Introduction to amRml

Source: vignettes/intro.Rmd

Overview

amRml predicts antimicrobial resistance (AMR) from bacterial genomic features. It consumes a DuckDB produced by amRdata and produces ML matrices, tuned logistic regression models, per-genome predictions, feature importances, and Fisher’s exact tests as a non-ML baseline.

This vignette uses the Shigella flexneri (Sfl) DuckDB bundled in inst/extdata.

fixture <- system.file("extdata", "Sfl_parquet.duckdb", package = "amRml")
out_dir <- file.path(tempdir(), "amRml_vignette")
dir.create(out_dir, showWarnings = FALSE, recursive = TRUE)

Generating ML input matrices

generateMLInputs() reads the bug-level DuckDB (metadata + feature parquets) and writes one long-format sparse parquet per drug × feature × encoding combination into out_path/matrix/. With stratify_by, it additionally writes year- or country-stratified matrices into matrix_year/ or matrix_country/.

generateMLInputs(
  parquet_duckdb_path = fixture,
  out_path            = out_dir,
  n_fold              = 5,
  split               = c(1, 0),
  min_n               = 25,
  verbosity           = "minimal"
)
#> Selected mode: CV (n_fold = 5), train = 1, val = 0, test = 0
#> Matrix output directory: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year
#> Connected to DuckDB for bug: Sfl
#> Building ML matrices for drug_year: FOX_2015-2019
#> Building ML matrices for drug_year: TET_2015-2019
#> Building ML matrices for drug_year: CRO_2015-2019
#> Building ML matrices for drug_year: FEP_2015-2019
#> Building ML matrices for drug_year: TMP_2015-2019
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_TMP_2015-2019_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_TMP_2015-2019_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_TMP_2015-2019_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_TMP_2015-2019_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_TMP_2015-2019_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_TMP_2015-2019_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_TMP_2015-2019_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_TMP_2015-2019_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_TMP_2015-2019_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_TMP_2015-2019_struct_binary_sparse.parquet
#> Building ML matrices for drug_year: GEN_2015-2019
#> Building ML matrices for drug_year: AMP_2015-2019
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_AMP_2015-2019_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_AMP_2015-2019_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_AMP_2015-2019_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_AMP_2015-2019_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_AMP_2015-2019_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_AMP_2015-2019_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_AMP_2015-2019_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_AMP_2015-2019_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_AMP_2015-2019_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_AMP_2015-2019_struct_binary_sparse.parquet
#> Building ML matrices for drug_year: AMX-CLA_2015-2019
#> Building ML matrices for drug_year: CAZ_2015-2019
#> Building ML matrices for drug_year: SMX_2015-2019
#> Building ML matrices for drug_year: CRO_2010-2014
#> Building ML matrices for drug_year: NAL_2015-2019
#> Building ML matrices for drug_year: CIP_2015-2019
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_CIP_2015-2019_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_CIP_2015-2019_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_CIP_2015-2019_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_CIP_2015-2019_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_CIP_2015-2019_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_CIP_2015-2019_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_CIP_2015-2019_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_CIP_2015-2019_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_CIP_2015-2019_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_CIP_2015-2019_struct_binary_sparse.parquet
#> Building ML matrices for drug_year: CTX_2015-2019
#> Building ML matrices for drug_year: CHL_2015-2019
#> Building ML matrices for drug_year: MEM_2015-2019
#> Building ML matrices for drug_year: AZM_2015-2019
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_AZM_2015-2019_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_AZM_2015-2019_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_AZM_2015-2019_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_AZM_2015-2019_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_AZM_2015-2019_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_AZM_2015-2019_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_AZM_2015-2019_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_AZM_2015-2019_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_AZM_2015-2019_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_year_AZM_2015-2019_struct_binary_sparse.parquet
#> Building ML matrices for drug_year: CST_2015-2019
#> Building ML matrices for drug_class_year: PEN_2015-2019
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_PEN_2015-2019_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_PEN_2015-2019_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_PEN_2015-2019_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_PEN_2015-2019_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_PEN_2015-2019_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_PEN_2015-2019_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_PEN_2015-2019_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_PEN_2015-2019_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_PEN_2015-2019_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_PEN_2015-2019_struct_binary_sparse.parquet
#> Building ML matrices for drug_class_year: TMD_2015-2019
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_TMD_2015-2019_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_TMD_2015-2019_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_TMD_2015-2019_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_TMD_2015-2019_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_TMD_2015-2019_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_TMD_2015-2019_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_TMD_2015-2019_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_TMD_2015-2019_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_TMD_2015-2019_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_TMD_2015-2019_struct_binary_sparse.parquet
#> Building ML matrices for drug_class_year: SUL_2015-2019
#> Building ML matrices for drug_class_year: AMG_2015-2019
#> Building ML matrices for drug_class_year: CEP_2015-2019
#> Building ML matrices for drug_class_year: AMF_2015-2019
#> Building ML matrices for drug_class_year: POL_2015-2019
#> Building ML matrices for drug_class_year: FLQ_2015-2019
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_FLQ_2015-2019_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_FLQ_2015-2019_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_FLQ_2015-2019_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_FLQ_2015-2019_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_FLQ_2015-2019_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_FLQ_2015-2019_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_FLQ_2015-2019_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_FLQ_2015-2019_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_FLQ_2015-2019_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_FLQ_2015-2019_struct_binary_sparse.parquet
#> Building ML matrices for drug_class_year: PEN-BLI_2015-2019
#> Building ML matrices for drug_class_year: TET_2015-2019
#> Building ML matrices for drug_class_year: CAR_2015-2019
#> Building ML matrices for drug_class_year: QUI_2015-2019
#> Building ML matrices for drug_class_year: MAC_2015-2019
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_MAC_2015-2019_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_MAC_2015-2019_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_MAC_2015-2019_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_MAC_2015-2019_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_MAC_2015-2019_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_MAC_2015-2019_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_MAC_2015-2019_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_MAC_2015-2019_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_MAC_2015-2019_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_year/Sfl_drug_class_year_MAC_2015-2019_struct_binary_sparse.parquet
#> Building ML matrices for drug_class_year: CEP_2010-2014
#> All LOO matrices generated and saved.
#> Matrix output directory: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country
#> Connected to DuckDB for bug: Sfl
#> Building ML matrices for drug_country: NAL_PRT
#> Building ML matrices for drug_country: CIP_PRT
#> Building ML matrices for drug_country: CTX_PRT
#> Building ML matrices for drug_country: CHL_PRT
#> Building ML matrices for drug_country: MEM_PRT
#> Building ML matrices for drug_country: CRO_AUS
#> Building ML matrices for drug_country: CRO_THA
#> Building ML matrices for drug_country: FEP_PRT
#> Building ML matrices for drug_country: TMP_PRT
#> Building ML matrices for drug_country: GEN_PRT
#> Building ML matrices for drug_country: AMP_AUS
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_AMP_AUS_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_AMP_AUS_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_AMP_AUS_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_AMP_AUS_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_AMP_AUS_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_AMP_AUS_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_AMP_AUS_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_AMP_AUS_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_AMP_AUS_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_AMP_AUS_struct_binary_sparse.parquet
#> Building ML matrices for drug_country: AMP_PRT
#> Building ML matrices for drug_country: AMX-CLA_PRT
#> Building ML matrices for drug_country: CAZ_PRT
#> Building ML matrices for drug_country: SMX_PRT
#> Building ML matrices for drug_country: TMP_AUS
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_TMP_AUS_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_TMP_AUS_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_TMP_AUS_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_TMP_AUS_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_TMP_AUS_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_TMP_AUS_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_TMP_AUS_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_TMP_AUS_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_TMP_AUS_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_TMP_AUS_struct_binary_sparse.parquet
#> Building ML matrices for drug_country: GEN_AUS
#> Building ML matrices for drug_country: FOX_PRT
#> Building ML matrices for drug_country: TET_PRT
#> Building ML matrices for drug_country: CIP_AUS
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_CIP_AUS_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_CIP_AUS_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_CIP_AUS_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_CIP_AUS_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_CIP_AUS_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_CIP_AUS_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_CIP_AUS_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_CIP_AUS_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_CIP_AUS_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_CIP_AUS_struct_binary_sparse.parquet
#> Building ML matrices for drug_country: MEM_AUS
#> Building ML matrices for drug_country: AZM_AUS
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_AZM_AUS_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_AZM_AUS_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_AZM_AUS_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_AZM_AUS_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_AZM_AUS_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_AZM_AUS_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_AZM_AUS_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_AZM_AUS_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_AZM_AUS_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_country_AZM_AUS_struct_binary_sparse.parquet
#> Building ML matrices for drug_country: CST_THA
#> Building ML matrices for drug_class_country: QUI_PRT
#> Building ML matrices for drug_class_country: FLQ_AUS
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_FLQ_AUS_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_FLQ_AUS_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_FLQ_AUS_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_FLQ_AUS_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_FLQ_AUS_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_FLQ_AUS_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_FLQ_AUS_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_FLQ_AUS_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_FLQ_AUS_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_FLQ_AUS_struct_binary_sparse.parquet
#> Building ML matrices for drug_class_country: CAR_AUS
#> Building ML matrices for drug_class_country: PEN_PRT
#> Building ML matrices for drug_class_country: TMD_PRT
#> Building ML matrices for drug_class_country: SUL_PRT
#> Building ML matrices for drug_class_country: AMG_PRT
#> Building ML matrices for drug_class_country: CEP_AUS
#> Building ML matrices for drug_class_country: POL_THA
#> Building ML matrices for drug_class_country: CEP_THA
#> Building ML matrices for drug_class_country: CEP_PRT
#> Building ML matrices for drug_class_country: AMF_PRT
#> Building ML matrices for drug_class_country: PEN_AUS
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_PEN_AUS_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_PEN_AUS_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_PEN_AUS_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_PEN_AUS_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_PEN_AUS_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_PEN_AUS_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_PEN_AUS_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_PEN_AUS_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_PEN_AUS_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_PEN_AUS_struct_binary_sparse.parquet
#> Building ML matrices for drug_class_country: TMD_AUS
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_TMD_AUS_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_TMD_AUS_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_TMD_AUS_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_TMD_AUS_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_TMD_AUS_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_TMD_AUS_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_TMD_AUS_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_TMD_AUS_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_TMD_AUS_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_TMD_AUS_struct_binary_sparse.parquet
#> Building ML matrices for drug_class_country: AMG_AUS
#> Building ML matrices for drug_class_country: FLQ_PRT
#> Building ML matrices for drug_class_country: PEN-BLI_PRT
#> Building ML matrices for drug_class_country: TET_PRT
#> Building ML matrices for drug_class_country: CAR_PRT
#> Building ML matrices for drug_class_country: MAC_AUS
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_MAC_AUS_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_MAC_AUS_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_MAC_AUS_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_MAC_AUS_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_MAC_AUS_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_MAC_AUS_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_MAC_AUS_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_MAC_AUS_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_MAC_AUS_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix_country/Sfl_drug_class_country_MAC_AUS_struct_binary_sparse.parquet
#> All LOO matrices generated and saved.
#> Matrix output directory: /tmp/RtmpaDp8kK/amRml_vignette/matrix
#> Connected to DuckDB for bug: Sfl
#> Building ML matrices for drug: NAL
#> Building ML matrices for drug: CIP
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_CIP_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_CIP_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_CIP_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_CIP_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_CIP_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_CIP_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_CIP_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_CIP_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_CIP_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_CIP_struct_binary_sparse.parquet
#> Building ML matrices for drug: GEN
#> Building ML matrices for drug: CRO
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_CRO_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_CRO_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_CRO_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_CRO_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_CRO_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_CRO_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_CRO_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_CRO_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_CRO_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_CRO_struct_binary_sparse.parquet
#> Building ML matrices for drug: AMP
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_AMP_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_AMP_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_AMP_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_AMP_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_AMP_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_AMP_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_AMP_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_AMP_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_AMP_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_AMP_struct_binary_sparse.parquet
#> Building ML matrices for drug: AMX-CLA
#> Building ML matrices for drug: TMP
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_TMP_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_TMP_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_TMP_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_TMP_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_TMP_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_TMP_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_TMP_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_TMP_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_TMP_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_TMP_struct_binary_sparse.parquet
#> Building ML matrices for drug: SXZ
#> Building ML matrices for drug: AZM
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_AZM_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_AZM_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_AZM_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_AZM_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_AZM_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_AZM_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_AZM_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_AZM_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_AZM_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_AZM_struct_binary_sparse.parquet
#> Building ML matrices for drug: CAZ
#> Building ML matrices for drug: TET
#> Building ML matrices for drug: CHL
#> Building ML matrices for drug: TMP-SMX
#> Building ML matrices for drug: CST
#> Building ML matrices for drug: CTX
#> Building ML matrices for drug: FOX
#> Building ML matrices for drug: FEP
#> Building ML matrices for drug: SMX
#> Building ML matrices for drug: MEM
#> Building ML matrices for drug_class: SUL
#> Building ML matrices for drug_class: TMD-SUL
#> Building ML matrices for drug_class: MAC
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_MAC_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_MAC_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_MAC_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_MAC_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_MAC_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_MAC_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_MAC_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_MAC_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_MAC_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_MAC_struct_binary_sparse.parquet
#> Building ML matrices for drug_class: POL
#> Building ML matrices for drug_class: AMF
#> Building ML matrices for drug_class: FLQ
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_FLQ_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_FLQ_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_FLQ_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_FLQ_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_FLQ_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_FLQ_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_FLQ_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_FLQ_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_FLQ_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_FLQ_struct_binary_sparse.parquet
#> Building ML matrices for drug_class: PEN
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_PEN_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_PEN_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_PEN_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_PEN_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_PEN_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_PEN_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_PEN_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_PEN_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_PEN_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_PEN_struct_binary_sparse.parquet
#> Building ML matrices for drug_class: PEN-BLI
#> Building ML matrices for drug_class: CEP
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_CEP_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_CEP_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_CEP_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_CEP_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_CEP_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_CEP_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_CEP_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_CEP_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_CEP_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_CEP_struct_binary_sparse.parquet
#> Building ML matrices for drug_class: TET
#> Building ML matrices for drug_class: TMD
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_TMD_genes_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_TMD_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_TMD_genes_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_TMD_proteins_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_TMD_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_TMD_proteins_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_TMD_domains_counts_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_TMD_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_TMD_domains_binary_sparse.parquet
#> Exported matrix: /tmp/RtmpaDp8kK/amRml_vignette/matrix/Sfl_drug_class_TMD_struct_binary_sparse.parquet
#> Building ML matrices for drug_class: AMG
#> Building ML matrices for drug_class: CAR
#> Building ML matrices for drug_class: QUI
#> Building MDR matrices for classes: PEN, PEN_TMD, TMD_MAC_PEN, TMD_PEN, TMD_PEN_MAC
#> Exported MDR matrix: /tmp/RtmpaDp8kK/amRml_vignette/MDR_matrix/Sfl_MDR_resistant_classes_genes_counts_sparse.parquet
#> Exported MDR matrix: /tmp/RtmpaDp8kK/amRml_vignette/MDR_matrix/Sfl_MDR_resistant_classes_genes_binary_sparse.parquet
#> Exported MDR matrix: /tmp/RtmpaDp8kK/amRml_vignette/MDR_matrix/Sfl_MDR_resistant_classes_genes_binary_sparse.parquet
#> Exported MDR matrix: /tmp/RtmpaDp8kK/amRml_vignette/MDR_matrix/Sfl_MDR_resistant_classes_proteins_counts_sparse.parquet
#> Exported MDR matrix: /tmp/RtmpaDp8kK/amRml_vignette/MDR_matrix/Sfl_MDR_resistant_classes_proteins_binary_sparse.parquet
#> Exported MDR matrix: /tmp/RtmpaDp8kK/amRml_vignette/MDR_matrix/Sfl_MDR_resistant_classes_proteins_binary_sparse.parquet
#> Exported MDR matrix: /tmp/RtmpaDp8kK/amRml_vignette/MDR_matrix/Sfl_MDR_resistant_classes_domains_counts_sparse.parquet
#> Exported MDR matrix: /tmp/RtmpaDp8kK/amRml_vignette/MDR_matrix/Sfl_MDR_resistant_classes_domains_binary_sparse.parquet
#> Exported MDR matrix: /tmp/RtmpaDp8kK/amRml_vignette/MDR_matrix/Sfl_MDR_resistant_classes_domains_binary_sparse.parquet
#> Exported MDR matrix: /tmp/RtmpaDp8kK/amRml_vignette/MDR_matrix/Sfl_MDR_resistant_classes_struct_binary_sparse.parquet
#> The MDR matrix has been generated and saved.
#> All matrices generated and saved.

list.files(file.path(out_dir, "matrix"))[1:5]
#> [1] "Sfl_drug_AMP_domains_binary_sparse.parquet" 
#> [2] "Sfl_drug_AMP_domains_counts_sparse.parquet" 
#> [3] "Sfl_drug_AMP_genes_binary_sparse.parquet"   
#> [4] "Sfl_drug_AMP_genes_counts_sparse.parquet"   
#> [5] "Sfl_drug_AMP_proteins_binary_sparse.parquet"

For a classical train/validation/test split instead of cross-validation, set n_fold = NULL and pass a length-2 split of c(train_prop, val_prop); the test proportion is the remainder.

generateMLInputs(
  parquet_duckdb_path = fixture,
  out_path            = out_dir,
  n_fold              = NULL,
  split               = c(0.7, 0.15),
  verbosity           = "debug"
)

Loading a single matrix

The generated parquets are long-format sparse tibbles with these columns:

Column Description
genome_id Unique identifier for each isolate
feature_id Feature name (gene, protein, domain, or struct)
value Binary presence/absence (0/1) or count
genome_drug.resistant_phenotype "Resistant" or "Susceptible"

loadMLInputTibble() converts one of them to wide format (one row per genome, one column per feature) ready for ML.

matrix_path <- file.path(
  out_dir, "matrix", "Sfl_drug_AMP_genes_binary_sparse.parquet"
)

ml_tibble <- loadMLInputTibble(matrix_path)
n_features <- getNumFeat(ml_tibble)
target_var <- .getTargetVarName(ml_tibble)

c(n_features = n_features, target_var = target_var)
#> $n_features
#> [1] 6946
#> 
#> $target_var
#> genome_drug.resistant_phenotype

Per-matrix pipeline

runMLPipeline() runs the train/tune/fit/predict pipeline on a single matrix in memory. Use it to iterate on one drug-feature combo before scaling to all of them.

results <- runMLPipeline(
  ml_input_tibble    = ml_tibble,
  model              = "LR",
  split              = c(1, 0),
  n_fold             = 2,
  n_top_feats        = 20,
  penalty_vec        = 10^c(-3, -1),
  mix_vec            = c(0, 0.5, 1),
  select_best_metric = "mcc",
  return_fit         = TRUE,
  return_pred        = TRUE,
  verbose            = FALSE
)
#> Warning in .calculateLog2APOP(test_data_plus_predictions): Classes are
#> imbalanced toward the resistant phenotype. Calculation of log2(AUPRC/prior) may
#> be inappropriate.

results$performance_tibble
#> # A tibble: 1 × 18
#>   num_obs res_prop n_feat model train_prop val_prop lower_prop_vi_top_feats
#>     <int>    <dbl>  <int> <chr>      <dbl>    <dbl>                   <dbl>
#> 1     331     0.89   6946 LR             1        0                       0
#> # ℹ 11 more variables: upper_prop_vi_top_feats <dbl>, n_feats_returned <int>,
#> #   n_fold <dbl>, fit_penalty <dbl>, fit_mixture <dbl>, nmcc <dbl>,
#> #   log2_apop <dbl>, f1 <dbl>, bal_acc <dbl>, run_time_sec <dbl>, date <chr>
head(results$top_feat_tibble)
#> # A tibble: 6 × 3
#>   Variable   Importance Sign 
#>   <chr>           <dbl> <chr>
#> 1 group_8463      1.16  NEG  
#> 2 group_8464      0.599 NEG  
#> 3 group_6726      0.282 NEG  
#> 4 group_8320      0.251 NEG  
#> 5 group_7233      0.233 NEG  
#> 6 group_8372      0.231 NEG

Output structure

runMLPipeline() returns a named list:

performance_tibble — one row of model performance metrics:

Column Description
num_obs Number of observations
res_prop Proportion of resistant samples
n_feat Number of features
model Model type ("LR")
train_prop, val_prop Train/validation split proportions
fit_penalty, fit_mixture Fitted hyperparameters
nmcc, f1, bal_acc, log2_apop Performance metrics
run_time_sec Runtime in seconds

top_feat_tibble — ranked feature importance:

Column Description
Variable Feature name
Importance Variable importance score
Sign Direction of effect (POS = associated with resistance, NEG = with susceptibility)

Optional outputs (when return_* = TRUE):

  • tune_res — tuning results from grid search
  • fit — the fitted workflow object
  • pred — predictions with .pred_class, .pred_Resistant, .pred_Susceptible

Step-by-step model building

The builders below are what runMLPipeline() chains internally. Call them directly when you need control over any individual step.

data_split <- splitMLInputTibble(ml_tibble, split = c(0.6, 0.2), seed = 123)
train_data <- rsample::training(data_split)
test_data <- rsample::testing(data_split)
recipe <- buildRecipe(train_data, use_pca = FALSE)
lr_mod <- buildLRModel(multi_class = FALSE)
wflow <- buildWflow(lr_mod, recipe)

grid <- buildTuningGrid(
  model       = "LR",
  penalty_vec = 10^c(-3, -1),
  mix_vec     = c(0, 0.5, 1)
)
tune_res <- tuneGrid(wflow, data_split, grid, n_fold = 2)
best_wflow <- selectBestModel(tune_res, wflow, select_best_metric = "mcc")
fit <- fitBestModel(best_wflow, train_data)

preds <- predictML(fit, test_data)

Performance metrics

calculateEvalMets() returns all of nMCC, F1, balanced accuracy, AUPRC, log2(AUPRC/prior), sensitivity, and specificity from a tibble of predictions + truth.

calculateEvalMets(preds)
#> Warning in .calculateLog2APOP(test_data_plus_predictions): Classes are
#> imbalanced toward the resistant phenotype. Calculation of log2(AUPRC/prior) may
#> be inappropriate.
#> [1] 0.99 1.00 0.99 0.96 0.16
getConfusionMatrix(preds)
#>              Truth
#> Prediction    Resistant Susceptible
#>   Resistant          59           0
#>   Susceptible         1           7

Feature importance

extractTopFeats() ranks features by absolute coefficient (for LR). Use n_top_feats for a fixed count or prop_vi_top_feats for a percentile range.

top_features <- extractTopFeats(fit, n_top_feats = 20)
head(top_features)
#> # A tibble: 6 × 3
#>   Variable   Importance Sign 
#>   <chr>           <dbl> <chr>
#> 1 group_8463      1.06  NEG  
#> 2 group_8464      0.553 NEG  
#> 3 group_6726      0.283 NEG  
#> 4 group_8320      0.269 NEG  
#> 5 group_8372      0.248 NEG  
#> 6 group_7233      0.240 NEG

Visualization 

plotPRC(results$pred)

plotTopFeatsVI(results$fit, n_top_feats = 10)

For a baseline comparison against random labels, fit a shuffled-label pipeline and compare:

shuffled <- runMLPipeline(
  ml_input_tibble = ml_tibble,
  model           = "LR",
  split           = c(1, 0),
  n_fold          = 2,
  shuffle_labels  = TRUE,
  return_pred     = TRUE
)

plotBaselineComparison(
  non_shuffled_label_results = results$performance_tibble,
  shuffled_label_results     = shuffled$performance_tibble
)

Iterative feature elimination

runIFE() retrains the model after iteratively removing top-ranked features, helping identify the minimal predictive subset. It runs the pipeline once per percentile in percent_removal_vec

ife_results <- runIFE(
  ml_tibble,
  by_num              = TRUE,
  by_vi               = FALSE,
  percent_removal_vec = 10 * 1:9,
  mix_vec             = 0,
  return_feats        = TRUE,
  verbose             = FALSE
)

ife_results$ife_performance_tibble
ife_results$feats_removed

removeTopFeats() strips a given set of features from a matrix tibble if you want to do this manually:

trimmed <- removeTopFeats(ml_tibble, head(top_features, 5))
ncol(ml_tibble) - ncol(trimmed)
#> [1] 5

Fisher’s exact tests (non-ML baseline)

runFishers() runs a Fisher’s exact test of feature presence vs. phenotype for each feature, applies Benjamini–Hochberg correction, and computes per-class frequencies.

fisher_results <- runFishers(
  matrix_path       = matrix_path,
  Q                 = 0.05,
  alternative       = "two.sided",
  susceptible_label = "Susceptible",
  resistant_label   = "Resistant"
)

head(fisher_results)
#> # A tibble: 6 × 8
#>   gene        p_value adj_p_value sig_after_bh alternative     Q
#>   <chr>         <dbl>       <dbl> <lgl>        <chr>       <dbl>
#> 1 group_8463 2.14e-32    1.49e-28 TRUE         two.sided    0.05
#> 2 group_8464 1.17e-26    4.06e-23 TRUE         two.sided    0.05
#> 3 group_6726 7.70e-23    1.78e-19 TRUE         two.sided    0.05
#> 4 group_6877 4.51e-21    7.84e-18 TRUE         two.sided    0.05
#> 5 group_7233 1.02e-20    1.42e-17 TRUE         two.sided    0.05
#> 6 group_6847 3.56e-20    3.67e-17 TRUE         two.sided    0.05
#> # ℹ 2 more variables: freq_susceptible_gene_pres <dbl>,
#> #   freq_resistant_gene_pres <dbl>
plotFishers(fisher_results, alpha = 0.05, label_top_n = 5)

Training all models with runMLmodels

runMLmodels() trains a model on every matrix produced by generateMLInputs() and writes performance TSVs into out_path/ML_performance/, predictions into ML_pred/, and top features into ML_top_features/. Takes over an hour on Sfl with default settings.

runMLmodels(
  path            = out_dir,
  stratify_by     = NULL,
  LOO             = FALSE,
  cross_test      = FALSE,
  threads         = max(1L, parallel::detectCores() - 1L),
  split           = c(1, 0),
  n_fold          = 5,
  verbose         = TRUE,
  return_pred     = TRUE,
  use_saved_split = TRUE
)

End-to-end: runModelingPipeline

For the full pipeline from a DuckDB to all outputs in one call:

runModelingPipeline(
  parquet_duckdb_path = fixture,
  threads             = max(1L, parallel::detectCores() - 1L),
  n_fold              = 5,
  split               = c(1, 0),
  min_n               = 25,
  prop_vi_top_feats   = c(0, 1),
  pca_threshold       = 0.99,
  verbose             = TRUE,
  use_saved_split     = TRUE
)

Session info 

sessionInfo()
#> R version 4.6.0 (2026-04-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
#>  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
#>  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
#> [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
#> 
#> time zone: UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] amRml_0.99.0     BiocStyle_2.40.0
#> 
#> loaded via a namespace (and not attached):
#>   [1] DBI_1.3.0           rlang_1.2.0         magrittr_2.0.5     
#>   [4] tailor_0.1.0        furrr_0.4.0         sgof_2.3.5         
#>   [7] compiler_4.6.0      systemfonts_1.3.2   vctrs_0.7.3        
#>  [10] stringr_1.6.0       tune_2.1.0          crayon_1.5.3       
#>  [13] pkgconfig_2.0.3     shape_1.4.6.1       fastmap_1.2.0      
#>  [16] labeling_0.4.3      utf8_1.2.6          rmarkdown_2.31     
#>  [19] prodlim_2026.03.11  tzdb_0.5.0          ragg_1.5.2         
#>  [22] purrr_1.2.2         bit_4.6.0           xfun_0.57          
#>  [25] glmnet_5.0          cachem_1.1.0        jsonlite_2.0.0     
#>  [28] recipes_1.3.3       vip_0.4.6           parallel_4.6.0     
#>  [31] R6_2.6.1            bslib_0.11.0        stringi_1.8.7      
#>  [34] rsample_1.3.2       RColorBrewer_1.1-3  parallelly_1.47.0  
#>  [37] rpart_4.1.27        lubridate_1.9.5     jquerylib_0.1.4    
#>  [40] Rcpp_1.1.1-1.1      bookdown_0.46       assertthat_0.2.1   
#>  [43] dials_1.4.3         iterators_1.0.14    knitr_1.51         
#>  [46] future.apply_1.20.2 poibin_1.6          readr_2.2.0        
#>  [49] Matrix_1.7-5        splines_4.6.0       nnet_7.3-20        
#>  [52] timechange_0.4.0    tidyselect_1.2.1    yaml_2.3.12        
#>  [55] timeDate_4052.112   codetools_0.2-20    listenv_0.10.1     
#>  [58] lattice_0.22-9      tibble_3.3.1        withr_3.0.2        
#>  [61] S7_0.2.2            evaluate_1.0.5      future_1.70.0      
#>  [64] desc_1.4.3          survival_3.8-6      pillar_1.11.1      
#>  [67] BiocManager_1.30.27 foreach_1.5.2       generics_0.1.4     
#>  [70] vroom_1.7.1         hms_1.1.4           ggplot2_4.0.3      
#>  [73] scales_1.4.0        globals_0.19.1      class_7.3-23       
#>  [76] glue_1.8.1          tools_4.6.0         data.table_1.18.4  
#>  [79] gower_1.0.2         fs_2.1.0            grid_4.6.0         
#>  [82] yardstick_1.4.0     tidyr_1.3.2         workflowsets_1.1.1 
#>  [85] ipred_0.9-15        duckdb_1.5.2        cli_3.6.6          
#>  [88] DiceDesign_1.10     textshaping_1.0.5   workflows_1.3.0    
#>  [91] parsnip_1.6.0       lava_1.9.1          arrow_24.0.0       
#>  [94] dplyr_1.2.1         gtable_0.3.6        sass_0.4.10        
#>  [97] digest_0.6.39       ggrepel_0.9.8       farver_2.1.2       
#> [100] htmltools_0.5.9     pkgdown_2.2.0       lifecycle_1.0.5    
#> [103] hardhat_1.4.3       sparsevctrs_0.3.6   bit64_4.8.2        
#> [106] MASS_7.3-65