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

Source: R/tidyMS_aggregation.R
rlm_estimate.Rd

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

Usage

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

Arguments

pdata

data

response

intensities

feature

e.g. peptideIDs.

samples

e.g. sample_name

See also

Other aggregation: INTERNAL_FUNCTIONS_BY_FAMILY, aggregate_intensity_top_n(), estimate_intensity(), medpolish_estimate(), medpolish_estimate_df(), medpolish_estimate_dfconfig(), 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),
  sample_name = rep(letters[1:4], 5))
rlm_estimate(xx2, "log2Area", "peptide_Id", "sample_name")
#>   sample_name mean.log2Area     weights      lmrob
#> 1           a     0.9156846 0.009114066 -0.1575930
#> 2           b     0.7375349 0.053831495  0.7375349
#> 3           c    -5.4504240 0.010381022 -6.1380233
#> 4           d     1.0085976 0.036831551  1.0085976
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$log2Area[sample(1:20, sample(1:15, 1))] <- NA
  res[[i]] <- list(data = xx3, summary = rlm_estimate(xx3, "log2Area", "peptide_Id", "sample_name"))
}
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
rlm_estimate(xx2[xx2$peptide_Id == "A", ], "log2Area", "peptide_Id", "sample_name")
#>    sample_name mean.log2Area     lmrob weights
#> 1            a     -8.956332 -8.956332       1
#> 6            b     -1.231429 -1.231429       1
#> 11           c      3.402596  3.402596       1
#> 16           d     10.894123 10.894123       1
rlm_estimate(xx2[xx2$sample_name == "a", ], "log2Area", "peptide_Id", "sample_name")
#> # A tibble: 1 × 4
#>   sample_name lmrob mean.log2Area weights
#>   <chr>       <dbl>         <dbl>   <dbl>
#> 1 a           0.916         0.916       1


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