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:
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:
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:
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:
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)
}
These are the MHC-I haplotypes that have a trained model.
These are the MHC-II haplotypes that have a trained model.
mhcnuggetsr_report()
#> ***************
#> * mhcnuggetsr *
#> ***************
#> OS: unix
#> Python location: /usr/bin/python3
#> Is pip installed: TRUE
#> pip version: 20.0.2
#> Python NumPy available: TRUE
#> Python mhcnuggets available: TRUE
#> **************
#> * MHCnuggets *
#> **************
#> Is MHCnuggets installed: FALSE
#> Tip: when working on the Groninger Peregrine computer cluster,
#> type:
#>
#> ~/.local/share/r-miniconda/envs/r-reticulate/bin/python -m pip install mhcnuggets
#> ***************
#> * sessionInfo *
#> ***************
#> R version 3.6.3 (2020-02-29)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] testthat_2.3.2 mhcnuggetsr_1.1 knitr_1.30
#>
#> loaded via a namespace (and not attached):
#> [1] Rcpp_1.0.5 magrittr_1.5 rappdirs_0.3.1
#> [4] lattice_0.20-40 R6_2.4.1 rlang_0.4.8
#> [7] highr_0.8 stringr_1.4.0 tools_3.6.3
#> [10] grid_3.6.3 xfun_0.18 htmltools_0.5.0.9001
#> [13] ellipsis_0.3.1 yaml_2.2.1 digest_0.6.27
#> [16] tibble_3.0.4 lifecycle_0.2.0 crayon_1.3.4
#> [19] Matrix_1.2-18 vctrs_0.3.4 evaluate_0.14
#> [22] rmarkdown_2.4 stringi_1.5.3 compiler_3.6.3
#> [25] pillar_1.4.6 reticulate_1.16 jsonlite_1.7.1
#> [28] pkgconfig_2.0.3