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demo

demo

library(knitr)
library(mhcnuggetsr)
library(testthat)

For this vignette, we use the same example as the MHCnuggets Python notebooks.

Get the path to the testing peptides, and show them:

if (is_mhcnuggets_installed()) {
  peptides_path <- get_example_filename("test_peptides.peps")
  expect_true(file.exists(peptides_path))
  readLines(peptides_path, warn = FALSE)
}

Pick an MHC-I haplotype:

if (is_mhcnuggets_installed()) {
  mhc_1_haplotype <- "HLA-A02:01"
  expect_true(mhc_1_haplotype %in% get_trained_mhc_1_haplotypes())
}

Predict:

#{r mhc1_predict_ic50_from_file_no_ba_models}
if (is_mhcnuggets_installed()) {
  mhcnuggets_options <- create_mhcnuggets_options(
    mhc = mhc_1_haplotype
  )
  df <- predict_ic50_from_file(
    peptides_path = peptides_path,
    mhcnuggets_options = mhcnuggets_options
  )
  kable(df)
}

Predict:

#{r mhc1_predict_ic50_from_file_ba_models}
if (is_mhcnuggets_installed()) {
  mhcnuggets_options <- create_mhcnuggets_options(
    mhc = mhc_1_haplotype,
    ba_models = TRUE
  )
  df <- predict_ic50_from_file(
    peptides_path = peptides_path,
    mhcnuggets_options = mhcnuggets_options
  )
  kable(df)
}

Use MCH-II haplotype:

if (is_mhcnuggets_installed()) {
  mhc_2_haplotype <- "HLA-DRB101:01"
  expect_true(mhc_2_haplotype %in% get_trained_mhc_2_haplotypes())
}

Predict:

#{r mhc2_predict_ic50_from_file_no_ba_models}
if (is_mhcnuggets_installed()) {
  mhcnuggets_options <- create_mhcnuggets_options(
    mhc = mhc_2_haplotype
  )
  df <- predict_ic50_from_file(
    peptides_path = peptides_path,
    mhcnuggets_options = mhcnuggets_options
  )
  kable(df)
}

Use another MHC-I haplotype. In this case, MHCnuggets has not been trained upon it, but it is a valid supertype:

if (is_mhcnuggets_installed()) {
  mhc_1_haplotype <- "HLA-A02:60"
  expect_false(mhc_1_haplotype %in% get_trained_mhc_1_haplotypes())
}

Predict:

#{r predict_mhc_1_haplotype_supertype}
if (is_mhcnuggets_installed()) {
  mhcnuggets_options <- create_mhcnuggets_options(
    mhc_class = "I",
    mhc = mhc_1_haplotype
  )
  df <- predict_ic50_from_file(
    peptides_path = peptides_path,
    mhcnuggets_options = mhcnuggets_options
  )
  kable(df)
}

Appendix

All example files

if (is_mhcnuggets_installed()) {
  basename(get_example_filenames())
}

All MHC-I haplotypes

These are the MHC-I haplotypes that have a trained model.

if (is_mhcnuggets_installed()) {
  cat(get_trained_mhc_1_haplotypes())
}

All MHC-II haplotypes

These are the MHC-II haplotypes that have a trained model.

if (is_mhcnuggets_installed()) {
  cat(get_trained_mhc_2_haplotypes())
}

Session info

mhcnuggetsr_report()
#> ***************
#> * mhcnuggetsr *
#> ***************
#> OS: unix
#> **************
#> * MHCnuggets *
#> **************
#> Is MHCnuggets installed: FALSE
#> ****************
#> * session info *
#> ****************
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.3.3 (2024-02-29)
#>  os       Ubuntu 24.04.4 LTS
#>  system   x86_64, linux-gnu
#>  ui       X11
#>  language (EN)
#>  collate  C
#>  ctype    en_US.UTF-8
#>  tz       Europe/Stockholm
#>  date     2026-07-04
#>  pandoc   3.1.3 @ /usr/bin/ (via rmarkdown)
#>  quarto   1.3.450 @ /usr/local/bin/quarto
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package     * version date (UTC) lib source
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#>  * ── Packages attached to the search path.
#> 
#> ──────────────────────────────────────────────────────────────────────────────

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