "peptide_Id", "sampleName")
#>   sampleName mean.log2Area    weights     lmrob
#> 1          a     -6.307444 0.05027647 -6.307444
#> 2          b            NA         NA        NA
#> 3          c      2.489945 0.00329409  2.489945
#> 4          d     -9.076788 0.01213768 -9.076788
res <- vector(100,mode = "list")
for (i in seq_len(100)) {
  xx3 <- xx2
  xx3$log2Area[sample(1:20,sample(1:15,1))] <- NA
  res[[i]] <- list(data = xx3, summary = rlm_estimate(xx3, "log2Area", "peptide_Id", "sampleName"))
}
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
rlm_estimate(xx2[xx2$peptide_Id == 'A',],"log2Area", "peptide_Id", "sampleName")
#>    sampleName mean.log2Area       lmrob weights
#> 1           a    4.06124230  4.06124230       1
#> 6           b   -5.76691823 -5.76691823       1
#> 11          c   12.40584606 12.40584606       1
#> 16          d   -0.01408112 -0.01408112       1
rlm_estimate(xx2[xx2$sampleName == 'a',],"log2Area", "peptide_Id", "sampleName")
#> # A tibble: 1 × 4
#>   sampleName  lmrob mean.log2Area weights
#>   <chr>       <dbl>         <dbl>   <dbl>
#> 1 a          -0.532        -0.532       1


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$data[[1]]
bb <- rlm_estimate(x,
 response = conf$table$get_response(),
  feature = feature,
   samples = conf$table$sampleName)